pandemic online classes essay

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Wiley - PMC COVID-19 Collection

Students’ experience of online learning during the COVID‐19 pandemic: A province‐wide survey study

Lixiang yan.

1 Centre for Learning Analytics at Monash, Faculty of Information Technology, Monash University, Clayton VIC, Australia

Alexander Whitelock‐Wainwright

2 Portfolio of the Deputy Vice‐Chancellor (Education), Monash University, Melbourne VIC, Australia

Quanlong Guan

3 Department of Computer Science, Jinan University, Guangzhou China

Gangxin Wen

4 College of Cyber Security, Jinan University, Guangzhou China

Dragan Gašević

Guanliang chen, associated data.

The data is not openly available as it is restricted by the Chinese government.

Online learning is currently adopted by educational institutions worldwide to provide students with ongoing education during the COVID‐19 pandemic. Even though online learning research has been advancing in uncovering student experiences in various settings (i.e., tertiary, adult, and professional education), very little progress has been achieved in understanding the experience of the K‐12 student population, especially when narrowed down to different school‐year segments (i.e., primary and secondary school students). This study explores how students at different stages of their K‐12 education reacted to the mandatory full‐time online learning during the COVID‐19 pandemic. For this purpose, we conducted a province‐wide survey study in which the online learning experience of 1,170,769 Chinese students was collected from the Guangdong Province of China. We performed cross‐tabulation and Chi‐square analysis to compare students’ online learning conditions, experiences, and expectations. Results from this survey study provide evidence that students’ online learning experiences are significantly different across school years. Foremost, policy implications were made to advise government authorises and schools on improving the delivery of online learning, and potential directions were identified for future research into K‐12 online learning.

Practitioner notes

What is already known about this topic

• Online learning has been widely adopted during the COVID‐19 pandemic to ensure the continuation of K‐12 education.
• Student success in K‐12 online education is substantially lower than in conventional schools.
• Students experienced various difficulties related to the delivery of online learning.

What this paper adds

• Provide empirical evidence for the online learning experience of students in different school years.
• Identify the different needs of students in primary, middle, and high school.
• Identify the challenges of delivering online learning to students of different age.

Implications for practice and/or policy

• Authority and schools need to provide sufficient technical support to students in online learning.
• The delivery of online learning needs to be customised for students in different school years.

INTRODUCTION

The ongoing COVID‐19 pandemic poses significant challenges to the global education system. By July 2020, the UN Educational, Scientific and Cultural Organization (2020) reported nationwide school closure in 111 countries, affecting over 1.07 billion students, which is around 61% of the global student population. Traditional brick‐and‐mortar schools are forced to transform into full‐time virtual schools to provide students with ongoing education (Van Lancker & Parolin,  2020 ). Consequently, students must adapt to the transition from face‐to‐face learning to fully remote online learning, where synchronous video conferences, social media, and asynchronous discussion forums become their primary venues for knowledge construction and peer communication.

For K‐12 students, this sudden transition is problematic as they often lack prior online learning experience (Barbour & Reeves,  2009 ). Barbour and LaBonte ( 2017 ) estimated that even in countries where online learning is growing rapidly, such as USA and Canada, less than 10% of the K‐12 student population had prior experience with this format. Maladaptation to online learning could expose inexperienced students to various vulnerabilities, including decrements in academic performance (Molnar et al.,  2019 ), feeling of isolation (Song et al.,  2004 ), and lack of learning motivation (Muilenburg & Berge,  2005 ). Unfortunately, with confirmed cases continuing to rise each day, and new outbreaks occur on a global scale, full‐time online learning for most students could last longer than anticipated (World Health Organization,  2020 ). Even after the pandemic, the current mass adoption of online learning could have lasting impacts on the global education system, and potentially accelerate and expand the rapid growth of virtual schools on a global scale (Molnar et al.,  2019 ). Thus, understanding students' learning conditions and their experiences of online learning during the COVID pandemic becomes imperative.

Emerging evidence on students’ online learning experience during the COVID‐19 pandemic has identified several major concerns, including issues with internet connection (Agung et al.,  2020 ; Basuony et al.,  2020 ), problems with IT equipment (Bączek et al.,  2021 ; Niemi & Kousa,  2020 ), limited collaborative learning opportunities (Bączek et al.,  2021 ; Yates et al.,  2020 ), reduced learning motivation (Basuony et al.,  2020 ; Niemi & Kousa,  2020 ; Yates et al.,  2020 ), and increased learning burdens (Niemi & Kousa,  2020 ). Although these findings provided valuable insights about the issues students experienced during online learning, information about their learning conditions and future expectations were less mentioned. Such information could assist educational authorises and institutions to better comprehend students’ difficulties and potentially improve their online learning experience. Additionally, most of these recent studies were limited to higher education, except for Yates et al. ( 2020 ) and Niemi and Kousa’s ( 2020 ) studies on senior high school students. Empirical research targeting the full spectrum of K‐12students remain scarce. Therefore, to address these gaps, the current paper reports the findings of a large‐scale study that sought to explore K‐12 students’ online learning experience during the COVID‐19 pandemic in a provincial sample of over one million Chinese students. The findings of this study provide policy recommendations to educational institutions and authorities regarding the delivery of K‐12 online education.

LITERATURE REVIEW

Learning conditions and technologies.

Having stable access to the internet is critical to students’ learning experience during online learning. Berge ( 2005 ) expressed the concern of the divide in digital‐readiness, and the pedagogical approach between different countries could influence students’ online learning experience. Digital‐readiness is the availability and adoption of information technologies and infrastructures in a country. Western countries like America (3rd) scored significantly higher in digital‐readiness compared to Asian countries like China (54th; Cisco,  2019 ). Students from low digital‐readiness countries could experience additional technology‐related problems. Supporting evidence is emerging in recent studies conducted during the COVID‐19 pandemic. In Egypt's capital city, Basuony et al. ( 2020 ) found that only around 13.9%of the students experienced issues with their internet connection. Whereas more than two‐thirds of the students in rural Indonesia reported issues of unstable internet, insufficient internet data, and incompatible learning device (Agung et al.,  2020 ).

Another influential factor for K‐12 students to adequately adapt to online learning is the accessibility of appropriate technological devices, especially having access to a desktop or a laptop (Barbour et al., 2018 ). However, it is unlikely for most of the students to satisfy this requirement. Even in higher education, around 76% of students reported having incompatible devices for online learning and only 15% of students used laptop for online learning, whereas around 85% of them used smartphone (Agung et al.,  2020 ). It is very likely that K‐12 students also suffer from this availability issue as they depend on their parents to provide access to relevant learning devices.

Technical issues surrounding technological devices could also influence students’ experience in online learning. (Barbour & Reeves,  2009 ) argues that students need to have a high level of digital literacy to find and use relevant information and communicate with others through technological devices. Students lacking this ability could experience difficulties in online learning. Bączek et al. ( 2021 ) found that around 54% of the medical students experienced technical problems with IT equipment and this issue was more prevalent in students with lower years of tertiary education. Likewise, Niemi and Kousa ( 2020 ) also find that students in a Finish high school experienced increased amounts of technical problems during the examination period, which involved additional technical applications. These findings are concerning as young children and adolescent in primary and lower secondary school could be more vulnerable to these technical problems as they are less experienced with the technologies in online learning (Barbour & LaBonte,  2017 ). Therefore, it is essential to investigate the learning conditions and the related difficulties experienced by students in K‐12 education as the extend of effects on them remain underexplored.

Learning experience and interactions

Apart from the aforementioned issues, the extent of interaction and collaborative learning opportunities available in online learning could also influence students’ experience. The literature on online learning has long emphasised the role of effective interaction for the success of student learning. According to Muirhead and Juwah ( 2004 ), interaction is an event that can take the shape of any type of communication between two or subjects and objects. Specifically, the literature acknowledges the three typical forms of interactions (Moore,  1989 ): (i) student‐content, (ii) student‐student, and (iii) student‐teacher. Anderson ( 2003 ) posits, in the well‐known interaction equivalency theorem, learning experiences will not deteriorate if only one of the three interaction is of high quality, and the other two can be reduced or even eliminated. Quality interaction can be accomplished by across two dimensions: (i) structure—pedagogical means that guide student interaction with contents or other students and (ii) dialogue—communication that happens between students and teachers and among students. To be able to scale online learning and prevent the growth of teaching costs, the emphasise is typically on structure (i.e., pedagogy) that can promote effective student‐content and student‐student interaction. The role of technology and media is typically recognised as a way to amplify the effect of pedagogy (Lou et al.,  2006 ). Novel technological innovations—for example learning analytics‐based personalised feedback at scale (Pardo et al.,  2019 ) —can also empower teachers to promote their interaction with students.

Online education can lead to a sense of isolation, which can be detrimental to student success (McInnerney & Roberts,  2004 ). Therefore, integration of social interaction into pedagogy for online learning is essential, especially at the times when students do not actually know each other or have communication and collaboration skills underdeveloped (Garrison et al.,  2010 ; Gašević et al.,  2015 ). Unfortunately, existing evidence suggested that online learning delivery during the COVID‐19 pandemic often lacks interactivity and collaborative experiences (Bączek et al.,  2021 ; Yates et al.,  2020 ). Bączek et al., ( 2021 ) found that around half of the medical students reported reduced interaction with teachers, and only 4% of students think online learning classes are interactive. Likewise, Yates et al. ( 2020 )’s study in high school students also revealed that over half of the students preferred in‐class collaboration over online collaboration as they value the immediate support and the proximity to teachers and peers from in‐class interaction.

Learning expectations and age differentiation

Although these studies have provided valuable insights and stressed the need for more interactivity in online learning, K‐12 students in different school years could exhibit different expectations for the desired activities in online learning. Piaget's Cognitive Developmental Theory illustrated children's difficulties in understanding abstract and hypothetical concepts (Thomas,  2000 ). Primary school students will encounter many abstract concepts in their STEM education (Uttal & Cohen,  2012 ). In face‐to‐face learning, teachers provide constant guidance on students’ learning progress and can help them to understand difficult concepts. Unfortunately, the level of guidance significantly drops in online learning, and, in most cases, children have to face learning obstacles by themselves (Barbour,  2013 ). Additionally, lower primary school students may lack the metacognitive skills to use various online learning functions, maintain engagement in synchronous online learning, develop and execute self‐regulated learning plans, and engage in meaningful peer interactions during online learning (Barbour,  2013 ; Broadbent & Poon,  2015 ; Huffaker & Calvert, 2003; Wang et al.,  2013 ). Thus, understanding these younger students’ expectations is imperative as delivering online learning to them in the same way as a virtual high school could hinder their learning experiences. For students with more matured metacognition, their expectations of online learning could be substantially different from younger students. Niemi et al.’s study ( 2020 ) with students in a Finish high school have found that students often reported heavy workload and fatigue during online learning. These issues could cause anxiety and reduce students’ learning motivation, which would have negative consequences on their emotional well‐being and academic performance (Niemi & Kousa,  2020 ; Yates et al.,  2020 ), especially for senior students who are under the pressure of examinations. Consequently, their expectations of online learning could be orientated toward having additional learning support functions and materials. Likewise, they could also prefer having more opportunities for peer interactions as these interactions are beneficial to their emotional well‐being and learning performance (Gašević et al., 2013 ; Montague & Rinaldi, 2001 ). Therefore, it is imperative to investigate the differences between online learning expectations in students of different school years to suit their needs better.

Research questions

By building upon the aforementioned relevant works, this study aimed to contribute to the online learning literature with a comprehensive understanding of the online learning experience that K‐12 students had during the COVID‐19 pandemic period in China. Additionally, this study also aimed to provide a thorough discussion of what potential actions can be undertaken to improve online learning delivery. Formally, this study was guided by three research questions (RQs):

RQ1 . What learning conditions were experienced by students across 12 years of education during their online learning process in the pandemic period? RQ2 . What benefits and obstacles were perceived by students across 12 years of education when performing online learning? RQ3 . What expectations do students, across 12 years of education, have for future online learning practices ?

Participants

The total number of K‐12 students in the Guangdong Province of China is around 15 million. In China, students of Year 1–6, Year 7–9, and Year 10–12 are referred to as students of primary school, middle school, and high school, respectively. Typically, students in China start their study in primary school at the age of around six. At the end of their high‐school study, students have to take the National College Entrance Examination (NCEE; also known as Gaokao) to apply for tertiary education. The survey was administrated across the whole Guangdong Province, that is the survey was exposed to all of the 15 million K‐12 students, though it was not mandatory for those students to accomplish the survey. A total of 1,170,769 students completed the survey, which accounts for a response rate of 7.80%. After removing responses with missing values and responses submitted from the same IP address (duplicates), we had 1,048,575 valid responses, which accounts to about 7% of the total K‐12 students in the Guangdong Province. The number of students in different school years is shown in Figure  1 . Overall, students were evenly distributed across different school years, except for a smaller sample in students of Year 10–12.

The number of students in each school year

Survey design

The survey was designed collaboratively by multiple relevant parties. Firstly, three educational researchers working in colleges and universities and three educational practitioners working in the Department of Education in Guangdong Province were recruited to co‐design the survey. Then, the initial draft of the survey was sent to 30 teachers from different primary and secondary schools, whose feedback and suggestions were considered to improve the survey. The final survey consisted of a total of 20 questions, which, broadly, can be classified into four categories: demographic, behaviours, experiences, and expectations. Details are available in Appendix.

All K‐12 students in the Guangdong Province were made to have full‐time online learning from March 1, 2020 after the outbreak of COVID‐19 in January in China. A province‐level online learning platform was provided to all schools by the government. In addition to the learning platform, these schools can also use additional third‐party platforms to facilitate the teaching activities, for example WeChat and Dingding, which provide services similar to WhatsApp and Zoom. The main change for most teachers was that they had to shift the classroom‐based lectures to online lectures with the aid of web‐conferencing tools. Similarly, these teachers also needed to perform homework marking and have consultation sessions in an online manner.

The Department of Education in the Guangdong Province of China distributed the survey to all K‐12 schools in the province on March 21, 2020 and collected responses on March 26, 2020. Students could access and answer the survey anonymously by either scan the Quick Response code along with the survey or click the survey address link on their mobile device. The survey was administrated in a completely voluntary manner and no incentives were given to the participants. Ethical approval was granted by the Department of Education in the Guangdong Province. Parental approval was not required since the survey was entirely anonymous and facilitated by the regulating authority, which satisfies China's ethical process.

The original survey was in Chinese, which was later translated by two bilingual researchers and verified by an external translator who is certified by the Australian National Accreditation Authority of Translators and Interpreters. The original and translated survey questionnaires are available in Supporting Information. Given the limited space we have here and the fact that not every survey item is relevant to the RQs, the following items were chosen to answer the RQs: item Q3 (learning media) and Q11 (learning approaches) for RQ1, item Q13 (perceived obstacle) and Q19 (perceived benefits) for RQ2, and item Q19 (expected learning activities) for RQ3. Cross‐tabulation based approaches were used to analyse the collected data. To scrutinise whether the differences displayed by students of different school years were statistically significant, we performed Chi‐square tests and calculated the Cramer's V to assess the strengths of the association after chi‐square had determined significance.

For the analyses, students were segmented into four categories based on their school years, that is Year 1–3, Year 4–6, Year 7–9, and Year 10–12, to provide a clear understanding of the different experiences and needs that different students had for online learning. This segmentation was based on the educational structure of Chinese schools: elementary school (Year 1–6), middle school (Year 7–9), and high school (Year 10–12). Children in elementary school can further be segmented into junior (Year 1–3) or senior (Year 4–6) students because senior elementary students in China are facing more workloads compared to junior students due to the provincial Middle School Entry Examination at the end of Year 6.

Learning conditions—RQ1

Learning media.

The Chi‐square test showed significant association between school years and students’ reported usage of learning media, χ 2 (55, N  = 1,853,952) = 46,675.38, p  < 0.001. The Cramer's V is 0.07 ( df ∗ = 5), which indicates a small‐to‐medium effect according to Cohen’s ( 1988 ) guidelines. Based on Figure  2 , we observed that an average of up to 87.39% students used smartphones to perform online learning, while only 25.43% students used computer, which suggests that smartphones, with widespread availability in China (2020), have been adopted by students for online learning. As for the prevalence of the two media, we noticed that both smartphones ( χ 2 (3, N  = 1,048,575) = 9,395.05, p < 0.001, Cramer's V  = 0.10 ( df ∗ = 1)) and computers ( χ 2 (3, N  = 1,048,575) = 11,025.58, p <.001, Cramer's V  = 0.10 ( df ∗ = 1)) were more adopted by high‐school‐year (Year 7–12) than early‐school‐year students (Year 1–6), both with a small effect size. Besides, apparent discrepancies can be observed between the usages of TV and paper‐based materials across different school years, that is early‐school‐year students reported more TV usage ( χ 2 (3, N  = 1,048,575) = 19,505.08, p <.001), with a small‐to‐medium effect size, Cramer's V  = 0.14( df ∗ = 1). High‐school‐year students (especially Year 10–12) reported more usage of paper‐based materials ( χ 2 (3, N  = 1,048,575) = 23,401.64, p < 0.001), with a small‐to‐medium effect size, Cramer's V  = 0.15( df ∗ = 1).

Learning media used by students in online learning

Learning approaches

School years is also significantly associated with the different learning approaches students used to tackle difficult concepts during online learning, χ 2 (55, N  = 2,383,751) = 58,030.74, p < 0.001. The strength of this association is weak to moderate as shown by the Cramer's V (0.07, df ∗ = 5; Cohen,  1988 ). When encountering problems related to difficult concepts, students typically chose to “solve independently by searching online” or “rewatch recorded lectures” instead of consulting to their teachers or peers (Figure  3 ). This is probably because, compared to classroom‐based education, it is relatively less convenient and more challenging for students to seek help from others when performing online learning. Besides, compared to high‐school‐year students, early‐school‐year students (Year 1–6), reported much less use of “solve independently by searching online” ( χ 2 (3, N  = 1,048,575) = 48,100.15, p <.001), with a small‐to‐medium effect size, Cramer's V  = 0.21 ( df ∗ = 1). Also, among those approaches of seeking help from others, significantly more high‐school‐year students preferred “communicating with other students” than early‐school‐year students ( χ 2 (3, N  = 1,048,575) = 81,723.37, p < 0.001), with a medium effect size, Cramer's V  = 0.28 ( df ∗ = 1).

Learning approaches used by students in online learning

Perceived benefits and obstacles—RQ2

Perceived benefits.

The association between school years and perceived benefits in online learning is statistically significant, χ 2 (66, N  = 2,716,127) = 29,534.23, p  < 0.001, and the Cramer's V (0.04, df ∗ = 6) indicates a small effect (Cohen,  1988 ). Unsurprisingly, benefits brought by the convenience of online learning are widely recognised by students across all school years (Figure  4 ), that is up to 75% of students reported that it is “more convenient to review course content” and 54% said that they “can learn anytime and anywhere” . Besides, we noticed that about 50% of early‐school‐year students appreciated the “access to courses delivered by famous teachers” and 40%–47% of high‐school‐year students indicated that online learning is “helpful to develop self‐regulation and autonomy” .

Perceived benefits of online learning reported by students

Perceived obstacles

The Chi‐square test shows a significant association between school years and students’ perceived obstacles in online learning, χ 2 (77, N  = 2,699,003) = 31,987.56, p < 0.001. This association is relatively weak as shown by the Cramer's V (0.04, df ∗ = 7; Cohen,  1988 ). As shown in Figure  5 , the biggest obstacles encountered by up to 73% of students were the “eyestrain caused by long staring at screens” . Disengagement caused by nearby disturbance was reported by around 40% of students, especially those of Year 1–3 and 10–12. Technological‐wise, about 50% of students experienced poor Internet connection during their learning process, and around 20% of students reported the “confusion in setting up the platforms” across of school years.

Perceived obstacles of online learning reported by students

Expectations for future practices of online learning – RQ3

Online learning activities.

The association between school years and students’ expected online learning activities is significant, χ 2 (66, N  = 2,416,093) = 38,784.81, p < 0.001. The Cramer's V is 0.05 ( df ∗ = 6) which suggests a small effect (Cohen,  1988 ). As shown in Figure  6 , the most expected activity for future online learning is “real‐time interaction with teachers” (55%), followed by “online group discussion and collaboration” (38%). We also observed that more early‐school‐year students expect reflective activities, such as “regular online practice examinations” ( χ 2 (3, N  = 1,048,575) = 11,644.98, p < 0.001), with a small effect size, Cramer's V  = 0.11 ( df ∗ = 1). In contrast, more high‐school‐year students expect “intelligent recommendation system …” ( χ 2 (3, N  = 1,048,575) = 15,327.00, p < 0.001), with a small effect size, Cramer's V  = 0.12 ( df ∗ = 1).

Students’ expected online learning activities

Regarding students’ learning conditions, substantial differences were observed in learning media, family dependency, and learning approaches adopted in online learning between students in different school years. The finding of more computer and smartphone usage in high‐school‐year than early‐school‐year students can probably be explained by that, with the growing abilities in utilising these media as well as the educational systems and tools which run on these media, high‐school‐year students tend to make better use of these media for online learning practices. Whereas, the differences in paper‐based materials may imply that high‐school‐year students in China have to accomplish a substantial amount of exercise, assignments, and exam papers to prepare for the National College Entrance Examination (NCEE), whose delivery was not entirely digitised due to the sudden transition to online learning. Meanwhile, high‐school‐year students may also have preferred using paper‐based materials for exam practice, as eventually, they would take their NCEE in the paper format. Therefore, these substantial differences in students’ usage of learning media should be addressed by customising the delivery method of online learning for different school years.

Other than these between‐age differences in learning media, the prevalence of smartphone in online learning resonates with Agung et al.’s ( 2020 ) finding on the issues surrounding the availability of compatible learning device. The prevalence of smartphone in K‐12 students is potentially problematic as the majority of the online learning platform and content is designed for computer‐based learning (Berge,  2005 ; Molnar et al.,  2019 ). Whereas learning with smartphones has its own unique challenges. For example, Gikas and Grant ( 2013 ) discovered that students who learn with smartphone experienced frustration with the small screen‐size, especially when trying to type with the tiny keypad. Another challenge relates to the distraction of various social media applications. Although similar distractions exist in computer and web‐based social media, the level of popularity, especially in the young generation, are much higher in mobile‐based social media (Montag et al.,  2018 ). In particular, the message notification function in smartphones could disengage students from learning activities and allure them to social media applications (Gikas & Grant,  2013 ). Given these challenges of learning with smartphones, more research efforts should be devoted to analysing students’ online learning behaviour in the setting of mobile learning to accommodate their needs better.

The differences in learning approaches, once again, illustrated that early‐school‐year students have different needs compared to high‐school‐year students. In particular, the low usage of the independent learning methods in early‐school‐year students may reflect their inability to engage in independent learning. Besides, the differences in help seeking behaviours demonstrated the distinctive needs for communication and interaction between different students, that is early‐school‐year students have a strong reliance on teachers and high‐school‐year students, who are equipped with stronger communication ability, are more inclined to interact with their peers. This finding implies that the design of online learning platforms should take students’ different needs into account. Thus, customisation is urgently needed for the delivery of online learning to different school years.

In terms of the perceived benefits and challenges of online learning, our results resonate with several previous findings. In particular, the benefits of convenience are in line with the flexibility advantages of online learning, which were mentioned in prior works (Appana,  2008 ; Bączek et al.,  2021 ; Barbour,  2013 ; Basuony et al.,  2020 ; Harvey et al.,  2014 ). Early‐school‐year students’ higher appreciation in having “access to courses delivered by famous teachers” and lower appreciation in the independent learning skills developed through online learning are also in line with previous literature (Barbour,  2013 ; Harvey et al.,  2014 ; Oliver et al.,  2009 ). Again, these similar findings may indicate the strong reliance that early‐school‐year students place on teachers, while high‐school‐year students are more capable of adapting to online learning by developing independent learning skills.

Technology‐wise, students’ experience of poor internet connection and confusion in setting up online learning platforms are particularly concerning. The problem of poor internet connection corroborated the findings reported in prior studies (Agung et al.,  2020 ; Barbour,  2013 ; Basuony et al.,  2020 ; Berge,  2005 ; Rice,  2006 ), that is the access issue surrounded the digital divide as one of the main challenges of online learning. In the era of 4G and 5G networks, educational authorities and institutions that deliver online education could fall into the misconception of most students have a stable internet connection at home. The internet issue we observed is particularly vital to students’ online learning experience as most students prefer real‐time communications (Figure  6 ), which rely heavily on stable internet connection. Likewise, the finding of students’ confusion in technology is also consistent with prior studies (Bączek et al.,  2021 ; Muilenburg & Berge,  2005 ; Niemi & Kousa,  2020 ; Song et al.,  2004 ). Students who were unsuccessfully in setting up the online learning platforms could potentially experience declines in confidence and enthusiasm for online learning, which would cause a subsequent unpleasant learning experience. Therefore, both the readiness of internet infrastructure and student technical skills remain as the significant challenges for the mass‐adoption of online learning.

On the other hand, students’ experience of eyestrain from extended screen time provided empirical evidence to support Spitzer’s ( 2001 ) speculation about the potential ergonomic impact of online learning. This negative effect is potentially related to the prevalence of smartphone device and the limited screen size of these devices. This finding not only demonstrates the potential ergonomic issues that would be caused by smartphone‐based online learning but also resonates with the aforementioned necessity of different platforms and content designs for different students.

A less‐mentioned problem in previous studies on online learning experiences is the disengagement caused by nearby disturbance, especially in Year 1–3 and 10–12. It is likely that early‐school‐year students suffered from this problem because of their underdeveloped metacognitive skills to concentrate on online learning without teachers’ guidance. As for high‐school‐year students, the reasons behind their disengagement require further investigation in the future. Especially it would be worthwhile to scrutinise whether this type of disengagement is caused by the substantial amount of coursework they have to undertake and the subsequent a higher level of pressure and a lower level of concentration while learning.

Across age‐level differences are also apparent in terms of students’ expectations of online learning. Although, our results demonstrated students’ needs of gaining social interaction with others during online learning, findings (Bączek et al.,  2021 ; Harvey et al.,  2014 ; Kuo et al.,  2014 ; Liu & Cavanaugh,  2012 ; Yates et al.,  2020 ). This need manifested differently across school years, with early‐school‐year students preferring more teacher interactions and learning regulation support. Once again, this finding may imply that early‐school‐year students are inadequate in engaging with online learning without proper guidance from their teachers. Whereas, high‐school‐year students prefer more peer interactions and recommendation to learning resources. This expectation can probably be explained by the large amount of coursework exposed to them. Thus, high‐school‐year students need further guidance to help them better direct their learning efforts. These differences in students’ expectations for future practices could guide the customisation of online learning delivery.

Implications

As shown in our results, improving the delivery of online learning not only requires the efforts of policymakers but also depend on the actions of teachers and parents. The following sub‐sections will provide recommendations for relevant stakeholders and discuss their essential roles in supporting online education.

Technical support

The majority of the students has experienced technical problems during online learning, including the internet lagging and confusion in setting up the learning platforms. These problems with technology could impair students’ learning experience (Kauffman,  2015 ; Muilenburg & Berge,  2005 ). Educational authorities and schools should always provide a thorough guide and assistance for students who are experiencing technical problems with online learning platforms or other related tools. Early screening and detection could also assist schools and teachers to direct their efforts more effectively in helping students with low technology skills (Wilkinson et al.,  2010 ). A potential identification method involves distributing age‐specific surveys that assess students’ Information and Communication Technology (ICT) skills at the beginning of online learning. For example, there are empirical validated ICT surveys available for both primary (Aesaert et al.,  2014 ) and high school (Claro et al.,  2012 ) students.

For students who had problems with internet lagging, the delivery of online learning should provide options that require fewer data and bandwidth. Lecture recording is the existing option but fails to address students’ need for real‐time interaction (Clark et al.,  2015 ; Malik & Fatima,  2017 ). A potential alternative involves providing students with the option to learn with digital or physical textbooks and audio‐conferencing, instead of screen sharing and video‐conferencing. This approach significantly reduces the amount of data usage and lowers the requirement of bandwidth for students to engage in smooth online interactions (Cisco,  2018 ). It also requires little additional efforts from teachers as official textbooks are often available for each school year, and thus, they only need to guide students through the materials during audio‐conferencing. Educational authority can further support this approach by making digital textbooks available for teachers and students, especially those in financial hardship. However, the lack of visual and instructor presence could potentially reduce students’ attention, recall of information, and satisfaction in online learning (Wang & Antonenko,  2017 ). Therefore, further research is required to understand whether the combination of digital or physical textbooks and audio‐conferencing is appropriate for students with internet problems. Alternatively, suppose the local technological infrastructure is well developed. In that case, governments and schools can also collaborate with internet providers to issue data and bandwidth vouchers for students who are experiencing internet problems due to financial hardship.

For future adoption of online learning, policymakers should consider the readiness of the local internet infrastructure. This recommendation is particularly important for developing countries, like Bangladesh, where the majority of the students reported the lack of internet infrastructure (Ramij & Sultana,  2020 ). In such environments, online education may become infeasible, and alternative delivery method could be more appropriate, for example, the Telesecundaria program provides TV education for rural areas of Mexico (Calderoni,  1998 ).

Other than technical problems, choosing a suitable online learning platform is also vital for providing students with a better learning experience. Governments and schools should choose an online learning platform that is customised for smartphone‐based learning, as the majority of students could be using smartphones for online learning. This recommendation is highly relevant for situations where students are forced or involuntarily engaged in online learning, like during the COVID‐19 pandemic, as they might not have access to a personal computer (Molnar et al.,  2019 ).

Customisation of delivery methods

Customising the delivery of online learning for students in different school years is the theme that appeared consistently across our findings. This customisation process is vital for making online learning an opportunity for students to develop independent learning skills, which could help prepare them for tertiary education and lifelong learning. However, the pedagogical design of K‐12 online learning programs should be differentiated from adult‐orientated programs as these programs are designed for independent learners, which is rarely the case for students in K‐12 education (Barbour & Reeves,  2009 ).

For early‐school‐year students, especially Year 1–3 students, providing them with sufficient guidance from both teachers and parents should be the priority as these students often lack the ability to monitor and reflect on learning progress. In particular, these students would prefer more real‐time interaction with teachers, tutoring from parents, and regular online practice examinations. These forms of guidance could help early‐school‐year students to cope with involuntary online learning, and potentially enhance their experience in future online learning. It should be noted that, early‐school‐year students demonstrated interest in intelligent monitoring and feedback systems for learning. Additional research is required to understand whether these young children are capable of understanding and using learning analytics that relay information on their learning progress. Similarly, future research should also investigate whether young children can communicate effectively through digital tools as potential inability could hinder student learning in online group activities. Therefore, the design of online learning for early‐school‐year students should focus less on independent learning but ensuring that students are learning effective under the guidance of teachers and parents.

In contrast, group learning and peer interaction are essential for older children and adolescents. The delivery of online learning for these students should focus on providing them with more opportunities to communicate with each other and engage in collaborative learning. Potential methods to achieve this goal involve assigning or encouraging students to form study groups (Lee et al.,  2011 ), directing students to use social media for peer communication (Dabbagh & Kitsantas,  2012 ), and providing students with online group assignments (Bickle & Rucker,  2018 ).

Special attention should be paid to students enrolled in high schools. For high‐school‐year students, in particular, students in Year 10–12, we also recommend to provide them with sufficient access to paper‐based learning materials, such as revision booklet and practice exam papers, so they remain familiar with paper‐based examinations. This recommendation applies to any students who engage in online learning but has to take their final examination in paper format. It is also imperative to assist high‐school‐year students who are facing examinations to direct their learning efforts better. Teachers can fulfil this need by sharing useful learning resources on the learning management system, if it is available, or through social media groups. Alternatively, students are interested in intelligent recommendation systems for learning resources, which are emerging in the literature (Corbi & Solans,  2014 ; Shishehchi et al.,  2010 ). These systems could provide personalised recommendations based on a series of evaluation on learners’ knowledge. Although it is infeasible for situations where the transformation to online learning happened rapidly (i.e., during the COVID‐19 pandemic), policymakers can consider embedding such systems in future online education.

Limitations

The current findings are limited to primary and secondary Chinese students who were involuntarily engaged in online learning during the COVID‐19 pandemic. Despite the large sample size, the population may not be representative as participants are all from a single province. Also, information about the quality of online learning platforms, teaching contents, and pedagogy approaches were missing because of the large scale of our study. It is likely that the infrastructures of online learning in China, such as learning platforms, instructional designs, and teachers’ knowledge about online pedagogy, were underprepared for the sudden transition. Thus, our findings may not represent the experience of students who voluntarily participated in well‐prepared online learning programs, in particular, the virtual school programs in America and Canada (Barbour & LaBonte,  2017 ; Molnar et al.,  2019 ). Lastly, the survey was only evaluated and validated by teachers but not students. Therefore, students with the lowest reading comprehension levels might have a different understanding of the items’ meaning, especially terminologies that involve abstract contracts like self‐regulation and autonomy in item Q17.

In conclusion, we identified across‐year differences between primary and secondary school students’ online learning experience during the COVID‐19 pandemic. Several recommendations were made for the future practice and research of online learning in the K‐12 student population. First, educational authorities and schools should provide sufficient technical support to help students to overcome potential internet and technical problems, as well as choosing online learning platforms that have been customised for smartphones. Second, customising the online pedagogy design for students in different school years, in particular, focusing on providing sufficient guidance for young children, more online collaborative opportunity for older children and adolescent, and additional learning resource for senior students who are facing final examinations.

CONFLICT OF INTEREST

There is no potential conflict of interest in this study.

ETHICS STATEMENT

The data are collected by the Department of Education of the Guangdong Province who also has the authority to approve research studies in K12 education in the province.

Supporting information

Supplementary Material

ACKNOWLEDGEMENTS

This work is supported by the National Natural Science Foundation of China (62077028, 61877029), the Science and Technology Planning Project of Guangdong (2020B0909030005, 2020B1212030003, 2020ZDZX3013, 2019B1515120010, 2018KTSCX016, 2019A050510024), the Science and Technology Planning Project of Guangzhou (201902010041), and the Fundamental Research Funds for the Central Universities (21617408, 21619404).

SURVEY ITEMS

Yan, L , Whitelock‐Wainwright, A , Guan, Q , Wen, G , Gašević, D , & Chen, G . Students’ experience of online learning during the COVID‐19 pandemic: A province‐wide survey study . Br J Educ Technol . 2021; 52 :2038–2057. 10.1111/bjet.13102 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

DATA AVAILABILITY STATEMENT

• Aesaert, K. , Van Nijlen, D. , Vanderlinde, R. , & van Braak, J. (2014). Direct measures of digital information processing and communication skills in primary education: Using item response theory for the development and validation of an ICT competence scale . Computers & Education , 76 , 168–181. 10.1016/j.compedu.2014.03.013 [ CrossRef ] [ Google Scholar ]
• Agung, A. S. N. , Surtikanti, M. W. , & Quinones, C. A. (2020). Students’ perception of online learning during COVID‐19 pandemic: A case study on the English students of STKIP Pamane Talino . SOSHUM: Jurnal Sosial Dan Humaniora , 10 ( 2 ), 225–235. 10.31940/soshum.v10i2.1316 [ CrossRef ] [ Google Scholar ]
• Anderson, T. (2003). Getting the mix right again: An updated and theoretical rationale for interaction . The International Review of Research in Open and Distributed Learning , 4 ( 2 ). 10.19173/irrodl.v4i2.149 [ CrossRef ] [ Google Scholar ]
• Appana, S. (2008). A review of benefits and limitations of online learning in the context of the student, the instructor and the tenured faculty . International Journal on E‐learning , 7 ( 1 ), 5–22. [ Google Scholar ]
• Bączek, M. , Zagańczyk‐Bączek, M. , Szpringer, M. , Jaroszyński, A. , & Wożakowska‐Kapłon, B. (2021). Students’ perception of online learning during the COVID‐19 pandemic: A survey study of Polish medical students . Medicine , 100 ( 7 ), e24821. 10.1097/MD.0000000000024821 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Barbour, M. K. (2013). The landscape of k‐12 online learning: Examining what is known . Handbook of Distance Education , 3 , 574–593. [ Google Scholar ]
• Barbour, M. , Huerta, L. , & Miron, G. (2018). Virtual schools in the US: Case studies of policy, performance and research evidence. In Society for information technology & teacher education international conference (pp. 672–677). Association for the Advancement of Computing in Education (AACE). [ Google Scholar ]
• Barbour, M. K. , & LaBonte, R. (2017). State of the nation: K‐12 e‐learning in Canada, 2017 edition . http://k12sotn.ca/wp‐content/uploads/2018/02/StateNation17.pdf [ Google Scholar ]
• Barbour, M. K. , & Reeves, T. C. (2009). The reality of virtual schools: A review of the literature . Computers & Education , 52 ( 2 ), 402–416. [ Google Scholar ]
• Basuony, M. A. K. , EmadEldeen, R. , Farghaly, M. , El‐Bassiouny, N. , & Mohamed, E. K. A. (2020). The factors affecting student satisfaction with online education during the COVID‐19 pandemic: An empirical study of an emerging Muslim country . Journal of Islamic Marketing . 10.1108/JIMA-09-2020-0301 [ CrossRef ] [ Google Scholar ]
• Berge, Z. L. (2005). Virtual schools: Planning for success . Teachers College Press, Columbia University. [ Google Scholar ]
• Bickle, M. C. , & Rucker, R. (2018). Student‐to‐student interaction: Humanizing the online classroom using technology and group assignments . Quarterly Review of Distance Education , 19 ( 1 ), 1–56. [ Google Scholar ]
• Broadbent, J. , & Poon, W. L. (2015). Self‐regulated learning strategies & academic achievement in online higher education learning environments: A systematic review . The Internet and Higher Education , 27 , 1–13. [ Google Scholar ]
• Calderoni, J. (1998). Telesecundaria: Using TV to bring education to rural Mexico (Tech. Rep.). The World Bank. [ Google Scholar ]
• Cisco . (2018). Bandwidth requirements for meetings with cisco Webex and collaboration meeting rooms white paper . http://dwz.date/dpbc [ Google Scholar ]
• Cisco . (2019). Cisco digital readiness 2019 . https://www.cisco.com/c/m/en_us/about/corporate‐social‐responsibility/research‐resources/digital‐readiness‐index.html#/ (Library Catalog: www.cisco.com). [ Google Scholar ]
• Clark, C. , Strudler, N. , & Grove, K. (2015). Comparing asynchronous and synchronous video vs. text based discussions in an online teacher education course . Online Learning , 19 ( 3 ), 48–69. [ Google Scholar ]
• Claro, M. , Preiss, D. D. , San Martín, E. , Jara, I. , Hinostroza, J. E. , Valenzuela, S. , Cortes, F. , & Nussbaum, M. (2012). Assessment of 21st century ICT skills in Chile: Test design and results from high school level students . Computers & Education , 59 ( 3 ), 1042–1053. 10.1016/j.compedu.2012.04.004 [ CrossRef ] [ Google Scholar ]
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences . Routledge Academic. [ Google Scholar ]
• Corbi, A. , & Solans, D. B. (2014). Review of current student‐monitoring techniques used in elearning‐focused recommender systems and learning analytics: The experience API & LIME model case study . IJIMAI , 2 ( 7 ), 44–52. [ Google Scholar ]
• Dabbagh, N. , & Kitsantas, A. (2012). Personal learning environments, social media, and self‐regulated learning: A natural formula for connecting formal and informal learning . The Internet and Higher Education , 15 ( 1 ), 3–8. 10.1016/j.iheduc.2011.06.002 [ CrossRef ] [ Google Scholar ]
• Garrison, D. R. , Cleveland‐Innes, M. , & Fung, T. S. (2010). Exploring causal relationships among teaching, cognitive and social presence: Student perceptions of the community of inquiry framework . The Internet and Higher Education , 13 ( 1–2 ), 31–36. 10.1016/j.iheduc.2009.10.002 [ CrossRef ] [ Google Scholar ]
• Gašević, D. , Adesope, O. , Joksimović, S. , & Kovanović, V. (2015). Externally‐facilitated regulation scaffolding and role assignment to develop cognitive presence in asynchronous online discussions . The Internet and Higher Education , 24 , 53–65. 10.1016/j.iheduc.2014.09.006 [ CrossRef ] [ Google Scholar ]
• Gašević, D. , Zouaq, A. , & Janzen, R. (2013). “Choose your classmates, your GPA is at stake!” The association of cross‐class social ties and academic performance . American Behavioral Scientist , 57 ( 10 ), 1460–1479. [ Google Scholar ]
• Gikas, J. , & Grant, M. M. (2013). Mobile computing devices in higher education: Student perspectives on learning with cellphones, smartphones & social media . The Internet and Higher Education , 19 , 18–26. [ Google Scholar ]
• Harvey, D. , Greer, D. , Basham, J. , & Hu, B. (2014). From the student perspective: Experiences of middle and high school students in online learning . American Journal of Distance Education , 28 ( 1 ), 14–26. 10.1080/08923647.2014.868739 [ CrossRef ] [ Google Scholar ]
• Kauffman, H. (2015). A review of predictive factors of student success in and satisfaction with online learning . Research in Learning Technology , 23 . 10.3402/rlt.v23.26507 [ CrossRef ] [ Google Scholar ]
• Kuo, Y.‐C. , Walker, A. E. , Belland, B. R. , Schroder, K. E. , & Kuo, Y.‐T. (2014). A case study of integrating interwise: Interaction, internet self‐efficacy, and satisfaction in synchronous online learning environments . International Review of Research in Open and Distributed Learning , 15 ( 1 ), 161–181. 10.19173/irrodl.v15i1.1664 [ CrossRef ] [ Google Scholar ]
• Lee, S. J. , Srinivasan, S. , Trail, T. , Lewis, D. , & Lopez, S. (2011). Examining the relationship among student perception of support, course satisfaction, and learning outcomes in online learning . The Internet and Higher Education , 14 ( 3 ), 158–163. 10.1016/j.iheduc.2011.04.001 [ CrossRef ] [ Google Scholar ]
• Liu, F. , & Cavanaugh, C. (2012). Factors influencing student academic performance in online high school algebra . Open Learning: The Journal of Open, Distance and e‐Learning , 27 ( 2 ), 149–167. 10.1080/02680513.2012.678613 [ CrossRef ] [ Google Scholar ]
• Lou, Y. , Bernard, R. M. , & Abrami, P. C. (2006). Media and pedagogy in undergraduate distance education: A theory‐based meta‐analysis of empirical literature . Educational Technology Research and Development , 54 ( 2 ), 141–176. 10.1007/s11423-006-8252-x [ CrossRef ] [ Google Scholar ]
• Malik, M. , & Fatima, G. (2017). E‐learning: Students’ perspectives about asynchronous and synchronous resources at higher education level . Bulletin of Education and Research , 39 ( 2 ), 183–195. [ Google Scholar ]
• McInnerney, J. M. , & Roberts, T. S. (2004). Online learning: Social interaction and the creation of a sense of community . Journal of Educational Technology & Society , 7 ( 3 ), 73–81. [ Google Scholar ]
• Molnar, A. , Miron, G. , Elgeberi, N. , Barbour, M. K. , Huerta, L. , Shafer, S. R. , & Rice, J. K. (2019). Virtual schools in the US 2019 . National Education Policy Center. [ Google Scholar ]
• Montague, M. , & Rinaldi, C. (2001). Classroom dynamics and children at risk: A followup . Learning Disability Quarterly , 24 ( 2 ), 75–83. [ Google Scholar ]
• Montag, C. , Becker, B. , & Gan, C. (2018). The multipurpose application Wechat: A review on recent research . Frontiers in Psychology , 9 , 2247. 10.3389/fpsyg.2018.02247 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Moore, M. G. (1989). Editorial: Three types of interaction . American Journal of Distance Education , 3 ( 2 ), 1–7. 10.1080/08923648909526659 [ CrossRef ] [ Google Scholar ]
• Muilenburg, L. Y. , & Berge, Z. L. (2005). Student barriers to online learning: A factor analytic study . Distance Education , 26 ( 1 ), 29–48. 10.1080/01587910500081269 [ CrossRef ] [ Google Scholar ]
• Muirhead, B. , & Juwah, C. (2004). Interactivity in computer‐mediated college and university education: A recent review of the literature . Journal of Educational Technology & Society , 7 ( 1 ), 12–20. [ Google Scholar ]
• Niemi, H. M. , & Kousa, P. (2020). A case study of students’ and teachers’ perceptions in a finnish high school during the COVID pandemic . International Journal of Technology in Education and Science , 4 ( 4 ), 352–369. 10.46328/ijtes.v4i4.167 [ CrossRef ] [ Google Scholar ]
• Oliver, K. , Osborne, J. , & Brady, K. (2009). What are secondary students’ expectations for teachers in virtual school environments? Distance Education , 30 ( 1 ), 23–45. 10.1080/01587910902845923 [ CrossRef ] [ Google Scholar ]
• Pardo, A. , Jovanovic, J. , Dawson, S. , Gašević, D. , & Mirriahi, N. (2019). Using learning analytics to scale the provision of personalised feedback . British Journal of Educational Technology , 50 ( 1 ), 128–138. 10.1111/bjet.12592 [ CrossRef ] [ Google Scholar ]
• Ramij, M. , & Sultana, A. (2020). Preparedness of online classes in developing countries amid covid‐19 outbreak: A perspective from Bangladesh. Afrin, Preparedness of Online Classes in Developing Countries amid COVID‐19 Outbreak: A Perspective from Bangladesh (June 29, 2020) .
• Rice, K. L. (2006). A comprehensive look at distance education in the k–12 context . Journal of Research on Technology in Education , 38 ( 4 ), 425–448. 10.1080/15391523.2006.10782468 [ CrossRef ] [ Google Scholar ]
• Shishehchi, S. , Banihashem, S. Y. , & Zin, N. A. M. (2010). A proposed semantic recommendation system for elearning: A rule and ontology based e‐learning recommendation system. In 2010 international symposium on information technology (Vol. 1, pp. 1–5).
• Song, L. , Singleton, E. S. , Hill, J. R. , & Koh, M. H. (2004). Improving online learning: Student perceptions of useful and challenging characteristics . The Internet and Higher Education , 7 ( 1 ), 59–70. 10.1016/j.iheduc.2003.11.003 [ CrossRef ] [ Google Scholar ]
• Spitzer, D. R. (2001). Don’t forget the high‐touch with the high‐tech in distance learning . Educational Technology , 41 ( 2 ), 51–55. [ Google Scholar ]
• Thomas, R. M. (2000). Comparing theories of child development. Wadsworth/Thomson Learning. United Nations Educational, Scientific and Cultural Organization. (2020, March). Education: From disruption to recovery . https://en.unesco.org/covid19/educationresponse (Library Catalog: en.unesco.org)
• Uttal, D. H. , & Cohen, C. A. (2012). Spatial thinking and stem education: When, why, and how? In Psychology of learning and motivation (Vol. 57 , pp. 147–181). Elsevier. [ Google Scholar ]
• Van Lancker, W. , & Parolin, Z. (2020). Covid‐19, school closures, and child poverty: A social crisis in the making . The Lancet Public Health , 5 ( 5 ), e243–e244. 10.1016/S2468-2667(20)30084-0 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wang, C.‐H. , Shannon, D. M. , & Ross, M. E. (2013). Students’ characteristics, self‐regulated learning, technology self‐efficacy, and course outcomes in online learning . Distance Education , 34 ( 3 ), 302–323. 10.1080/01587919.2013.835779 [ CrossRef ] [ Google Scholar ]
• Wang, J. , & Antonenko, P. D. (2017). Instructor presence in instructional video: Effects on visual attention, recall, and perceived learning . Computers in Human Behavior , 71 , 79–89. 10.1016/j.chb.2017.01.049 [ CrossRef ] [ Google Scholar ]
• Wilkinson, A. , Roberts, J. , & While, A. E. (2010). Construction of an instrument to measure student information and communication technology skills, experience and attitudes to e‐learning . Computers in Human Behavior , 26 ( 6 ), 1369–1376. 10.1016/j.chb.2010.04.010 [ CrossRef ] [ Google Scholar ]
• World Health Organization . (2020, July). Coronavirus disease 2019 (COVID‐19): Situation Report‐164 (Situation Report No. 164). https://www.who.int/docs/default‐source/coronaviruse/situation‐reports/20200702‐covid‐19‐sitrep‐164.pdf?sfvrsn$=$ac074f58$_$2
• Yates, A. , Starkey, L. , Egerton, B. , & Flueggen, F. (2020). High school students’ experience of online learning during Covid‐19: The influence of technology and pedagogy . Technology, Pedagogy and Education , 9 , 1–15. 10.1080/1475939X.2020.1854337 [ CrossRef ] [ Google Scholar ]

Online Learning During the Pandemic

Today’s rapid shift in the traditional patterns of social lifestyle caused by the COVID-19 pandemic outbreak has resulted in the necessity to define possible approaches to living a full-scale life while respecting the need for social distancing. Thus, one of the major challenges in the context was to define the patterns of work and education process during the global lockdown. When it comes to the notion of education, the process of online learning has become a salvation to the problem of education access and efficiency. The definition of online learning stands for an umbrella term that encompasses a series of machine-learning techniques that allow learners to acquire relevant knowledge with the help of technology in a certain sequence [1]. Although the process of online learning has become widely popular due to an ongoing emergency, the term genesis can be traced back to decades prior to COVID-19, as machine learning is also regarded as a scientific outbreak besides being an urgent problem solution [2]. Thus, once the necessity of technological intervention in education became an absolute necessity, there had already been a variety of devices and software applications to implement.

Over the times of the pandemic, the concept of educational technology (EdTech) has become widely popular with software developers and investors. In fact, EdTech, despite a relatively long existence in the market, has now introduced a variety of software applications like Classplus and Edmingle that would facilitate the process of education in both developing and developed countries [3]. Moreover, the already existing educational sources powered by Microsoft and Google are also of great efficiency for today’s learners, as their plain yet efficient design helps students accommodate quickly to the process. Hence, taking everything into consideration, it might be concluded that the process for online education that was rapidly facilitated by a pandemic outbreak is likely to develop greatly over the next few years, creating a full-scale competition for conventional patterns of learning.

S. C. H. Hoi, D. Sahoo, J. Lu, and P. Zhao. “Online learning: A comprehensive survey,” SMU Technical Report , vol. 1, pp. 1-100, 2018.

A. Muhammad, and K. Anwar. “Online learning amid the COVID-19 pandemic: Students’ perspectives.” Online Submission , vol. 2, no. 1, pp. 45-51, 2020.

D. Shivangi. “Online learning: A panacea in the time of COVID-19 crisis.” Journal of Educational Technology Systems , vol. 49, no.1, pp. 5-22, 2020.

Cite this paper

• Chicago (N-B)
• Chicago (A-D)

StudyCorgi. (2022, February 16). Online Learning During the Pandemic. https://studycorgi.com/online-learning-during-the-pandemic/

"Online Learning During the Pandemic." StudyCorgi , 16 Feb. 2022, studycorgi.com/online-learning-during-the-pandemic/.

StudyCorgi . (2022) 'Online Learning During the Pandemic'. 16 February.

1. StudyCorgi . "Online Learning During the Pandemic." February 16, 2022. https://studycorgi.com/online-learning-during-the-pandemic/.

Bibliography

StudyCorgi . "Online Learning During the Pandemic." February 16, 2022. https://studycorgi.com/online-learning-during-the-pandemic/.

StudyCorgi . 2022. "Online Learning During the Pandemic." February 16, 2022. https://studycorgi.com/online-learning-during-the-pandemic/.

This paper, “Online Learning During the Pandemic”, was written and voluntary submitted to our free essay database by a straight-A student. Please ensure you properly reference the paper if you're using it to write your assignment.

Before publication, the StudyCorgi editorial team proofread and checked the paper to make sure it meets the highest standards in terms of grammar, punctuation, style, fact accuracy, copyright issues, and inclusive language. Last updated: February 16, 2022 .

If you are the author of this paper and no longer wish to have it published on StudyCorgi, request the removal . Please use the “ Donate your paper ” form to submit an essay.

Shaping the Future of Online Learning

Published may 22, 2024.

If you’ve been enrolled in any educational course or postsecondary educational program since 2020, chances are you’ve witnessed the rise in online learning firsthand .

The COVID-19 global pandemic shuttered storefronts, theaters, and classrooms alike, causing major disruptions in how goods and services were delivered. As consumers adopted Instacart for their grocery needs and streamed new blockbuster movies from the comfort of their living rooms, students needed an innovative way to bring their classes home. A year into the pandemic over 60% of all undergraduate students were enrolled in at least one online course , with 28% exclusively enrolled in online courses, according to the National Center for Education Statistics.

There are other reasons for the widespread adoption, including accessibility. Rural and international students who may be far removed from traditional educational institutions can now attend Harvard classes anywhere there’s an internet connection. Or, consider working adults seeking to progress or switch careers. Life doesn’t stop for a class, and attending one in-person can be prohibitive. While still challenging, logging into a virtual classroom is far more manageable. Online education is for everyone.

Technological and pedagogical developments have helped online learning progress beyond the days of discussion boards and essay uploads. Now, students can enjoy a multimedia educational experience that is rooted in the latest research, all while participating in the community of their “virtual campus”.

If you’re one of the millions of learners who have experienced online education, you might be interested to learn where it’s going next. At Harvard Online, the question, “what is the future of online learning?” guides an ongoing conversation that drives us everyday.

In this blog, we sat down with Catherine Breen , Managing Director of Harvard Online. With more than two decades of senior executive leadership at Harvard University and oversight of Harvard Online, Breen has an invaluable perspective on the future of online learning, and the exciting role Harvard Online is playing in bringing the future into the present. 

Catherine Breen, Managing Director of Harvard Online, in a team meeting.

Harvard Online (HO): How has the online learning landscape evolved in recent years? 

Catherine Breen (CB): At the beginning of the COVID-19 lockdown, there was a massive escalation in demand for online learning. Demand began to recede slowly as the months wore on and by late 2022, it started to level out. But we observed two big changes: Internally, the demand for Harvard Online content was still almost three times higher than pre-pandemic. Externally, in reaction to the demand surge, there was significant and rapid growth of new online course offerings and companies that purveyed varying types of digital products.    

HO: What is shaping the future of online learning today? 

CB: Because of the rapid and massive shift to online that occurred around the globe in the spring of 2020, the landscape changed permanently. There are many things shaping the future but here are just a few that I can see from my perspective:

• Increased adoption of online learning across all ages and levels of education: Everyone expanded their online course catalogs; new companies and offerings sprung up everywhere.
• Greater tech investment across organizations and industries: Organizations are investing more time, money, and effort into technology infrastructure, tools, and platforms to support online learning and participants in these courses.
• New pedagogical methods to bridge the gap between traditional and novel learning methods: Instructors have adapted their teaching methods for online, hybrid, and blended environments.
• Enhanced accessibility to quality education and learning experiences: Efforts have been made to improve access for students of all types, abilities, geographies, and backgrounds so that everyone can participate effectively.    

HO: What are the remaining challenges that online learning faces? 

CB: While these changes have improved the online learning experience, challenges remain, including addressing the digital divide, maximizing student engagement, and refining the quality of online courses.

The pandemic accelerated the adoption of online learning and its impact will likely continue to shape higher education for many years to come.  

HO: How does online learning contribute to Harvard's mission of promoting accessibility and inclusion in education, especially for learners who may not have traditional access to higher education?

CB: Online learning levels the playing field for learners in many ways.

Most students think that a Harvard-quality education is out of reach, for a variety of reasons. With online courses, however, learners from around the country and the world can take courses with Harvard instructors at their own pace at a more affordable price point.

Our online courses also typically incorporate a range of multimedia elements, allowing students with different learning styles to flourish. We also ensure that our online learning experiences are accessible to all learners, including those with disabilities. This commitment to inclusivity aligns with the broader goals of promoting equitable access to education.

Lastly, our online courses often include discussion forums and virtual communities where learners can connect and collaborate. This allows for interactions among students from diverse backgrounds and experiences, fostering a sense of belonging and inclusion.  

It’s clear that online learning has a lot to offer everyone, and it’s only getting better. In our next blog in this series, we’ll hear more from Cathy on how institutions can implement online learning modalities effectively. 

If you missed the first blog in this series detailing the future of online learning, you can check out the first blog here . To learn more about Harvard Online, explore our fully online course catalog here .

Related Blog Posts

Uniref brings harvard courses on web programming to syrian refugees.

Harvard Online is proud to provide access to education and experiences that help communities thrive.

Harvard Online in Your Workplace: Elevate Your Team's Professional Development

At Harvard Online we understand the value of an educated and skilled workforce.

A Decade of Innovation: Online Learning at Harvard

We are always asking, “What does the future look like for teaching and learning?”

The COVID-19 pandemic has changed education forever. This is how 

With schools shut across the world, millions of children have had to adapt to new types of learning. Image:  REUTERS/Gonzalo Fuentes

.chakra .wef-1c7l3mo{-webkit-transition:all 0.15s ease-out;transition:all 0.15s ease-out;cursor:pointer;-webkit-text-decoration:none;text-decoration:none;outline:none;color:inherit;}.chakra .wef-1c7l3mo:hover,.chakra .wef-1c7l3mo[data-hover]{-webkit-text-decoration:underline;text-decoration:underline;}.chakra .wef-1c7l3mo:focus,.chakra .wef-1c7l3mo[data-focus]{box-shadow:0 0 0 3px rgba(168,203,251,0.5);} Cathy Li

Farah lalani.

.chakra .wef-9dduvl{margin-top:16px;margin-bottom:16px;line-height:1.388;font-size:1.25rem;}@media screen and (min-width:56.5rem){.chakra .wef-9dduvl{font-size:1.125rem;}} Explore and monitor how .chakra .wef-15eoq1r{margin-top:16px;margin-bottom:16px;line-height:1.388;font-size:1.25rem;color:#F7DB5E;}@media screen and (min-width:56.5rem){.chakra .wef-15eoq1r{font-size:1.125rem;}} Education, Gender and Work is affecting economies, industries and global issues

.chakra .wef-1nk5u5d{margin-top:16px;margin-bottom:16px;line-height:1.388;color:#2846F8;font-size:1.25rem;}@media screen and (min-width:56.5rem){.chakra .wef-1nk5u5d{font-size:1.125rem;}} Get involved with our crowdsourced digital platform to deliver impact at scale

Stay up to date:, education, gender and work.

• The COVID-19 has resulted in schools shut all across the world. Globally, over 1.2 billion children are out of the classroom.
• As a result, education has changed dramatically, with the distinctive rise of e-learning, whereby teaching is undertaken remotely and on digital platforms.
• Research suggests that online learning has been shown to increase retention of information, and take less time, meaning the changes coronavirus have caused might be here to stay.

While countries are at different points in their COVID-19 infection rates, worldwide there are currently more than 1.2 billion children in 186 countries affected by school closures due to the pandemic. In Denmark, children up to the age of 11 are returning to nurseries and schools after initially closing on 12 March , but in South Korea students are responding to roll calls from their teachers online .

With this sudden shift away from the classroom in many parts of the globe, some are wondering whether the adoption of online learning will continue to persist post-pandemic, and how such a shift would impact the worldwide education market.

Even before COVID-19, there was already high growth and adoption in education technology, with global edtech investments reaching US$18.66 billion in 2019 and the overall market for online education projected to reach $350 Billion by 2025 . Whether it is language apps , virtual tutoring , video conferencing tools, or online learning software , there has been a significant surge in usage since COVID-19.

How is the education sector responding to COVID-19?

In response to significant demand, many online learning platforms are offering free access to their services, including platforms like BYJU’S , a Bangalore-based educational technology and online tutoring firm founded in 2011, which is now the world’s most highly valued edtech company . Since announcing free live classes on its Think and Learn app, BYJU’s has seen a 200% increase in the number of new students using its product, according to Mrinal Mohit, the company's Chief Operating Officer.

Tencent classroom, meanwhile, has been used extensively since mid-February after the Chinese government instructed a quarter of a billion full-time students to resume their studies through online platforms. This resulted in the largest “online movement” in the history of education with approximately 730,000 , or 81% of K-12 students, attending classes via the Tencent K-12 Online School in Wuhan.

Have you read?

The future of jobs report 2023, how to follow the growth summit 2023.

Other companies are bolstering capabilities to provide a one-stop shop for teachers and students. For example, Lark, a Singapore-based collaboration suite initially developed by ByteDance as an internal tool to meet its own exponential growth, began offering teachers and students unlimited video conferencing time, auto-translation capabilities, real-time co-editing of project work, and smart calendar scheduling, amongst other features. To do so quickly and in a time of crisis, Lark ramped up its global server infrastructure and engineering capabilities to ensure reliable connectivity.

Alibaba’s distance learning solution, DingTalk, had to prepare for a similar influx: “To support large-scale remote work, the platform tapped Alibaba Cloud to deploy more than 100,000 new cloud servers in just two hours last month – setting a new record for rapid capacity expansion,” according to DingTalk CEO, Chen Hang.

Some school districts are forming unique partnerships, like the one between The Los Angeles Unified School District and PBS SoCal/KCET to offer local educational broadcasts, with separate channels focused on different ages, and a range of digital options. Media organizations such as the BBC are also powering virtual learning; Bitesize Daily , launched on 20 April, is offering 14 weeks of curriculum-based learning for kids across the UK with celebrities like Manchester City footballer Sergio Aguero teaching some of the content.

What does this mean for the future of learning?

While some believe that the unplanned and rapid move to online learning – with no training, insufficient bandwidth, and little preparation – will result in a poor user experience that is unconducive to sustained growth, others believe that a new hybrid model of education will emerge, with significant benefits. “I believe that the integration of information technology in education will be further accelerated and that online education will eventually become an integral component of school education,“ says Wang Tao, Vice President of Tencent Cloud and Vice President of Tencent Education.

There have already been successful transitions amongst many universities. For example, Zhejiang University managed to get more than 5,000 courses online just two weeks into the transition using “DingTalk ZJU”. The Imperial College London started offering a course on the science of coronavirus, which is now the most enrolled class launched in 2020 on Coursera .

Many are already touting the benefits: Dr Amjad, a Professor at The University of Jordan who has been using Lark to teach his students says, “It has changed the way of teaching. It enables me to reach out to my students more efficiently and effectively through chat groups, video meetings, voting and also document sharing, especially during this pandemic. My students also find it is easier to communicate on Lark. I will stick to Lark even after coronavirus, I believe traditional offline learning and e-learning can go hand by hand."

These 3 charts show the global growth in online learning

The challenges of online learning.

There are, however, challenges to overcome. Some students without reliable internet access and/or technology struggle to participate in digital learning; this gap is seen across countries and between income brackets within countries. For example, whilst 95% of students in Switzerland, Norway, and Austria have a computer to use for their schoolwork, only 34% in Indonesia do, according to OECD data .

In the US, there is a significant gap between those from privileged and disadvantaged backgrounds: whilst virtually all 15-year-olds from a privileged background said they had a computer to work on, nearly 25% of those from disadvantaged backgrounds did not. While some schools and governments have been providing digital equipment to students in need, such as in New South Wales , Australia, many are still concerned that the pandemic will widenthe digital divide .

Is learning online as effective?

For those who do have access to the right technology, there is evidence that learning online can be more effective in a number of ways. Some research shows that on average, students retain 25-60% more material when learning online compared to only 8-10% in a classroom. This is mostly due to the students being able to learn faster online; e-learning requires 40-60% less time to learn than in a traditional classroom setting because students can learn at their own pace, going back and re-reading, skipping, or accelerating through concepts as they choose.

Nevertheless, the effectiveness of online learning varies amongst age groups. The general consensus on children, especially younger ones, is that a structured environment is required , because kids are more easily distracted. To get the full benefit of online learning, there needs to be a concerted effort to provide this structure and go beyond replicating a physical class/lecture through video capabilities, instead, using a range of collaboration tools and engagement methods that promote “inclusion, personalization and intelligence”, according to Dowson Tong, Senior Executive Vice President of Tencent and President of its Cloud and Smart Industries Group.

Since studies have shown that children extensively use their senses to learn, making learning fun and effective through use of technology is crucial, according to BYJU's Mrinal Mohit. “Over a period, we have observed that clever integration of games has demonstrated higher engagement and increased motivation towards learning especially among younger students, making them truly fall in love with learning”, he says.

A changing education imperative

It is clear that this pandemic has utterly disrupted an education system that many assert was already losing its relevance . In his book, 21 Lessons for the 21st Century , scholar Yuval Noah Harari outlines how schools continue to focus on traditional academic skills and rote learning , rather than on skills such as critical thinking and adaptability, which will be more important for success in the future. Could the move to online learning be the catalyst to create a new, more effective method of educating students? While some worry that the hasty nature of the transition online may have hindered this goal, others plan to make e-learning part of their ‘new normal’ after experiencing the benefits first-hand.

The importance of disseminating knowledge is highlighted through COVID-19

Major world events are often an inflection point for rapid innovation – a clear example is the rise of e-commerce post-SARS . While we have yet to see whether this will apply to e-learning post-COVID-19, it is one of the few sectors where investment has not dried up . What has been made clear through this pandemic is the importance of disseminating knowledge across borders, companies, and all parts of society. If online learning technology can play a role here, it is incumbent upon all of us to explore its full potential.

Our education system is losing relevance. Here's how to unleash its potential

3 ways the coronavirus pandemic could reshape education, celebrities are helping the uk's schoolchildren learn during lockdown, don't miss any update on this topic.

Create a free account and access your personalized content collection with our latest publications and analyses.

License and Republishing

World Economic Forum articles may be republished in accordance with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License, and in accordance with our Terms of Use.

The views expressed in this article are those of the author alone and not the World Economic Forum.

Related topics:

The agenda .chakra .wef-n7bacu{margin-top:16px;margin-bottom:16px;line-height:1.388;font-weight:400;} weekly.

A weekly update of the most important issues driving the global agenda

.chakra .wef-1dtnjt5{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;} More on Health and Healthcare Systems .chakra .wef-17xejub{-webkit-flex:1;-ms-flex:1;flex:1;justify-self:stretch;-webkit-align-self:stretch;-ms-flex-item-align:stretch;align-self:stretch;} .chakra .wef-nr1rr4{display:-webkit-inline-box;display:-webkit-inline-flex;display:-ms-inline-flexbox;display:inline-flex;white-space:normal;vertical-align:middle;text-transform:uppercase;font-size:0.75rem;border-radius:0.25rem;font-weight:700;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;line-height:1.2;-webkit-letter-spacing:1.25px;-moz-letter-spacing:1.25px;-ms-letter-spacing:1.25px;letter-spacing:1.25px;background:none;padding:0px;color:#B3B3B3;-webkit-box-decoration-break:clone;box-decoration-break:clone;-webkit-box-decoration-break:clone;}@media screen and (min-width:37.5rem){.chakra .wef-nr1rr4{font-size:0.875rem;}}@media screen and (min-width:56.5rem){.chakra .wef-nr1rr4{font-size:1rem;}} See all

Gender and health equity will only come with universal access to eye health – here's why

Dr. Princess Ifeoma Ike

May 24, 2024

Half the world is affected by oral disease – here’s how we can tackle this unmet healthcare need

Charlotte Edmond

May 23, 2024

This is how stress affects every organ in our bodies

Michelle Meineke and Kateryna Gordichuk

May 22, 2024

The care economy is one of humanity's most valuable assets. Here's how we secure its future

May 21, 2024

Antimicrobial resistance is a leading cause of global deaths. Now is the time to act

Dame Sally Davies, Hemant Ahlawat and Shyam Bishen

May 16, 2024

Inequality is driving antimicrobial resistance. Here's how to curb it

Michael Anderson, Gunnar Ljungqvist and Victoria Saint

May 15, 2024

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Review Article
• Published: 27 September 2021

Why lockdown and distance learning during the COVID-19 pandemic are likely to increase the social class achievement gap

• Sébastien Goudeau   ORCID: orcid.org/0000-0001-7293-0977 1 ,
• Camille Sanrey   ORCID: orcid.org/0000-0003-3158-1306 1 ,
• Arnaud Stanczak   ORCID: orcid.org/0000-0002-2596-1516 2 ,
• Antony Manstead   ORCID: orcid.org/0000-0001-7540-2096 3 &
• Céline Darnon   ORCID: orcid.org/0000-0003-2613-689X 2  

Nature Human Behaviour volume  5 ,  pages 1273–1281 ( 2021 ) Cite this article

115k Accesses

148 Citations

129 Altmetric

Metrics details

The COVID-19 pandemic has forced teachers and parents to quickly adapt to a new educational context: distance learning. Teachers developed online academic material while parents taught the exercises and lessons provided by teachers to their children at home. Considering that the use of digital tools in education has dramatically increased during this crisis, and it is set to continue, there is a pressing need to understand the impact of distance learning. Taking a multidisciplinary view, we argue that by making the learning process rely more than ever on families, rather than on teachers, and by getting students to work predominantly via digital resources, school closures exacerbate social class academic disparities. To address this burning issue, we propose an agenda for future research and outline recommendations to help parents, teachers and policymakers to limit the impact of the lockdown on social-class-based academic inequality.

Similar content being viewed by others

Large socio-economic, geographic and demographic disparities exist in exposure to school closures

Elementary school teachers’ perspectives about learning during the COVID-19 pandemic

Uncovering Covid-19, distance learning, and educational inequality in rural areas of Pakistan and China: a situational analysis method

The widespread effects of the COVID-19 pandemic that emerged in 2019–2020 have drastically increased health, social and economic inequalities 1 , 2 . For more than 900 million learners around the world, the pandemic led to the closure of schools and universities 3 . This exceptional situation forced teachers, parents and students to quickly adapt to a new educational context: distance learning. Teachers had to develop online academic materials that could be used at home to ensure educational continuity while ensuring the necessary physical distancing. Primary and secondary school students suddenly had to work with various kinds of support, which were usually provided online by their teachers. For college students, lockdown often entailed returning to their hometowns while staying connected with their teachers and classmates via video conferences, email and other digital tools. Despite the best efforts of educational institutions, parents and teachers to keep all children and students engaged in learning activities, ensuring educational continuity during school closure—something that is difficult for everyone—may pose unique material and psychological challenges for working-class families and students.

Not only did the pandemic lead to the closure of schools in many countries, often for several weeks, it also accelerated the digitalization of education and amplified the role of parental involvement in supporting the schoolwork of their children. Thus, beyond the specific circumstances of the COVID-19 lockdown, we believe that studying the effects of the pandemic on academic inequalities provides a way to more broadly examine the consequences of school closure and related effects (for example, digitalization of education) on social class inequalities. Indeed, bearing in mind that (1) the risk of further pandemics is higher than ever (that is, we are in a ‘pandemic era’ 4 , 5 ) and (2) beyond pandemics, the use of digital tools in education (and therefore the influence of parental involvement) has dramatically increased during this crisis, and is set to continue, there is a pressing need for an integrative and comprehensive model that examines the consequences of distance learning. Here, we propose such an integrative model that helps us to understand the extent to which the school closures associated with the pandemic amplify economic, digital and cultural divides that in turn affect the psychological functioning of parents, students and teachers in a way that amplifies academic inequalities. Bringing together research in social sciences, ranging from economics and sociology to social, cultural, cognitive and educational psychology, we argue that by getting students to work predominantly via digital resources rather than direct interactions with their teachers, and by making the learning process rely more than ever on families rather than teachers, school closures exacerbate social class academic disparities.

First, we review research showing that social class is associated with unequal access to digital tools, unequal familiarity with digital skills and unequal uses of such tools for learning purposes 6 , 7 . We then review research documenting how unequal familiarity with school culture, knowledge and skills can also contribute to the accentuation of academic inequalities 8 , 9 . Next, we present the results of surveys conducted during the 2020 lockdown showing that the quality and quantity of pedagogical support received from schools varied according to the social class of families (for examples, see refs. 10 , 11 , 12 ). We then argue that these digital, cultural and structural divides represent barriers to the ability of parents to provide appropriate support for children during distance learning (Fig. 1 ). These divides also alter the levels of self-efficacy of parents and children, thereby affecting their engagement in learning activities 13 , 14 . In the final section, we review preliminary evidence for the hypothesis that distance learning widens the social class achievement gap and we propose an agenda for future research. In addition, we outline recommendations that should help parents, teachers and policymakers to use social science research to limit the impact of school closure and distance learning on the social class achievement gap.

Economic, structural, digital and cultural divides influence the psychological functioning of parents and students in a way that amplify inequalities.

The digital divide

Unequal access to digital resources.

Although the use of digital technologies is almost ubiquitous in developed nations, there is a digital divide such that some people are more likely than others to be numerically excluded 15 (Fig. 1 ). Social class is a strong predictor of digital disparities, including the quality of hardware, software and Internet access 16 , 17 , 18 . For example, in 2019, in France, around 1 in 5 working-class families did not have personal access to the Internet compared with less than 1 in 20 of the most privileged families 19 . Similarly, in 2020, in the United Kingdom, 20% of children who were eligible for free school meals did not have access to a computer at home compared with 7% of other children 20 . In 2021, in the United States, 41% of working-class families do not own a laptop or desktop computer and 43% do not have broadband compared with 8% and 7%, respectively, of upper/middle-class Americans 21 . A similar digital gap is also evident between lower-income and higher-income countries 22 .

Second, simply having access to a computer and an Internet connection does not ensure effective distance learning. For example, many of the educational resources sent by teachers need to be printed, thereby requiring access to printers. Moreover, distance learning is more difficult in households with only one shared computer compared with those where each family member has their own 23 . Furthermore, upper/middle-class families are more likely to be able to guarantee a suitable workspace for each child than their working-class counterparts 24 .

In the context of school closures, such disparities are likely to have important consequences for educational continuity. In line with this idea, a survey of approximately 4,000 parents in the United Kingdom confirmed that during lockdown, more than half of primary school children from the poorest families did not have access to their own study space and were less well equipped for distance learning than higher-income families 10 . Similarly, a survey of around 1,300 parents in the Netherlands found that during lockdown, children from working-class families had fewer computers at home and less room to study than upper/middle-class children 11 .

Data from non-Western countries highlight a more general digital divide, showing that developing countries have poorer access to digital equipment. For example, in India in 2018, only 10.7% of households possessed a digital device 25 , while in Pakistan in 2020, 31% of higher-education teachers did not have Internet access and 68.4% did not have a laptop 26 . In general, developing countries lack access to digital technologies 27 , 28 , and these difficulties of access are even greater in rural areas (for example, see ref. 29 ). Consequently, school closures have huge repercussions for the continuity of learning in these countries. For example, in India in 2018, only 11% of the rural and 40% of the urban population above 14 years old could use a computer and access the Internet 25 . Time spent on education during school closure decreased by 80% in Bangladesh 30 . A similar trend was observed in other countries 31 , with only 22% of children engaging in remote learning in Kenya 32 and 50% in Burkina Faso 33 . In Ghana, 26–32% of children spent no time at all on learning during the pandemic 34 . Beyond the overall digital divide, social class disparities are also evident in developing countries, with lower access to digital resources among households in which parental educational levels were low (versus households in which parental educational levels were high; for example, see ref. 35 for Nigeria and ref. 31 for Ecuador).

Unequal digital skills

In addition to unequal access to digital tools, there are also systematic variations in digital skills 36 , 37 (Fig. 1 ). Upper/middle-class families are more familiar with digital tools and resources and are therefore more likely to have the digital skills needed for distance learning 38 , 39 , 40 . These digital skills are particularly useful during school closures, both for students and for parents, for organizing, retrieving and correctly using the resources provided by the teachers (for example, sending or receiving documents by email, printing documents or using word processors).

Social class disparities in digital skills can be explained in part by the fact that children from upper/middle-class families have the opportunity to develop digital skills earlier than working-class families 41 . In member countries of the OECD (Organisation for Economic Co-operation and Development), only 23% of working-class children had started using a computer at the age of 6 years or earlier compared with 43% of upper/middle-class children 42 . Moreover, because working-class people tend to persist less than upper/middle-class people when confronted with digital difficulties 23 , the use of digital tools and resources for distance learning may interfere with the ability of parents to help children with their schoolwork.

Unequal use of digital tools

A third level of digital divide concerns variations in digital tool use 18 , 43 (Fig. 1 ). Upper/middle-class families are more likely to use digital resources for work and education 6 , 41 , 44 , whereas working-class families are more likely to use these resources for entertainment, such as electronic games or social media 6 , 45 . This divide is also observed among students, whereby working-class students tend to use digital technologies for leisure activities, whereas their upper/middle-class peers are more likely to use them for academic activities 46 and to consider that computers and the Internet provide an opportunity for education and training 23 . Furthermore, working-class families appear to regulate the digital practices of their children less 47 and are more likely to allow screens in the bedrooms of children and teenagers without setting limits on times or practices 48 .

In sum, inequalities in terms of digital resources, skills and use have strong implications for distance learning. This is because they make working-class students and parents particularly vulnerable when learning relies on extensive use of digital devices rather than on face-to-face interaction with teachers.

The cultural divide

Even if all three levels of digital divide were closed, upper/middle-class families would still be better prepared than working-class families to ensure educational continuity for their children. Upper/middle-class families are more familiar with the academic knowledge and skills that are expected and valued in educational settings, as well as with the independent, autonomous way of learning that is valued in the school culture and becomes even more important during school closure (Fig. 1 ).

Unequal familiarity with academic knowledge and skills

According to classical social reproduction theory 8 , 49 , school is not a neutral place in which all forms of language and knowledge are equally valued. Academic contexts expect and value culture-specific and taken-for-granted forms of knowledge, skills and ways of being, thinking and speaking that are more in tune with those developed through upper/middle-class socialization (that is, ‘cultural capital’ 8 , 50 , 51 , 52 , 53 ). For instance, academic contexts value interest in the arts, museums and literature 54 , 55 , a type of interest that is more likely to develop through socialization in upper/middle-class families than in working-class socialization 54 , 56 . Indeed, upper/middle-class parents are more likely than working-class parents to engage in activities that develop this cultural capital. For example, they possess more books and cultural objects at home, read more stories to their children and visit museums and libraries more often (for examples, see refs. 51 , 54 , 55 ). Upper/middle-class children are also more involved in extra-curricular activities (for example, playing a musical instrument) than working-class children 55 , 56 , 57 .

Beyond this implicit familiarization with the school curriculum, upper/middle-class parents more often organize educational activities that are explicitly designed to develop academic skills of their children 57 , 58 , 59 . For example, they are more likely to monitor and re-explain lessons or use games and textbooks to develop and reinforce academic skills (for example, labelling numbers, letters or colours 57 , 60 ). Upper/middle-class parents also provide higher levels of support and spend more time helping children with homework than working-class parents (for examples, see refs. 61 , 62 ). Thus, even if all parents are committed to the academic success of their children, working-class parents have fewer chances to provide the help that children need to complete homework 63 , and homework is more beneficial for children from upper-middle class families than for children from working-class families 64 , 65 .

School closures amplify the impact of cultural inequalities

The trends described above have been observed in ‘normal’ times when schools are open. School closures, by making learning rely more strongly on practices implemented at home (rather than at school), are likely to amplify the impact of these disparities. Consistent with this idea, research has shown that the social class achievement gap usually greatly widens during school breaks—a phenomenon described as ‘summer learning loss’ or ‘summer setback’ 66 , 67 , 68 . During holidays, the learning by children tends to decline, and this is particularly pronounced in children from working-class families. Consequently, the social class achievement gap grows more rapidly during the summer months than it does in the rest of the year. This phenomenon is partly explained by the fact that during the break from school, social class disparities in investment in activities that are beneficial for academic achievement (for example, reading, travelling to a foreign country or museum visits) are more pronounced.

Therefore, when they are out of school, children from upper/middle-class backgrounds may continue to develop academic skills unlike their working-class counterparts, who may stagnate or even regress. Research also indicates that learning loss during school breaks tends to be cumulative 66 . Thus, repeated episodes of school closure are likely to have profound consequences for the social class achievement gap. Consistent with the idea that school closures could lead to similar processes as those identified during summer breaks, a recent survey indicated that during the COVID-19 lockdown in the United Kingdom, children from upper/middle-class families spent more time on educational activities (5.8 h per day) than those from working-class families (4.5 h per day) 7 , 69 .

Unequal dispositions for autonomy and self-regulation

School closures have encouraged autonomous work among students. This ‘independent’ way of studying is compatible with the family socialization of upper/middle-class students, but does not match the interdependent norms more commonly associated with working-class contexts 9 . Upper/middle-class contexts tend to promote cultural norms of independence whereby individuals perceive themselves as autonomous actors, independent of other individuals and of the social context, able to pursue their own goals 70 . For example, upper/middle-class parents tend to invite children to express their interests, preferences and opinions during the various activities of everyday life 54 , 55 . Conversely, in working-class contexts characterized by low economic resources and where life is more uncertain, individuals tend to perceive themselves as interdependent, connected to others and members of social groups 53 , 70 , 71 . This interdependent self-construal fits less well with the independent culture of academic contexts. This cultural mismatch between interdependent self-construal common in working-class students and the independent norms of the educational institution has negative consequences for academic performance 9 .

Once again, the impact of these differences is likely to be amplified during school closures, when being able to work alone and autonomously is especially useful. The requirement to work alone is more likely to match the independent self-construal of upper/middle-class students than the interdependent self-construal of working-class students. In the case of working-class students, this mismatch is likely to increase their difficulties in working alone at home. Supporting our argument, recent research has shown that working-class students tend to underachieve in contexts where students work individually compared with contexts where students work with others 72 . Similarly, during school closures, high self-regulation skills (for example, setting goals, selecting appropriate learning strategies and maintaining motivation 73 ) are required to maintain study activities and are likely to be especially useful for using digital resources efficiently. Research has shown that students from working-class backgrounds typically develop their self-regulation skills to a lesser extent than those from upper/middle-class backgrounds 74 , 75 , 76 .

Interestingly, some authors have suggested that independent (versus interdependent) self-construal may also affect communication with teachers 77 . Indeed, in the context of distance learning, working-class families are less likely to respond to the communication of teachers because their ‘interdependent’ self leads them to respect hierarchies, and thus perceive teachers as an expert who ‘can be trusted to make the right decisions for learning’. Upper/middle class families, relying on ‘independent’ self-construal, are more inclined to seek individualized feedback, and therefore tend to participate to a greater extent in exchanges with teachers. Such cultural differences are important because they can also contribute to the difficulties encountered by working-class families.

The structural divide: unequal support from schools

The issues reviewed thus far all increase the vulnerability of children and students from underprivileged backgrounds when schools are closed. To offset these disadvantages, it might be expected that the school should increase its support by providing additional resources for working-class students. However, recent data suggest that differences in the material and human resources invested in providing educational support for children during periods of school closure were—paradoxically—in favour of upper/middle-class students (Fig. 1 ). In England, for example, upper/middle-class parents reported benefiting from online classes and video-conferencing with teachers more often than working-class parents 10 . Furthermore, active help from school (for example, online teaching, private tutoring or chats with teachers) occurred more frequently in the richest households (64% of the richest households declared having received help from school) than in the poorest households (47%). Another survey found that in the United Kingdom, upper/middle-class children were more likely to take online lessons every day (30%) than working-class students (16%) 12 . This substantial difference might be due, at least in part, to the fact that private schools are better equipped in terms of online platforms (60% of schools have at least one online platform) than state schools (37%, and 23% in the most deprived schools) and were more likely to organize daily online lessons. Similarly, in the United Kingdom, in schools with a high proportion of students eligible for free school meals, teachers were less inclined to broadcast an online lesson for their pupils 78 . Interestingly, 58% of teachers in the wealthiest areas reported having messaged their students or their students’ parents during lockdown compared with 47% in the most deprived schools. In addition, the probability of children receiving technical support from the school (for example, by providing pupils with laptops or other devices) is, surprisingly, higher in the most advantaged schools than in the most deprived 78 .

In addition to social class disparities, there has been less support from schools for African-American and Latinx students. During school closures in the United States, 40% of African-American students and 30% of Latinx students received no online teaching compared with 10% of white students 79 . Another source of inequality is that the probability of school closure was correlated with social class and race. In the United States, for example, school closures from September to December 2020 were more common in schools with a high proportion of racial/ethnic minority students, who experience homelessness and are eligible for free/discounted school meals 80 .

Similarly, access to educational resources and support was lower in poorer (compared with richer) countries 81 . In sub-Saharan Africa, during lockdown, 45% of children had no exposure at all to any type of remote learning. Of those who did, the medium was mostly radio, television or paper rather than digital. In African countries, at most 10% of children received some material through the Internet. In Latin America, 90% of children received some remote learning, but less than half of that was through the internet—the remainder being via radio and television 81 . In Ecuador, high-school students from the lowest wealth quartile had fewer remote-learning opportunities, such as Google class/Zoom, than students from the highest wealth quartile 31 .

Thus, the achievement gap and its accentuation during lockdown are due not only to the cultural and digital disadvantages of working-class families but also to unequal support from schools. This inequality in school support is not due to teachers being indifferent to or even supportive of social stratification. Rather, we believe that these effects are fundamentally structural. In many countries, schools located in upper/middle-class neighbourhoods have more money than those in the poorest neighbourhoods. Moreover, upper/middle-class parents invest more in the schools of their children than working-class parents (for example, see ref. 82 ), and schools have an interest in catering more for upper/middle-class families than for working-class families 83 . Additionally, the expectation of teachers may be lower for working-class children 84 . For example, they tend to estimate that working-class students invest less effort in learning than their upper/middle-class counterparts 85 . These differences in perception may have influenced the behaviour of teachers during school closure, such that teachers in privileged neighbourhoods provided more information to students because they expected more from them in term of effort and achievement. The fact that upper/middle-class parents are better able than working-class parents to comply with the expectations of teachers (for examples, see refs. 55 , 86 ) may have reinforced this phenomenon. These discrepancies echo data showing that working-class students tend to request less help in their schoolwork than upper/middle-class ones 87 , and they may even avoid asking for help because they believe that such requests could lead to reprimands 88 . During school closures, these students (and their families) may in consequence have been less likely to ask for help and resources. Jointly, these phenomena have resulted in upper/middle-class families receiving more support from schools during lockdown than their working-class counterparts.

Psychological effects of digital, cultural and structural divides

Despite being strongly influenced by social class, differences in academic achievement are often interpreted by parents, teachers and students as reflecting differences in ability 89 . As a result, upper/middle-class students are usually perceived—and perceive themselves—as smarter than working-class students, who are perceived—and perceive themselves—as less intelligent 90 , 91 , 92 or less able to succeed 93 . Working-class students also worry more about the fact that they might perform more poorly than upper/middle-class students 94 , 95 . These fears influence academic learning in important ways. In particular, they can consume cognitive resources when children and students work on academic tasks 96 , 97 . Self-efficacy also plays a key role in engaging in learning and perseverance in the face of difficulties 13 , 98 . In addition, working-class students are those for whom the fear of being outperformed by others is the most negatively related to academic performance 99 .

The fact that working-class children and students are less familiar with the tasks set by teachers, and less well equipped and supported, makes them more likely to experience feelings of incompetence (Fig. 1 ). Working-class parents are also more likely than their upper/middle-class counterparts to feel unable to help their children with schoolwork. Consistent with this, research has shown that both working-class students and parents have lower feelings of academic self-efficacy than their upper/middle-class counterparts 100 , 101 . These differences have been documented under ‘normal’ conditions but are likely to be exacerbated during distance learning. Recent surveys conducted during the school closures have confirmed that upper/middle-class families felt better able to support their children in distance learning than did working-class families 10 and that upper/middle-class parents helped their children more and felt more capable to do so 11 , 12 .

Pandemic disparity, future directions and recommendations

The research reviewed thus far suggests that children and their families are highly unequal with respect to digital access, skills and use. It also shows that upper/middle-class students are more likely to be supported in their homework (by their parents and teachers) than working-class students, and that upper/middle-class students and parents will probably feel better able than working-class ones to adapt to the context of distance learning. For all these reasons, we anticipate that as a result of school closures, the COVID-19 pandemic will substantially increase the social class achievement gap. Because school closures are a recent occurrence, it is too early to measure with precision their effects on the widening of the achievement gap. However, some recent data are consistent with this idea.

Evidence for a widening gap during the pandemic

Comparing academic achievement in 2020 with previous years provides an early indication of the effects of school closures during the pandemic. In France, for example, first and second graders take national evaluations at the beginning of the school year. Initial comparisons of the results for 2020 with those from previous years revealed that the gap between schools classified as ‘priority schools’ (those in low-income urban areas) and schools in higher-income neighbourhoods—a gap observed every year—was particularly pronounced in 2020 in both French and mathematics 102 .

Similarly, in the Netherlands, national assessments take place twice a year. In 2020, they took place both before and after school closures. A recent analysis compared progress during this period in 2020 in mathematics/arithmetic, spelling and reading comprehension for 7–11-year-old students within the same period in the three previous years 103 . Results indicated a general learning loss in 2020. More importantly, for the 8% of working-class children, the losses were 40% greater than they were for upper/middle-class children.

Similar results were observed in Belgium among students attending the final year of primary school. Compared with students from previous cohorts, students affected by school closures experienced a substantial decrease in their mathematics and language scores, with children from more disadvantaged backgrounds experiencing greater learning losses 104 . Likewise, oral reading assessments in more than 100 school districts in the United States showed that the development of this skill among children in second and third grade significantly slowed between Spring and Autumn 2020, but this slowdown was more pronounced in schools from lower-achieving districts 105 .

It is likely that school closures have also amplified racial disparities in learning and achievement. For example, in the United States, after the first lockdown, students of colour lost the equivalent of 3–5 months of learning, whereas white students were about 1–3 months behind. Moreover, in the Autumn, when some students started to return to classrooms, African-American and Latinx students were more likely to continue distance learning, despite being less likely to have access to the digital tools, Internet access and live contact with teachers 106 .

In some African countries (for example, Ethiopia, Kenya, Liberia, Tanzania and Uganda), the COVID-19 crisis has resulted in learning loss ranging from 6 months to more 1 year 107 , and this learning loss appears to be greater for working-class children (that is, those attending no-fee schools) than for upper/middle-class children 108 .

These findings show that school closures have exacerbated achievement gaps linked to social class and ethnicity. However, more research is needed to address the question of whether school closures differentially affect the learning of students from working- and upper/middle-class families.

Future directions

First, to assess the specific and unique impact of school closures on student learning, longitudinal research should compare student achievement at different times of the year, before, during and after school closures, as has been done to document the summer learning loss 66 , 109 . In the coming months, alternating periods of school closure and opening may occur, thereby presenting opportunities to do such research. This would also make it possible to examine whether the gap diminishes a few weeks after children return to in-school learning or whether, conversely, it increases with time because the foundations have not been sufficiently acquired to facilitate further learning 110 .

Second, the mechanisms underlying the increase in social class disparities during school closures should be examined. As discussed above, school closures result in situations for which students are unevenly prepared and supported. It would be appropriate to seek to quantify the contribution of each of the factors that might be responsible for accentuating the social class achievement gap. In particular, distinguishing between factors that are relatively ‘controllable’ (for example, resources made available to pupils) and those that are more difficult to control (for example, the self-efficacy of parents in supporting the schoolwork of their children) is essential to inform public policy and teaching practices.

Third, existing studies are based on general comparisons and very few provide insights into the actual practices that took place in families during school closure and how these practices affected the achievement gap. For example, research has documented that parents from working-class backgrounds are likely to find it more difficult to help their children to complete homework and to provide constructive feedback 63 , 111 , something that could in turn have a negative impact on the continuity of learning of their children. In addition, it seems reasonable to assume that during lockdown, parents from upper/middle-class backgrounds encouraged their children to engage in practices that, even if not explicitly requested by teachers, would be beneficial to learning (for example, creative activities or reading). Identifying the practices that best predict the maintenance or decline of educational achievement during school closures would help identify levers for intervention.

Finally, it would be interesting to investigate teaching practices during school closures. The lockdown in the spring of 2020 was sudden and unexpected. Within a few days, teachers had to find a way to compensate for the school closure, which led to highly variable practices. Some teachers posted schoolwork on platforms, others sent it by email, some set work on a weekly basis while others set it day by day. Some teachers also set up live sessions in large or small groups, providing remote meetings for questions and support. There have also been variations in the type of feedback given to students, notably through the monitoring and correcting of work. Future studies should examine in more detail what practices schools and teachers used to compensate for the school closures and their effects on widening, maintaining or even reducing the gap, as has been done for certain specific literacy programmes 112 as well as specific instruction topics (for example, ecology and evolution 113 ).

Practical recommendations

We are aware of the debate about whether social science research on COVID-19 is suitable for making policy decisions 114 , and we draw attention to the fact that some of our recommendations (Table 1 ) are based on evidence from experiments or interventions carried out pre-COVID while others are more speculative. In any case, we emphasize that these suggestions should be viewed with caution and be tested in future research. Some of our recommendations could be implemented in the event of new school closures, others only when schools re-open. We also acknowledge that while these recommendations are intended for parents and teachers, their implementation largely depends on the adoption of structural policies. Importantly, given all the issues discussed above, we emphasize the importance of prioritizing, wherever possible, in-person learning over remote learning 115 and where this is not possible, of implementing strong policies to support distance learning, especially for disadvantaged families.

Where face-to face teaching is not possible and teachers are responsible for implementing distance learning, it will be important to make them aware of the factors that can exacerbate inequalities during lockdown and to provide them with guidance about practices that would reduce these inequalities. Thus, there is an urgent need for interventions aimed at making teachers aware of the impact of the social class of children and families on the following factors: (1) access to, familiarity with and use of digital devices; (2) familiarity with academic knowledge and skills; and (3) preparedness to work autonomously. Increasing awareness of the material, cultural and psychological barriers that working-class children and families face during lockdown should increase the quality and quantity of the support provided by teachers and thereby positively affect the achievements of working-class students.

In addition to increasing the awareness of teachers of these barriers, teachers should be encouraged to adjust the way they communicate with working-class families due to differences in self-construal compared with upper/middle-class families 77 . For example, questions about family (rather than personal) well-being would be congruent with interdependent self-construals. This should contribute to better communication and help keep a better track of the progress of students during distance learning.

It is also necessary to help teachers to engage in practices that have a chance of reducing inequalities 53 , 116 . Particularly important is that teachers and schools ensure that homework can be done by all children, for example, by setting up organizations that would help children whose parents are not in a position to monitor or assist with the homework of their children. Options include homework help groups and tutoring by teachers after class. When schools are open, the growing tendency to set homework through digital media should be resisted as far as possible given the evidence we have reviewed above. Moreover, previous research has underscored the importance of homework feedback provided by teachers, which is positively related to the amount of homework completed and predictive of academic performance 117 . Where homework is web-based, it has also been shown that feedback on web-based homework enhances the learning of students 118 . It therefore seems reasonable to predict that the social class achievement gap will increase more slowly (or even remain constant or be reversed) in schools that establish individualized monitoring of students, by means of regular calls and feedback on homework, compared with schools where the support provided to pupils is more generic.

Given that learning during lockdown has increasingly taken place in family settings, we believe that interventions involving the family are also likely to be effective 119 , 120 , 121 . Simply providing families with suitable material equipment may be insufficient. Families should be given training in the efficient use of digital technology and pedagogical support. This would increase the self-efficacy of parents and students, with positive consequences for achievement. Ideally, such training would be delivered in person to avoid problems arising from the digital divide. Where this is not possible, individualized online tutoring should be provided. For example, studies conducted during the lockdown in Botswana and Italy have shown that individual online tutoring directly targeting either parents or students in middle school has a positive impact on the achievement of students, particularly for working-class students 122 , 123 .

Interventions targeting families should also address the psychological barriers faced by working-class families and children. Some interventions have already been designed and been shown to be effective in reducing the social class achievement gap, particularly in mathematics and language 124 , 125 , 126 . For example, research showed that an intervention designed to train low-income parents in how to support the mathematical development of their pre-kindergarten children (including classes and access to a library of kits to use at home) increased the quality of support provided by the parents, with a corresponding impact on the development of mathematical knowledge of their children. Such interventions should be particularly beneficial in the context of school closure.

Beyond its impact on academic performance and inequalities, the COVID-19 crisis has shaken the economies of countries around the world, casting millions of families around the world into poverty 127 , 128 , 129 . As noted earlier, there has been a marked increase in economic inequalities, bringing with it all the psychological and social problems that such inequalities create 130 , 131 , especially for people who live in scarcity 132 . The increase in educational inequalities is just one facet of the many difficulties that working-class families will encounter in the coming years, but it is one that could seriously limit the chances of their children escaping from poverty by reducing their opportunities for upward mobility. In this context, it should be a priority to concentrate resources on the most deprived students. A large proportion of the poorest households do not own a computer and do not have personal access to the Internet, which has important consequences for distance learning. During school closures, it is therefore imperative to provide such families with adequate equipment and Internet service, as was done in some countries in spring 2020. Even if the provision of such equipment is not in itself sufficient, it is a necessary condition for ensuring pedagogical continuity during lockdown.

Finally, after prolonged periods of school closure, many students may not have acquired the skills needed to pursue their education. A possible consequence would be an increase in the number of students for whom teachers recommend class repetitions. Class repetitions are contentious. On the one hand, class repetition more frequently affects working-class children and is not efficient in terms of learning improvement 133 . On the other hand, accepting lower standards of academic achievement or even suspending the practice of repeating a class could lead to pupils pursuing their education without mastering the key abilities needed at higher grades. This could create difficulties in subsequent years and, in this sense, be counterproductive. We therefore believe that the most appropriate way to limit the damage of the pandemic would be to help children catch up rather than allowing them to continue without mastering the necessary skills. As is being done in some countries, systematic remedial courses (for example, summer learning programmes) should be organized and financially supported following periods of school closure, with priority given to pupils from working-class families. Such interventions have genuine potential in that research has shown that participation in remedial summer programmes is effective in reducing learning loss during the summer break 134 , 135 , 136 . For example, in one study 137 , 438 students from high-poverty schools were offered a multiyear summer school programme that included various pedagogical and enrichment activities (for example, science investigation and music) and were compared with a ‘no-treatment’ control group. Students who participated in the summer programme progressed more than students in the control group. A meta-analysis 138 of 41 summer learning programmes (that is, classroom- and home-based summer interventions) involving children from kindergarten to grade 8 showed that these programmes had significantly larger benefits for children from working-class families. Although such measures are costly, the cost is small compared to the price of failing to fulfil the academic potential of many students simply because they were not born into upper/middle-class families.

The unprecedented nature of the current pandemic means that we lack strong data on what the school closure period is likely to produce in terms of learning deficits and the reproduction of social inequalities. However, the research discussed in this article suggests that there are good reasons to predict that this period of school closures will accelerate the reproduction of social inequalities in educational achievement.

By making school learning less dependent on teachers and more dependent on families and digital tools and resources, school closures are likely to greatly amplify social class inequalities. At a time when many countries are experiencing second, third or fourth waves of the pandemic, resulting in fresh periods of local or general lockdowns, systematic efforts to test these predictions are urgently needed along with steps to reduce the impact of school closures on the social class achievement gap.

Bambra, C., Riordan, R., Ford, J. & Matthews, F. The COVID-19 pandemic and health inequalities. J. Epidemiol. Commun. Health 74 , 964–968 (2020).

Google Scholar  

Johnson, P, Joyce, R & Platt, L. The IFS Deaton Review of Inequalities: A New Year’s Message (Institute for Fiscal Studies, 2021).

Education: from disruption to recovery. https://en.unesco.org/covid19/educationresponse (UNESCO, 2020).

Daszak, P. We are entering an era of pandemics—it will end only when we protect the rainforest. The Guardian (28 July 2020); https://www.theguardian.com/commentisfree/2020/jul/28/pandemic-era-rainforest-deforestation-exploitation-wildlife-disease

Dobson, A. P. et al. Ecology and economics for pandemic prevention. Science 369 , 379–381 (2020).

Article   CAS   PubMed   Google Scholar  

Harris, C., Straker, L. & Pollock, C. A socioeconomic related ‘digital divide’ exists in how, not if, young people use computers. PLoS ONE 12 , e0175011 (2017).

Article   PubMed   PubMed Central   Google Scholar  

Zhang, M. Internet use that reproduces educational inequalities: evidence from big data. Comput. Educ. 86 , 212–223 (2015).

Article   Google Scholar  

Bourdieu, P. & Passeron, J. C. Reproduction in Education, Society and Culture (Sage, 1990).

Stephens, N. M., Fryberg, S. A., Markus, H. R., Johnson, C. S. & Covarrubias, R. Unseen disadvantage: how American universities’ focus on independence undermines the academic performance of first-generation college students. J. Pers. Soc. Psychol. 102 , 1178–1197 (2012).

Article   PubMed   Google Scholar  

Andrew, A. et al. Inequalities in children’s experiences of home learning during the COVID-19 lockdown in England. Fisc. Stud. 41 , 653–683 (2020).

Bol, T. Inequality in homeschooling during the Corona crisis in the Netherlands. First results from the LISS Panel. Preprint at SocArXiv https://doi.org/10.31235/osf.io/hf32q (2020).

Cullinane, C. & Montacute, R. COVID-19 and Social Mobility. Impact Brief #1: School Shutdown (The Sutton Trust, 2020).

Bandura, A. Self-efficacy: toward a unifying theory of behavioral change. Psychol. Rev. 84 , 191–215 (1977).

Prior, D. D., Mazanov, J., Meacheam, D., Heaslip, G. & Hanson, J. Attitude, digital literacy and self efficacy: low-on effects for online learning behavior. Internet High. Educ. 29 , 91–97 (2016).

Robinson, L. et al. Digital inequalities 2.0: legacy inequalities in the information age. First Monday https://doi.org/10.5210/fm.v25i7.10842 (2020).

Cruz-Jesus, F., Vicente, M. R., Bacao, F. & Oliveira, T. The education-related digital divide: an analysis for the EU-28. Comput. Hum. Behav. 56 , 72–82 (2016).

Rice, R. E. & Haythornthwaite, C. In The Handbook of New Media (eds Lievrouw, L. A. & Livingstone S. M.), 92–113 (Sage, 2006).

Yates, S., Kirby, J. & Lockley, E. Digital media use: differences and inequalities in relation to class and age. Sociol. Res. Online 20 , 71–91 (2015).

Legleye, S. & Rolland, A. Une personne sur six n’utilise pas Internet, plus d’un usager sur trois manques de compétences numériques de base [One in six people do not use the Internet, more than one in three users lack basic digital skills] (INSEE Première, 2019).

Green, F. Schoolwork in lockdown: new evidence on the epidemic of educational poverty (LLAKES Centre, 2020); https://www.llakes.ac.uk/wp-content/uploads/2021/03/RP-67-Francis-Green-Research-Paper-combined-file.pdf

Vogels, E. Digital divide persists even as americans with lower incomes make gains in tech adoption (Pew Research Center, 2021); https://www.pewresearch.org/fact-tank/2021/06/22/digital-divide-persists-even-as-americans-with-lower-incomes-make-gains-in-tech-adoption/

McBurnie, C., Adam, T. & Kaye, T. Is there learning continuity during the COVID-19 pandemic? A synthesis of the emerging evidence. J. Learn. Develop. http://dspace.col.org/handle/11599/3720 (2020).

Baillet, J., Croutte, P. & Prieur, V. Baromètre du numérique 2019 [Digital barometer 2019] (Sourcing Crédoc, 2019).

Giraud, F., Bertrand, J., Court, M. & Nicaise, S. In Enfances de Classes. De l’inégalité Parmi les Enfants (ed. Lahire, B.) 933–952 (Seuil, 2019).

Ahamed, S. & Siddiqui, Z. Disparity in access to quality education and the digital divide (Ideas for India, 2020); https://www.ideasforindia.in/topics/macroeconomics/disparity-in-access-to-quality-education-and-the-digital-divide.html

Soomro, K. A., Kale, U., Curtis, R., Akcaoglu, M. & Bernstein, M. Digital divide among higher education faculty. Int. J. Educ. Tech. High. Ed. 17 , 21 (2020).

Meng, Q. & Li, M. New economy and ICT development in China. Inf. Econ. Policy 14 , 275–295 (2002).

Chinn, M. D. & Fairlie, R. W. The determinants of the global digital divide: a cross-country analysis of computer and internet penetration. Oxf. Econ. Pap. 59 , 16–44 (2006).

Lembani, R., Gunter, A., Breines, M. & Dalu, M. T. B. The same course, different access: the digital divide between urban and rural distance education students in South Africa. J. Geogr. High. Educ. 44 , 70–84 (2020).

Asadullah, N., Bhattacharjee, A., Tasnim, M. & Mumtahena, F. COVID-19, schooling, and learning (BRAC Institute of Governance & Development, 2020); https://bigd.bracu.ac.bd/wp-content/uploads/2020/06/COVID-19-Schooling-and-Learning_June-25-2020.pdf

Asanov, I., Flores, F., McKenzie, D., Mensmann, M. & Schulte, M. Remote-learning, time-use, and mental health of Ecuadorian high-school students during the COVID-19 quarantine. World Dev. 138 , 105225 (2021).

Kihui, N. Kenya: 80% of students missing virtual learning amid school closures—study. AllAfrica (18 May 2020); https://allafrica.com/stories/202005180774.html

Debenedetti, L., Hirji, S., Chabi, M. O. & Swigart, T. Prioritizing evidence-based responses in Burkina Faso to mitigate the economic effects of COVID-19: lessons from RECOVR (Innovations for Poverty Action, 2020); https://www.poverty-action.org/blog/prioritizing-evidence-based-responses-burkina-faso-mitigate-economic-effects-covid-19-lessons

Bosumtwi-Sam, C. & Kabay, S. Using data and evidence to inform school reopening in Ghana (Innovations for Poverty Action, 2020); https://www.poverty-action.org/blog/using-data-and-evidence-inform-school-reopening-ghana

Azubuike, O. B., Adegboye, O. & Quadri, H. Who gets to learn in a pandemic? Exploring the digital divide in remote learning during the COVID-19 pandemic in Nigeria. Int. J. Educ. Res. Open 2 , 100022 (2021).

Attewell, P. Comment: the first and second digital divides. Sociol. Educ. 74 , 252–259 (2001).

DiMaggio, P., Hargittai, E., Neuman, W. R. & Robinson, J. P. Social implications of the Internet. Annu. Rev. Sociol. 27 , 307–336 (2001).

Hargittai, E. Digital na(t)ives? Variation in Internet skills and uses among members of the ‘Net Generation’. Sociol. Inq. 80 , 92–113 (2010).

Iivari, N., Sharma, S. & Ventä-Olkkonen, L. Digital transformation of everyday life—how COVID-19 pandemic transformed the basic education of the young generation and why information management research should care? Int. J. Inform. Manag. 55 , 102183 (2020).

Wei, L. & Hindman, D. B. Does the digital divide matter more? Comparing the effects of new media and old media use on the education-based knowledge gap. Mass Commun. Soc. 14 , 216–235 (2011).

Octobre, S. & Berthomier, N. L’enfance des loisirs [The childhood of leisure]. Cult. Études 6 , 1–12 (2011).

Education at a glance 2015: OECD indicators (OECD, 2015); https://doi.org/10.1787/eag-2015-en

North, S., Snyder, I. & Bulfin, S. Digital tastes: social class and young people’s technology use. Inform. Commun. Soc. 11 , 895–911 (2008).

Robinson, L. & Schulz, J. Net time negotiations within the family. Inform. Commun. Soc. 16 , 542–560 (2013).

Bonfadelli, H. The Internet and knowledge gaps: a theoretical and empirical investigation. Eur. J. Commun. 17 , 65–84 (2002).

Drabowicz, T. Social theory of Internet use: corroboration or rejection among the digital natives? Correspondence analysis of adolescents in two societies. Comput. Educ. 105 , 57–67 (2017).

Nikken, P. & Jansz, J. Developing scales to measure parental mediation of young children’s Internet use. Learn. Media Technol. 39 , 250–266 (2014).

Danic, I., Fontar, B., Grimault-Leprince, A., Le Mentec, M. & David, O. Les espaces de construction des inégalités éducatives [The areas of construction of educational inequalities] (Presses Univ. de Rennes, 2019).

Goudeau, S. Comment l'école reproduit-elle les inégalités? [How does school reproduce inequalities?] (Univ. Grenoble Alpes Editions/Presses Univ. de Grenoble, 2020).

Bernstein, B. Class, Codes, and Control (Routledge, 1975).

Gaddis, S. M. The influence of habitus in the relationship between cultural capital and academic achievement. Soc. Sci. Res. 42 , 1–13 (2013).

Lamont, M. & Lareau, A. Cultural capital: allusions, gaps and glissandos in recent theoretical developments. Sociol. Theory 6 , 153–168 (1988).

Stephens, N. M., Markus, H. R. & Phillips, L. T. Social class culture cycles: how three gateway contexts shape selves and fuel inequality. Annu. Rev. Psychol. 65 , 611–634 (2014).

Lahire, B. Enfances de classe. De l’inégalité parmi les enfants [Social class childhood. Inequality among children] (Le Seuil, 2019).

Lareau, A. Unequal Childhoods: Class, Race, and Family Life (Univ. of California Press, 2003).

Bourdieu, P. La distinction. Critique sociale du jugement [Distinction: a social critique of the judgement of taste] (Éditions de Minuit, 1979).

Bradley, R. H., Corwyn, R. F., McAdoo, H. P. & Garcia Coll, C. The home environments of children in the United States part I: variations by age, ethnicity, and poverty status. Child Dev. 72 , 1844–1867 (2001).

Blevins‐Knabe, B. & Musun‐Miller, L. Number use at home by children and their parents and its relationship to early mathematical performance. Early Dev. Parent. 5 , 35–45 (1996).

LeFevre, J. A. et al. Pathays to mathematics: longitudinal predictors of performance. Child Dev. 81 , 1753–1767 (2010).

Lareau, A. Home Advantage. Social Class and Parental Intervention in Elementary Education (Falmer Press, 1989).

Guryan, J., Hurst, E. & Kearney, M. Parental education and parental time with children. J. Econ. Perspect. 22 , 23–46 (2008).

Hill, C. R. & Stafford, F. P. Allocation of time to preschool children and educational opportunity. J. Hum. Resour. 9 , 323–341 (1974).

Calarco, J. M. A Field Guide to Grad School: Uncovering the Hidden Curriculum (Princeton Univ. Press, 2020).

Daw, J. Parental income and the fruits of labor: variability in homework efficacy in secondary school. Res. Soc. Strat. Mobil. 30 , 246–264 (2012).

Rønning, M. Who benefits from homework assignments? Econ. Educ. Rev. 30 , 55–64 (2011).

Alexander, K. L., Entwisle, D. R. & Olson, L. S. Lasting consequences of the summer learning gap. Am. Sociol. Rev. 72 , 167–180 (2007).

Cooper, H., Nye, B., Charlton, K., Lindsay, J. & Greathouse, S. The effects of summer vacation on achievement test scores: a narrative and meta-analytic review. Rev. Educ. Res. 66 , 227–268 (1996).

Stewart, H., Watson, N. & Campbell, M. The cost of school holidays for children from low income families. Childhood 25 , 516–529 (2018).

Pensiero, N., Kelly, A. & Bokhove, C. Learning inequalities during the Covid-19 pandemic: how families cope with home-schooling (University of Southampton, 2020); https://doi.org/10.5258/SOTON/P0025

Stephens, N. M., Markus, H. R. & Townsend, S. S. Choice as an act of meaning: the case of social class. J. Pers. Soc. Psychol. 93 , 814–830 (2007).

Kraus, M. W., Piff, P. K. & Keltner, D. Social class, sense of control, and social explanation. J. Pers. Soc. Psychol. 97 , 992–1004 (2009).

Dittmann, A. G., Stephens, N. M. & Townsend, S. S. Achievement is not class-neutral: working together benefits pople from working-class contexts. J. Pers. Soc. Psychol. 119 , 517–539 (2020).

Zimmerman, B. J. Investigating self-regulation and motivation: historical background, methodological developments, and future prospects. Am. Educ. Res. J. 45 , 166–183 (2008).

Backer-Grøndahl, A., Nærde, A., Ulleberg, P. & Janson, H. Measuring effortful control using the children’s behavior questionnaire–very short form: modeling matters. J. Pers. Assess. 98 , 100–109 (2016).

Johnson, S. E., Richeson, J. A. & Finkel, E. J. Middle class and marginal? Socioeconomic status, stigma, and self-regulation at an elite university. J. Pers. Soc. Psychol. 100 , 838–852 (2011).

Størksen, I., Ellingsen, I. T., Wanless, S. B. & McClelland, M. M. The influence of parental socioeconomic background and gender on self-regulation among 5-year-old children in Norway. Early Educ. Dev. 26 , 663–684 (2015).

Brady, L. et al. 7 ways for teachers to truly connect with parents. Education Week (31 December 2020); https://www.edweek.org/leadership/opinion-7-ways-for-teachers-to-truly-connect-with-parents/2020/12

Montacute, R. Social mobility and Covid-19: implications of the Covid-19 crisis for educational inequality (Sutton Trust, 2020); https://dera.ioe.ac.uk/35323/2/COVID-19-and-Social-Mobility-1.pdf

Dorn, E., Hancock, B., Sarakatsannis, J. & Viruleg, E. COVID-19 and student learning in the United States: the hurt could last a lifetime (McKinsey & Company, 2020); https://www.mckinsey.com/industries/public-and-social-sector/our-insights/covid-19-and-student-learning-in-the-united-states-the-hurt-could-last-a-lifetime

Parolin, Z. & Lee, E. K. Large socio-economic, geographic and demographic disparities exist in exposure to school closures. Nat. Hum. Behav. 5 , 522–528 (2021).

Saavedra, J. A silent and unequal education crisis. And the seeds for its solution (World Bank, 2021); https://blogs.worldbank.org/education/silent-and-unequal-education-crisis-and-seeds-its-solution

Murray, B., Domina, T., Renzulli, L. & Boylan, R. Civil society goes to school: parent–teacher associations and the equality of educational opportunity. Russell Sage Found. J. Soc. Sci. 5 , 41–63 (2019).

Calarco, J. M. Avoiding us versus them: how schools’ dependence on privileged ‘helicopter’ parents influences enforcement of rules. Am. Sociol. Rev. 85 , 223–246 (2020).

Rist, R. Student social class and teacher expectations: the self-fulfilling prophecy in ghetto education. Harv. Educ. Rev. 40 , 411–451 (1970).

Tobisch, A. & Dresel, M. Negatively or positively biased? Dependencies of teachers’ judgments and expectations based on students’ ethnic and social backgrounds. Soc. Psychol. Educ. 20 , 731–752 (2017).

Brantlinger, E. Dividing Classes: How the Middle-class Negotiates and Rationalizes School Advantage (Routledge, 2003).

Calarco, J. M. ‘I need help!’ Social class and children’s help-seeking in elementary school. Am. Sociol. Rev. 76 , 862–882 (2011).

Calarco, J. M. The inconsistent curriculum: cultural tool kits and student interpretations of ambiguous expectations. Soc. Psychol. Quart. 77 , 185–209 (2014).

Goudeau, S. & Cimpian, A. How do young children explain differences in the classroom? Implications for achievement, motivation, and educational equity. Perspect. Psychol. Sci. 16 , 533–552 (2021).

Croizet, J. C., Goudeau, S., Marot, M. & Millet, M. How do educational contexts contribute to the social class achievement gap: documenting symbolic violence from a social psychological point of view. Curr. Opin. Psychol. 18 , 105–110 (2017).

Goudeau, S. & Croizet, J.-C. Hidden advantages and disadvantages of social class: how classroom settings reproduce social inequality by staging unfair comparison. Psychol. Sci. 28 , 162–170 (2017).

Kudrna, L., Furnham, A. & Swami, V. The influence of social class salience on self-assessed intelligence. Soc. Behav. Personal. 38 , 859–864 (2010).

Wiederkehr, V., Darnon, C., Chazal, S., Guimond, S. & Martinot, D. From social class to self-efficacy: internalization of low social status pupils’ school performance. Soc. Psychol. Educ. 18 , 769–784 (2015).

Jury, M., Smeding, A., Court, M. & Darnon, C. When first-generation students succeed at university: on the link between social class, academic performance, and performance-avoidance goals. Contemp. Educ. Psychol. 41 , 25–36 (2015).

Jury, M., Quiamzade, A., Darnon, C. & Mugny, G. Higher and lower status individuals’ performance goals: the role of hierarchy stability. Motiv. Sci. 5 , 52–65 (2019).

Autin, F. & Croizet, J.-C. Improving working memory efficiency by reframing metacognitive interpretation of task difficulty. J. Exp. Psychol. Gen. 141 , 610–618 (2012).

Schmader, T., Johns, M. & Forbes, C. An integrated process model of stereotype threat effects on performance. Psychol. Rev. 115 , 336–356 (2008).

Usher, E. L. & Pajares, F. Self-efficacy for self-regulated learning: a validation study. Educ. Psychol. Meas. 68 , 443–463 (2008).

Bruno, A., Jury, M., Toczek-Capelle, M.-C. & Darnon, C. Are performance-avoidance goals always deleterious for academic achievement in college? The moderating role of social class. Soc. Psychol. Educ. 22 , 539–555 (2019).

Holloway, S. D. et al. Parenting self-efficacy and parental involvement: mediators or moderators between socioeconomic status and children’s academic competence in Japan and Korea? Res. Hum. Dev. 13 , 258–272 (2016).

Tazouti, Y. & Jarlégan, A. The mediating effects of parental self-efficacy and parental involvement on the link between family socioeconomic status and children’s academic achievement. J. Fam. Stud. 25 , 250–266 (2019).

Andreu, S. et al. Évaluations 2020, repères CP, CE1: premiers résultats [2020 assessments, first and second grades benchmarks: first results] (Ministère de l’Éducation nationale, de la Jeunesse et des Sports, 2020); https://www.education.gouv.fr/evaluations-2020-reperes-cp-ce1-premiers-resultats-307122

Engzell, P., Frey, A. & Verhagen, M. D. Learning loss due to school closures during the COVID-19 pandemic. Proc. Natl Acad. Sci. USA 118 , e2022376118 (2021).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Maldonado, J. E. & De Witte, K. The effect of school closures on standardized student test outcomes (KU Leuven—Faculty of Economics and Business, 2020); https://limo.libis.be/primo-explore/fulldisplay?docid=LIRIAS3189074&context=L&vid=Lirias&search_scope=Lirias&tab=default_tab&lang=en_US

Domingue, B., Hough, H. J., Lang, D. & Yeatman, J. Changing patterns of growth in oral reading fluency during the COVID-19 pandemic (PACE, 2021); https://edpolicyinca.org/publications/changing-patterns-growth-oral-reading-fluency-during-covid-19-pandemic

Dorn, E., Hancock, B., Sarakatsannis, J. & Viruleg, E. COVID-19 and learning loss—disparities grow and students need help (McKinsey & Company, 2020); https://www.mckinsey.com/industries/public-and-social-sector/our-insights/covid-19-and-learning-loss-disparities-grow-and-students-need-help

Angrist, N. et al. Building back better to avert a learning catastrophe: estimating learning loss from COVID-19 school shutdowns in Africa and facilitating short-term and long-term learning recovery. Int. J. Educ. Dev. 84 , 102397 (2021).

Reddy, V., Soudien, C. & Winnaar, L. Disrupted learning during COVID-19: the impact of school closures on education outcomes in South Africa (The Conversation, 2020); https://theconversation.com/impact-of-school-closures-on-education-outcomes-in-south-africa-136889

Entwisle, D. R. & Alexander, K. L. Summer setback: race, poverty, school composition, and mathematics achievement in the first two years of school. Am. Sociol. Rev. 57 , 72–84 (1992).

Kieffer, M. J. Catching up or falling behind? Initial English proficiency, concentrated poverty, and the reading growth of language minority learners in the United States. J. Educ. Psychol. 100 , 851–868 (2008).

Calarco, J. M., Horn, I. & Chen, G. A. ‘You need to be more responsible’: how math homework operates as a status-reinforcing process in school. Preprint at SocArXiv https://doi.org/10.31235/osf.io/xf96q (2020).

Kaiper-Marquez, A. et al. On the fly: adapting quickly to emergency remote instruction in a family literacy program. Int. Rev. Educ. 66 , 1–23 (2020).

Barton, D. C. Impacts of the COVID‐19 pandemic on field instruction and remote teaching alternatives: results from a survey of instructors. Ecol. Evol. 10 , 12499–12507 (2020).

Article   PubMed Central   Google Scholar  

IJzerman, H. et al. Use caution when applying behavioural science to policy. Nat. Hum. Behav. 4 , 1092–1094 (2020).

Taylor, J. & Mallery, J. In person and online learning go together (Stanford Institute for Economic Policy Research, 2020); https://siepr.stanford.edu/research/publications/person-and-online-learning-go-together

Dietrichson, J., Bøg, M., Filges, T. & Klint Jørgensen, A. M. Academic interventions for elementary and middle school students with low socioeconomic status: a systematic review and meta-analysis. Rev. Educ. Res. 87 , 243–282 (2017).

Núñez, J. C. et al. Teachers’ feedback on homework, homework-related behaviors, and academic achievement. J. Educ. Res. 108 , 204–216 (2015).

Singh, R. et al. In Artificial Intelligence in Education (eds Biswas, G.et al.) 328–336 (Springer Berlin Heidelberg, 2011).

Harackiewicz, J. M., Rozek, C. S., Hulleman, C. S. & Hyde, J. S. Helping parents to motivate adolescents in mathematics and science: an experimental test of a utility-value intervention. Psychol. Sci. 23 , 899–906 (2012).

Jeynes, W. A meta-analysis of the efficacy of different types of parental involvement programs for urban students. Urban Educ. 47 , 706–742 (2012).

Mol, S. E., Bus, A. G., De Jong, M. T. & Smeets, D. J. Added value of dialogic parent–child book readings: a meta-analysis. Early Educ. Dev. 19 , 7–26 (2008).

Angrist, N., Bergman, P. & Matsheng, M. School’s out: experimental evidence on limiting learning loss using “low-tech” in a pandemic (National Bureau of Economic Research, 2021); https://www.nber.org/papers/w28205

Carlana, M. & La Ferrara, E. Apart but connected: online tutoring and student outcomes during the COVID-19 pandemic (Institute of Labor Economics, 2021); http://hdl.handle.net/10419/232846

Pagan, S. & Sénéchal, M. Involving parents in a summer book reading program to promote reading comprehension, fluency, and vocabulary in grade 3 and grade 5 children. Can. J. Educ. 37 , 1–31 (2014).

Sénéchal, M. & LeFevre, J. A. Parental involvement in the development of children’s reading skill: a five‐year longitudinal study. Child Dev. 73 , 445–460 (2002).

Starkey, P. & Klein, A. Fostering parental support for children’s mathematical development: an intervention with Head Start families. Early Educ. Dev. 11 , 659–680 (2000).

Buheji, M. et al. The extent of Covid-19 pandemic socio-economic impact on global poverty: a global integrative multidisciplinary review. Am. J. Econ. 10 , 213–224 (2020).

The world economy on a tightrope (OECD, 2020); http://www.oecd.org/economic-outlook/june-2020/

Martin, A., Markhvida, M., Hallegatte, S. & Walsh, B. Socio-economic impacts of COVID-19 on household consumption and poverty. Econ. Disasters Clim. Change 4 , 453–479 (2020).

Jetten, J., Mols, F. & Selvanathan, H. P. How economic inequality fuels the rise and persistence of the Yellow Vest movement. Int. Rev. Soc. Psychol. 33 , 2 (2020).

Wilkinson, R. G. & Pickett, K. E. Income inequality and social dysfunction. Annu. Rev. Sociol. 35 , 493–511 (2009).

Sommet, N., Morselli, D. & Spini, D. Income inequality affects the psychological health of only the people facing scarcity. Psychol. Sci. 29 , 1911–1921 (2018).

Hattie, J. Visible Learning: A Synthesis of over 800 Meta-analyses Relating to Achievement (Routledge, 2008).

Cooper, H., Charlton, K., Valentine, J. C., Muhlenbruck, L. & Borman, G. D. Making the most of summer school: a meta-analytic and narrative review. Monogr. Soc. Res. Child 65 , 1–127 (2000).

Heyns, B. Schooling and cognitive development: is there a season for learning? Child Dev. 58 , 1151–1160 (1987).

McCombs, J. S., Augustine, C. H. & Schwartz, H. L. Making Summer Count: How Summer Programs can Boost Children’s Learning (Rand Education, 2011).

Borman, G. D. & Dowling, N. M. Longitudinal achievement effects of multiyear summer school: evidence from the teach Baltimore randomized field trial. Educ. Eval. Policy 28 , 25–48 (2006).

Kim, J. S. & Quinn, D. M. The effects of summer reading on low-income children’s literacy achievement from kindergarten to grade 8: a meta-analysis of classroom and home interventions. Rev. Educ. Res. 83 , 386–431 (2013).

Download references

Acknowledgements

We thank G. Reis for editing the figure. The writing of this manuscript was supported by grant ANR-19-CE28-0007–PRESCHOOL from the French National Research Agency (S.G.).

Author information

Authors and affiliations.

Université de Poitiers, CNRS, CeRCA, Centre de Recherches sur la Cognition et l’Apprentissage, Poitiers, France

Sébastien Goudeau & Camille Sanrey

Université Clermont Auvergne, CNRS, LAPSCO, Laboratoire de Psychologie Sociale et Cognitive, Clermont-Ferrand, France

Arnaud Stanczak & Céline Darnon

School of Psychology, Cardiff University, Cardiff, UK

Antony Manstead

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Sébastien Goudeau .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Peer review information Nature Human Behaviour thanks Daniele Checchi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Cite this article.

Goudeau, S., Sanrey, C., Stanczak, A. et al. Why lockdown and distance learning during the COVID-19 pandemic are likely to increase the social class achievement gap. Nat Hum Behav 5 , 1273–1281 (2021). https://doi.org/10.1038/s41562-021-01212-7

Download citation

Received : 15 March 2021

Accepted : 06 September 2021

Published : 27 September 2021

Issue Date : October 2021

DOI : https://doi.org/10.1038/s41562-021-01212-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Socioeconomic inequalities in psychosocial well-being among adolescents under the covid-19 pandemic: a cross-regional comparative analysis in hong kong, mainland china, and the netherlands.

• Gary Ka-Ki Chung
• Xiaoting Liu
• Roger Yat-Nork Chung

Social Psychiatry and Psychiatric Epidemiology (2024)

Digital gender gaps in Students’ knowledge, attitudes and skills: an integrative data analysis across 32 Countries

• Diego G. Campos
• Ronny Scherer

Education and Information Technologies (2024)

Microlearning as a Concept to Optimize Integrated Services for Racially/Ethnically Diverse Families of Autistic Children

• Zhiwen Xiao
• Sandra Vanegas

Journal of Developmental and Physical Disabilities (2024)

The ethicality of the COVID-19 response in children and adolescents

• Fiona McNicholas

Irish Journal of Medical Science (1971 -) (2024)

Health-related quality of life of young refugees in Germany during the COVID-19 pandemic: comparisons to non-refugees and pre-pandemic times

• Johanna Braig
• Pia Schmees
• Heike Eschenbeck

Current Psychology (2024)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

March 13, 2020

Online Learning during the COVID-19 Pandemic

What do we gain and what do we lose when classrooms go virtual?

By Yoshiko Iwai

Watchara Piriaputtanapun Getty Images

This article was published in Scientific American’s former blog network and reflects the views of the author, not necessarily those of Scientific American

I woke up an hour late Wednesday morning, and by the time I had thrown on a sweatshirt, prepared my glass of Emergen-C, and logged onto Zoom , my class had been going on for 15 minutes. The night before I had taken cough syrup for my seasonal cold, and this was the first day my school switched to virtual instruction. Over the course of the three-hour workshop, I noticed my puffy eyes on the panel of faces and became self-conscious. I turned off my video. I became distracted with the noise of sirens outside and muted my speaker, only to then realize: by the time you’re done muting-and-unmuting, the right moment to join the conversation has already passed. I found myself texting on my computer, stepping away to make coffee, running to the bathroom, writing a couple e-mails, and staring at my classmate’s dog in one of the video panels. I don’t think my experience is unique; I imagined similar situations playing out in virtual offices and classrooms across the world.

In the aftermath of the World Health Organization’s designation of the novel coronavirus as a pandemic on March 11, universities across America are shutting down in an attempt to slow its spread. On March 6, the University of Washington took the lead , canceling all in-person classes, with a wave of universities across the country following suit: University of California, Berkeley, U.C., San Diego, Stanford, Rice, Harvard, Columbia, Barnard, N.Y.U, Princeton and Duke, among many others .

This shift into virtual classrooms is the culmination of the past weeks’ efforts to prevent COVID-19 from entering university populations and spreading to local communities: cancellation of university-funded international travel for conferences , blanket bans on any international travel for spring break, canceling study-abroad programs, creating registration systems for any domestic travel.

On supporting science journalism

If you're enjoying this article, consider supporting our award-winning journalism by subscribing . By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today.

Columbia University, which I now attend virtually, moved all classes online starting on March 11. The following morning, president Bollinger declared that classes would be held virtually for the remainder of the school year, and suspended all university-related travel; both international and domestic. The pandemic has affected over 114 countries, killing over 4,000 and shows no sign of abating, leading to chaos in university administration and among students. I find myself obsessing over my family in Japan, especially my mother, whose lung cancer puts her at particular risk. Cancellations are affecting future students as well—admitted students’ events, open houses, and campus tours are all being canceled to minimize contagion.

The quick turn to platforms like Zoom is disrupting curricula, particularly for professors less equipped to navigate the internet and the particularities of managing a classroom mediated by a screen and microphone. I had professors cancel class because they had technical difficulties, trouble with WiFi, or were simply panicked over the prospect of teaching the full class over the new platform. With university IT services focusing efforts on providing professors with how-to webinars on using online platforms, individual student needs for these same services have been placed on hold.

While the initial shift online has created a flurry of chaos, there are benefits to a virtual classroom. Especially in a place like New York, students can continue participating in discussion sections and lectures without riding the subway for an hour, avoiding the anxiety of using public transit or being in other incubators like classrooms, public bathrooms and cafeterias. Students can “sit in” on a class while nursing a common cold or allergies that come with the season, but which can make students a target of serious threats or violence—particularly racialized harassment for Asians. I have found immense relief in not having to pay for Lyfts to campus, avoiding side-eyes for my runny nose or using the little remaining hand sanitizer I have left after holding subway poles. In some situations, online teaching may not even affect student behavior or learning. Studies have shown that medical students learn and perform equally in live versus recorded lectures, and these results are reassuring at a time like the COVID-19 outbreak.

However, the reality is that some subjects are much harder to transfer online. A biochemistry or introductory economics lecture is easier to teach virtually than a music or dance class. The creation of a film or theatrical production requires physical bodies in close proximity. Even in my creative writing workshop, responding to a colleagues’ memoir about her mother’s death is hard to do without looking her in the eye. The screen creates an emotional remove that makes it difficult to have back-and-forth dialogue between multiple people, and it’s almost impossible to provide thoughtful feedback without feeling like you’re speaking into a void.

Over the last few decades, online learning in higher education has been studied extensively. Online MBA programs are on the rise, perhaps unsurprising for a field that often requires virtual conferencing and remote collaboration. Universities now offer online master’s programs to accommodate full-time work and long commutes, or to circumvent the financial barriers of moving to a new location with family. Online bachelor’s degrees are offered by a growing number of schools: Ohio State, University of Illinois Chicago, University of Florida, Arizona State, Penn State and many more. The benefits are the same: classes can be taken anywhere, lack of commute offers more time for studying or external commitments, and the structure is more welcoming to students with physical disability or illness. And yet, online learning hasn’t threatened the traditional model of in-person learning.

A large part of this can be attributed to accountability . Online classes require significantly more motivation and attention. I found it difficult to focus on a pixelated video screen when I could browse the internet on my computer, text on my phone, watch TV in the background, have one hand in the pantry, or just lay comfortably in my bed. The problem, too, is that webinar technology doesn’t quite live up to the hype. Noise and feedback—rustling papers, ambulances, kettles, wind—make it impossible to hear people talk, and so everyone is asked to mute their microphones.

But muting your audio means you can’t jump into a conversation quickly. The “raise hand” function often goes unnoticed by teachers and the chat box is distracting. Sometimes the gallery view just doesn’t work, so you’re stuck staring at your own face or just two of your eighteen classmates. It also means another hurdle for those who hesitate to speak up, even in the best of circumstances. It means you’re just one click away from turning off your camera and being totally off the hook. In an online class over the summer, I once watched a woman—who forgot her camera was still on, though she was muted—vacuum her entire kitchen and living room during a seminar.

In a recent New York Times article, columnist Kevin Roose wrote about his experience working from home while quarantined after COVID-19 exposure. Roose, once a remote worker, cites studies that suggest remote employees are more productive , taking shorter breaks and fewer sick days. But he also writes extensively about the isolation and lack of productivity he feels: “I’ve realized that I can’t be my best, most human self in sweatpants, pretending to pay attention on video conferences between trips to the fridge.” He notes that Steve Jobs, who was a firm believer in in-person collaboration and opposed remote work, once said, “Creativity comes from spontaneous meetings, from random discussions. You run into someone, you ask what they’re doing, you say ‘Wow,’ and soon you’re cooking up all sorts of ideas.”

In educational settings, creativity is arguably one of the most important things at stake. The surprises and unexpected interactions fuel creativity—often a result of sitting in a room brushing shoulders with a classmate, running into professors in a bathroom line, or landing on ideas and insights that arise out of discomfort in the room. This unpredictability is often lost online.

In the essay “Sim Life,” from her book, Make It Scream, Make it Burn , Leslie Jamison writes about the shortcomings of virtual life: “So much of lived experience is composed of what lies beyond our agency and prediction, beyond our grasp, in missteps and unforeseen obstacles and the textures of imperfection: the grit and grain of a sidewalk with its cigarette butts and faint summer stench of garbage and taxi exhaust, the possibility of a rat scuttling from a pile of trash bags, the lilt and laughter of nearby strangers’ voices.”

Classrooms offer these opportunities for riffs and surprise, and a large part of being a student is learning to deliver critique through uncomfortable eye contact, or negotiating a room full of voices and opinions that create friction with your own. When I Zoomed into class from my apartment, I missed being interrupted by classmates who complicated my ideas about a poem or short story. I missed being in workshop and bouncing ideas off of each other to find the best structure for a piece. I missed handwritten critiques, and felt limited in Word: no check pluses, no smiley faces, “Wow” feels flat when it’s not handwritten in the margins, and "Great" feels sarcastic in 10-point Calibri. I was frustrated that I could sleep in because online class meant I could wake up five minutes before class and pretend like I’d been ready all morning.

The COVID-19 pandemic will likely continue presenting challenges beyond those that come up in the course of routine virtual education. Even if this viral spread subsides, or a vaccination becomes readily available, the shift from online classes back to in-person learning may create disruptions of its own—adjusting back to higher standards of accountability, weaning off of phone-checking habits, and transferring comments back to hard copies instead of digital notes. Hopefully, these phases of trouble shooting can provide universities, professors and students the opportunity to practice adaptability, patience and resilience. And hopefully, these experiences will serve as preparation for future challenges that come with the next epidemic, pandemic and other disaster.

For now, I am trying to not look at myself in the gallery of faces, stop being distracted by my expressions, resisting the impulses to check my phone or e-mail, or at least recognize these urges when they arise.

Read more about the coronavirus outbreak  here .

Advertisement

Students’ online learning challenges during the pandemic and how they cope with them: The case of the Philippines

• Published: 28 May 2021
• Volume 26 , pages 7321–7338, ( 2021 )

Cite this article

• Jessie S. Barrot   ORCID: orcid.org/0000-0001-8517-4058 1 ,
• Ian I. Llenares 1 &
• Leo S. del Rosario 1  

934k Accesses

234 Citations

4 Altmetric

Explore all metrics

Recently, the education system has faced an unprecedented health crisis that has shaken up its foundation. Given today’s uncertainties, it is vital to gain a nuanced understanding of students’ online learning experience in times of the COVID-19 pandemic. Although many studies have investigated this area, limited information is available regarding the challenges and the specific strategies that students employ to overcome them. Thus, this study attempts to fill in the void. Using a mixed-methods approach, the findings revealed that the online learning challenges of college students varied in terms of type and extent. Their greatest challenge was linked to their learning environment at home, while their least challenge was technological literacy and competency. The findings further revealed that the COVID-19 pandemic had the greatest impact on the quality of the learning experience and students’ mental health. In terms of strategies employed by students, the most frequently used were resource management and utilization, help-seeking, technical aptitude enhancement, time management, and learning environment control. Implications for classroom practice, policy-making, and future research are discussed.

Avoid common mistakes on your manuscript.

1 Introduction

Since the 1990s, the world has seen significant changes in the landscape of education as a result of the ever-expanding influence of technology. One such development is the adoption of online learning across different learning contexts, whether formal or informal, academic and non-academic, and residential or remotely. We began to witness schools, teachers, and students increasingly adopt e-learning technologies that allow teachers to deliver instruction interactively, share resources seamlessly, and facilitate student collaboration and interaction (Elaish et al., 2019 ; Garcia et al., 2018 ). Although the efficacy of online learning has long been acknowledged by the education community (Barrot, 2020 , 2021 ; Cavanaugh et al., 2009 ; Kebritchi et al., 2017 ; Tallent-Runnels et al., 2006 ; Wallace, 2003 ), evidence on the challenges in its implementation continues to build up (e.g., Boelens et al., 2017 ; Rasheed et al., 2020 ).

Recently, the education system has faced an unprecedented health crisis (i.e., COVID-19 pandemic) that has shaken up its foundation. Thus, various governments across the globe have launched a crisis response to mitigate the adverse impact of the pandemic on education. This response includes, but is not limited to, curriculum revisions, provision for technological resources and infrastructure, shifts in the academic calendar, and policies on instructional delivery and assessment. Inevitably, these developments compelled educational institutions to migrate to full online learning until face-to-face instruction is allowed. The current circumstance is unique as it could aggravate the challenges experienced during online learning due to restrictions in movement and health protocols (Gonzales et al., 2020 ; Kapasia et al., 2020 ). Given today’s uncertainties, it is vital to gain a nuanced understanding of students’ online learning experience in times of the COVID-19 pandemic. To date, many studies have investigated this area with a focus on students’ mental health (Copeland et al., 2021 ; Fawaz et al., 2021 ), home learning (Suryaman et al., 2020 ), self-regulation (Carter et al., 2020 ), virtual learning environment (Almaiah et al., 2020 ; Hew et al., 2020 ; Tang et al., 2020 ), and students’ overall learning experience (e.g., Adarkwah, 2021 ; Day et al., 2021 ; Khalil et al., 2020 ; Singh et al., 2020 ). There are two key differences that set the current study apart from the previous studies. First, it sheds light on the direct impact of the pandemic on the challenges that students experience in an online learning space. Second, the current study explores students’ coping strategies in this new learning setup. Addressing these areas would shed light on the extent of challenges that students experience in a full online learning space, particularly within the context of the pandemic. Meanwhile, our nuanced understanding of the strategies that students use to overcome their challenges would provide relevant information to school administrators and teachers to better support the online learning needs of students. This information would also be critical in revisiting the typology of strategies in an online learning environment.

2 Literature review

2.1 education and the covid-19 pandemic.

In December 2019, an outbreak of a novel coronavirus, known as COVID-19, occurred in China and has spread rapidly across the globe within a few months. COVID-19 is an infectious disease caused by a new strain of coronavirus that attacks the respiratory system (World Health Organization, 2020 ). As of January 2021, COVID-19 has infected 94 million people and has caused 2 million deaths in 191 countries and territories (John Hopkins University, 2021 ). This pandemic has created a massive disruption of the educational systems, affecting over 1.5 billion students. It has forced the government to cancel national examinations and the schools to temporarily close, cease face-to-face instruction, and strictly observe physical distancing. These events have sparked the digital transformation of higher education and challenged its ability to respond promptly and effectively. Schools adopted relevant technologies, prepared learning and staff resources, set systems and infrastructure, established new teaching protocols, and adjusted their curricula. However, the transition was smooth for some schools but rough for others, particularly those from developing countries with limited infrastructure (Pham & Nguyen, 2020 ; Simbulan, 2020 ).

Inevitably, schools and other learning spaces were forced to migrate to full online learning as the world continues the battle to control the vicious spread of the virus. Online learning refers to a learning environment that uses the Internet and other technological devices and tools for synchronous and asynchronous instructional delivery and management of academic programs (Usher & Barak, 2020 ; Huang, 2019 ). Synchronous online learning involves real-time interactions between the teacher and the students, while asynchronous online learning occurs without a strict schedule for different students (Singh & Thurman, 2019 ). Within the context of the COVID-19 pandemic, online learning has taken the status of interim remote teaching that serves as a response to an exigency. However, the migration to a new learning space has faced several major concerns relating to policy, pedagogy, logistics, socioeconomic factors, technology, and psychosocial factors (Donitsa-Schmidt & Ramot, 2020 ; Khalil et al., 2020 ; Varea & González-Calvo, 2020 ). With reference to policies, government education agencies and schools scrambled to create fool-proof policies on governance structure, teacher management, and student management. Teachers, who were used to conventional teaching delivery, were also obliged to embrace technology despite their lack of technological literacy. To address this problem, online learning webinars and peer support systems were launched. On the part of the students, dropout rates increased due to economic, psychological, and academic reasons. Academically, although it is virtually possible for students to learn anything online, learning may perhaps be less than optimal, especially in courses that require face-to-face contact and direct interactions (Franchi, 2020 ).

2.2 Related studies

Recently, there has been an explosion of studies relating to the new normal in education. While many focused on national policies, professional development, and curriculum, others zeroed in on the specific learning experience of students during the pandemic. Among these are Copeland et al. ( 2021 ) and Fawaz et al. ( 2021 ) who examined the impact of COVID-19 on college students’ mental health and their coping mechanisms. Copeland et al. ( 2021 ) reported that the pandemic adversely affected students’ behavioral and emotional functioning, particularly attention and externalizing problems (i.e., mood and wellness behavior), which were caused by isolation, economic/health effects, and uncertainties. In Fawaz et al.’s ( 2021 ) study, students raised their concerns on learning and evaluation methods, overwhelming task load, technical difficulties, and confinement. To cope with these problems, students actively dealt with the situation by seeking help from their teachers and relatives and engaging in recreational activities. These active-oriented coping mechanisms of students were aligned with Carter et al.’s ( 2020 ), who explored students’ self-regulation strategies.

In another study, Tang et al. ( 2020 ) examined the efficacy of different online teaching modes among engineering students. Using a questionnaire, the results revealed that students were dissatisfied with online learning in general, particularly in the aspect of communication and question-and-answer modes. Nonetheless, the combined model of online teaching with flipped classrooms improved students’ attention, academic performance, and course evaluation. A parallel study was undertaken by Hew et al. ( 2020 ), who transformed conventional flipped classrooms into fully online flipped classes through a cloud-based video conferencing app. Their findings suggested that these two types of learning environments were equally effective. They also offered ways on how to effectively adopt videoconferencing-assisted online flipped classrooms. Unlike the two studies, Suryaman et al. ( 2020 ) looked into how learning occurred at home during the pandemic. Their findings showed that students faced many obstacles in a home learning environment, such as lack of mastery of technology, high Internet cost, and limited interaction/socialization between and among students. In a related study, Kapasia et al. ( 2020 ) investigated how lockdown impacts students’ learning performance. Their findings revealed that the lockdown made significant disruptions in students’ learning experience. The students also reported some challenges that they faced during their online classes. These include anxiety, depression, poor Internet service, and unfavorable home learning environment, which were aggravated when students are marginalized and from remote areas. Contrary to Kapasia et al.’s ( 2020 ) findings, Gonzales et al. ( 2020 ) found that confinement of students during the pandemic had significant positive effects on their performance. They attributed these results to students’ continuous use of learning strategies which, in turn, improved their learning efficiency.

Finally, there are those that focused on students’ overall online learning experience during the COVID-19 pandemic. One such study was that of Singh et al. ( 2020 ), who examined students’ experience during the COVID-19 pandemic using a quantitative descriptive approach. Their findings indicated that students appreciated the use of online learning during the pandemic. However, half of them believed that the traditional classroom setting was more effective than the online learning platform. Methodologically, the researchers acknowledge that the quantitative nature of their study restricts a deeper interpretation of the findings. Unlike the above study, Khalil et al. ( 2020 ) qualitatively explored the efficacy of synchronized online learning in a medical school in Saudi Arabia. The results indicated that students generally perceive synchronous online learning positively, particularly in terms of time management and efficacy. However, they also reported technical (internet connectivity and poor utility of tools), methodological (content delivery), and behavioral (individual personality) challenges. Their findings also highlighted the failure of the online learning environment to address the needs of courses that require hands-on practice despite efforts to adopt virtual laboratories. In a parallel study, Adarkwah ( 2021 ) examined students’ online learning experience during the pandemic using a narrative inquiry approach. The findings indicated that Ghanaian students considered online learning as ineffective due to several challenges that they encountered. Among these were lack of social interaction among students, poor communication, lack of ICT resources, and poor learning outcomes. More recently, Day et al. ( 2021 ) examined the immediate impact of COVID-19 on students’ learning experience. Evidence from six institutions across three countries revealed some positive experiences and pre-existing inequities. Among the reported challenges are lack of appropriate devices, poor learning space at home, stress among students, and lack of fieldwork and access to laboratories.

Although there are few studies that report the online learning challenges that higher education students experience during the pandemic, limited information is available regarding the specific strategies that they use to overcome them. It is in this context that the current study was undertaken. This mixed-methods study investigates students’ online learning experience in higher education. Specifically, the following research questions are addressed: (1) What is the extent of challenges that students experience in an online learning environment? (2) How did the COVID-19 pandemic impact the online learning challenges that students experience? (3) What strategies did students use to overcome the challenges?

2.3 Conceptual framework

The typology of challenges examined in this study is largely based on Rasheed et al.’s ( 2020 ) review of students’ experience in an online learning environment. These challenges are grouped into five general clusters, namely self-regulation (SRC), technological literacy and competency (TLCC), student isolation (SIC), technological sufficiency (TSC), and technological complexity (TCC) challenges (Rasheed et al., 2020 , p. 5). SRC refers to a set of behavior by which students exercise control over their emotions, actions, and thoughts to achieve learning objectives. TLCC relates to a set of challenges about students’ ability to effectively use technology for learning purposes. SIC relates to the emotional discomfort that students experience as a result of being lonely and secluded from their peers. TSC refers to a set of challenges that students experience when accessing available online technologies for learning. Finally, there is TCC which involves challenges that students experience when exposed to complex and over-sufficient technologies for online learning.

To extend Rasheed et al. ( 2020 ) categories and to cover other potential challenges during online classes, two more clusters were added, namely learning resource challenges (LRC) and learning environment challenges (LEC) (Buehler, 2004 ; Recker et al., 2004 ; Seplaki et al., 2014 ; Xue et al., 2020 ). LRC refers to a set of challenges that students face relating to their use of library resources and instructional materials, whereas LEC is a set of challenges that students experience related to the condition of their learning space that shapes their learning experiences, beliefs, and attitudes. Since learning environment at home and learning resources available to students has been reported to significantly impact the quality of learning and their achievement of learning outcomes (Drane et al., 2020 ; Suryaman et al., 2020 ), the inclusion of LRC and LEC would allow us to capture other important challenges that students experience during the pandemic, particularly those from developing regions. This comprehensive list would provide us a clearer and detailed picture of students’ experiences when engaged in online learning in an emergency. Given the restrictions in mobility at macro and micro levels during the pandemic, it is also expected that such conditions would aggravate these challenges. Therefore, this paper intends to understand these challenges from students’ perspectives since they are the ones that are ultimately impacted when the issue is about the learning experience. We also seek to explore areas that provide inconclusive findings, thereby setting the path for future research.

3 Material and methods

The present study adopted a descriptive, mixed-methods approach to address the research questions. This approach allowed the researchers to collect complex data about students’ experience in an online learning environment and to clearly understand the phenomena from their perspective.

3.1 Participants

This study involved 200 (66 male and 134 female) students from a private higher education institution in the Philippines. These participants were Psychology, Physical Education, and Sports Management majors whose ages ranged from 17 to 25 ( x̅  = 19.81; SD  = 1.80). The students have been engaged in online learning for at least two terms in both synchronous and asynchronous modes. The students belonged to low- and middle-income groups but were equipped with the basic online learning equipment (e.g., computer, headset, speakers) and computer skills necessary for their participation in online classes. Table 1 shows the primary and secondary platforms that students used during their online classes. The primary platforms are those that are formally adopted by teachers and students in a structured academic context, whereas the secondary platforms are those that are informally and spontaneously used by students and teachers for informal learning and to supplement instructional delivery. Note that almost all students identified MS Teams as their primary platform because it is the official learning management system of the university.

Informed consent was sought from the participants prior to their involvement. Before students signed the informed consent form, they were oriented about the objectives of the study and the extent of their involvement. They were also briefed about the confidentiality of information, their anonymity, and their right to refuse to participate in the investigation. Finally, the participants were informed that they would incur no additional cost from their participation.

3.2 Instrument and data collection

The data were collected using a retrospective self-report questionnaire and a focused group discussion (FGD). A self-report questionnaire was considered appropriate because the indicators relate to affective responses and attitude (Araujo et al., 2017 ; Barrot, 2016 ; Spector, 1994 ). Although the participants may tell more than what they know or do in a self-report survey (Matsumoto, 1994 ), this challenge was addressed by explaining to them in detail each of the indicators and using methodological triangulation through FGD. The questionnaire was divided into four sections: (1) participant’s personal information section, (2) the background information on the online learning environment, (3) the rating scale section for the online learning challenges, (4) the open-ended section. The personal information section asked about the students’ personal information (name, school, course, age, and sex), while the background information section explored the online learning mode and platforms (primary and secondary) used in class, and students’ length of engagement in online classes. The rating scale section contained 37 items that relate to SRC (6 items), TLCC (10 items), SIC (4 items), TSC (6 items), TCC (3 items), LRC (4 items), and LEC (4 items). The Likert scale uses six scores (i.e., 5– to a very great extent , 4– to a great extent , 3– to a moderate extent , 2– to some extent , 1– to a small extent , and 0 –not at all/negligible ) assigned to each of the 37 items. Finally, the open-ended questions asked about other challenges that students experienced, the impact of the pandemic on the intensity or extent of the challenges they experienced, and the strategies that the participants employed to overcome the eight different types of challenges during online learning. Two experienced educators and researchers reviewed the questionnaire for clarity, accuracy, and content and face validity. The piloting of the instrument revealed that the tool had good internal consistency (Cronbach’s α = 0.96).

The FGD protocol contains two major sections: the participants’ background information and the main questions. The background information section asked about the students’ names, age, courses being taken, online learning mode used in class. The items in the main questions section covered questions relating to the students’ overall attitude toward online learning during the pandemic, the reasons for the scores they assigned to each of the challenges they experienced, the impact of the pandemic on students’ challenges, and the strategies they employed to address the challenges. The same experts identified above validated the FGD protocol.

Both the questionnaire and the FGD were conducted online via Google survey and MS Teams, respectively. It took approximately 20 min to complete the questionnaire, while the FGD lasted for about 90 min. Students were allowed to ask for clarification and additional explanations relating to the questionnaire content, FGD, and procedure. Online surveys and interview were used because of the ongoing lockdown in the city. For the purpose of triangulation, 20 (10 from Psychology and 10 from Physical Education and Sports Management) randomly selected students were invited to participate in the FGD. Two separate FGDs were scheduled for each group and were facilitated by researcher 2 and researcher 3, respectively. The interviewers ensured that the participants were comfortable and open to talk freely during the FGD to avoid social desirability biases (Bergen & Labonté, 2020 ). These were done by informing the participants that there are no wrong responses and that their identity and responses would be handled with the utmost confidentiality. With the permission of the participants, the FGD was recorded to ensure that all relevant information was accurately captured for transcription and analysis.

3.3 Data analysis

To address the research questions, we used both quantitative and qualitative analyses. For the quantitative analysis, we entered all the data into an excel spreadsheet. Then, we computed the mean scores ( M ) and standard deviations ( SD ) to determine the level of challenges experienced by students during online learning. The mean score for each descriptor was interpreted using the following scheme: 4.18 to 5.00 ( to a very great extent ), 3.34 to 4.17 ( to a great extent ), 2.51 to 3.33 ( to a moderate extent ), 1.68 to 2.50 ( to some extent ), 0.84 to 1.67 ( to a small extent ), and 0 to 0.83 ( not at all/negligible ). The equal interval was adopted because it produces more reliable and valid information than other types of scales (Cicchetti et al., 2006 ).

For the qualitative data, we analyzed the students’ responses in the open-ended questions and the transcribed FGD using the predetermined categories in the conceptual framework. Specifically, we used multilevel coding in classifying the codes from the transcripts (Birks & Mills, 2011 ). To do this, we identified the relevant codes from the responses of the participants and categorized these codes based on the similarities or relatedness of their properties and dimensions. Then, we performed a constant comparative and progressive analysis of cases to allow the initially identified subcategories to emerge and take shape. To ensure the reliability of the analysis, two coders independently analyzed the qualitative data. Both coders familiarize themselves with the purpose, research questions, research method, and codes and coding scheme of the study. They also had a calibration session and discussed ways on how they could consistently analyze the qualitative data. Percent of agreement between the two coders was 86 percent. Any disagreements in the analysis were discussed by the coders until an agreement was achieved.

This study investigated students’ online learning experience in higher education within the context of the pandemic. Specifically, we identified the extent of challenges that students experienced, how the COVID-19 pandemic impacted their online learning experience, and the strategies that they used to confront these challenges.

4.1 The extent of students’ online learning challenges

Table 2 presents the mean scores and SD for the extent of challenges that students’ experienced during online learning. Overall, the students experienced the identified challenges to a moderate extent ( x̅  = 2.62, SD  = 1.03) with scores ranging from x̅  = 1.72 ( to some extent ) to x̅  = 3.58 ( to a great extent ). More specifically, the greatest challenge that students experienced was related to the learning environment ( x̅  = 3.49, SD  = 1.27), particularly on distractions at home, limitations in completing the requirements for certain subjects, and difficulties in selecting the learning areas and study schedule. It is, however, found that the least challenge was on technological literacy and competency ( x̅  = 2.10, SD  = 1.13), particularly on knowledge and training in the use of technology, technological intimidation, and resistance to learning technologies. Other areas that students experienced the least challenge are Internet access under TSC and procrastination under SRC. Nonetheless, nearly half of the students’ responses per indicator rated the challenges they experienced as moderate (14 of the 37 indicators), particularly in TCC ( x̅  = 2.51, SD  = 1.31), SIC ( x̅  = 2.77, SD  = 1.34), and LRC ( x̅  = 2.93, SD  = 1.31).

Out of 200 students, 181 responded to the question about other challenges that they experienced. Most of their responses were already covered by the seven predetermined categories, except for 18 responses related to physical discomfort ( N  = 5) and financial challenges ( N  = 13). For instance, S108 commented that “when it comes to eyes and head, my eyes and head get ache if the session of class was 3 h straight in front of my gadget.” In the same vein, S194 reported that “the long exposure to gadgets especially laptop, resulting in body pain & headaches.” With reference to physical financial challenges, S66 noted that “not all the time I have money to load”, while S121 claimed that “I don't know until when are we going to afford budgeting our money instead of buying essentials.”

4.2 Impact of the pandemic on students’ online learning challenges

Another objective of this study was to identify how COVID-19 influenced the online learning challenges that students experienced. As shown in Table 3 , most of the students’ responses were related to teaching and learning quality ( N  = 86) and anxiety and other mental health issues ( N  = 52). Regarding the adverse impact on teaching and learning quality, most of the comments relate to the lack of preparation for the transition to online platforms (e.g., S23, S64), limited infrastructure (e.g., S13, S65, S99, S117), and poor Internet service (e.g., S3, S9, S17, S41, S65, S99). For the anxiety and mental health issues, most students reported that the anxiety, boredom, sadness, and isolation they experienced had adversely impacted the way they learn (e.g., S11, S130), completing their tasks/activities (e.g., S56, S156), and their motivation to continue studying (e.g., S122, S192). The data also reveal that COVID-19 aggravated the financial difficulties experienced by some students ( N  = 16), consequently affecting their online learning experience. This financial impact mainly revolved around the lack of funding for their online classes as a result of their parents’ unemployment and the high cost of Internet data (e.g., S18, S113, S167). Meanwhile, few concerns were raised in relation to COVID-19’s impact on mobility ( N  = 7) and face-to-face interactions ( N  = 7). For instance, some commented that the lack of face-to-face interaction with her classmates had a detrimental effect on her learning (S46) and socialization skills (S36), while others reported that restrictions in mobility limited their learning experience (S78, S110). Very few comments were related to no effect ( N  = 4) and positive effect ( N  = 2). The above findings suggest the pandemic had additive adverse effects on students’ online learning experience.

4.3 Students’ strategies to overcome challenges in an online learning environment

The third objective of this study is to identify the strategies that students employed to overcome the different online learning challenges they experienced. Table 4 presents that the most commonly used strategies used by students were resource management and utilization ( N  = 181), help-seeking ( N  = 155), technical aptitude enhancement ( N  = 122), time management ( N  = 98), and learning environment control ( N  = 73). Not surprisingly, the top two strategies were also the most consistently used across different challenges. However, looking closely at each of the seven challenges, the frequency of using a particular strategy varies. For TSC and LRC, the most frequently used strategy was resource management and utilization ( N  = 52, N  = 89, respectively), whereas technical aptitude enhancement was the students’ most preferred strategy to address TLCC ( N  = 77) and TCC ( N  = 38). In the case of SRC, SIC, and LEC, the most frequently employed strategies were time management ( N  = 71), psychological support ( N  = 53), and learning environment control ( N  = 60). In terms of consistency, help-seeking appears to be the most consistent across the different challenges in an online learning environment. Table 4 further reveals that strategies used by students within a specific type of challenge vary.

5 Discussion and conclusions

The current study explores the challenges that students experienced in an online learning environment and how the pandemic impacted their online learning experience. The findings revealed that the online learning challenges of students varied in terms of type and extent. Their greatest challenge was linked to their learning environment at home, while their least challenge was technological literacy and competency. Based on the students’ responses, their challenges were also found to be aggravated by the pandemic, especially in terms of quality of learning experience, mental health, finances, interaction, and mobility. With reference to previous studies (i.e., Adarkwah, 2021 ; Copeland et al., 2021 ; Day et al., 2021 ; Fawaz et al., 2021 ; Kapasia et al., 2020 ; Khalil et al., 2020 ; Singh et al., 2020 ), the current study has complemented their findings on the pedagogical, logistical, socioeconomic, technological, and psychosocial online learning challenges that students experience within the context of the COVID-19 pandemic. Further, this study extended previous studies and our understanding of students’ online learning experience by identifying both the presence and extent of online learning challenges and by shedding light on the specific strategies they employed to overcome them.

Overall findings indicate that the extent of challenges and strategies varied from one student to another. Hence, they should be viewed as a consequence of interaction several many factors. Students’ responses suggest that their online learning challenges and strategies were mediated by the resources available to them, their interaction with their teachers and peers, and the school’s existing policies and guidelines for online learning. In the context of the pandemic, the imposed lockdowns and students’ socioeconomic condition aggravated the challenges that students experience.

While most studies revealed that technology use and competency were the most common challenges that students face during the online classes (see Rasheed et al., 2020 ), the case is a bit different in developing countries in times of pandemic. As the findings have shown, the learning environment is the greatest challenge that students needed to hurdle, particularly distractions at home (e.g., noise) and limitations in learning space and facilities. This data suggests that online learning challenges during the pandemic somehow vary from the typical challenges that students experience in a pre-pandemic online learning environment. One possible explanation for this result is that restriction in mobility may have aggravated this challenge since they could not go to the school or other learning spaces beyond the vicinity of their respective houses. As shown in the data, the imposition of lockdown restricted students’ learning experience (e.g., internship and laboratory experiments), limited their interaction with peers and teachers, caused depression, stress, and anxiety among students, and depleted the financial resources of those who belong to lower-income group. All of these adversely impacted students’ learning experience. This finding complemented earlier reports on the adverse impact of lockdown on students’ learning experience and the challenges posed by the home learning environment (e.g., Day et al., 2021 ; Kapasia et al., 2020 ). Nonetheless, further studies are required to validate the impact of restrictions on mobility on students’ online learning experience. The second reason that may explain the findings relates to students’ socioeconomic profile. Consistent with the findings of Adarkwah ( 2021 ) and Day et al. ( 2021 ), the current study reveals that the pandemic somehow exposed the many inequities in the educational systems within and across countries. In the case of a developing country, families from lower socioeconomic strata (as in the case of the students in this study) have limited learning space at home, access to quality Internet service, and online learning resources. This is the reason the learning environment and learning resources recorded the highest level of challenges. The socioeconomic profile of the students (i.e., low and middle-income group) is the same reason financial problems frequently surfaced from their responses. These students frequently linked the lack of financial resources to their access to the Internet, educational materials, and equipment necessary for online learning. Therefore, caution should be made when interpreting and extending the findings of this study to other contexts, particularly those from higher socioeconomic strata.

Among all the different online learning challenges, the students experienced the least challenge on technological literacy and competency. This is not surprising considering a plethora of research confirming Gen Z students’ (born since 1996) high technological and digital literacy (Barrot, 2018 ; Ng, 2012 ; Roblek et al., 2019 ). Regarding the impact of COVID-19 on students’ online learning experience, the findings reveal that teaching and learning quality and students’ mental health were the most affected. The anxiety that students experienced does not only come from the threats of COVID-19 itself but also from social and physical restrictions, unfamiliarity with new learning platforms, technical issues, and concerns about financial resources. These findings are consistent with that of Copeland et al. ( 2021 ) and Fawaz et al. ( 2021 ), who reported the adverse effects of the pandemic on students’ mental and emotional well-being. This data highlights the need to provide serious attention to the mediating effects of mental health, restrictions in mobility, and preparedness in delivering online learning.

Nonetheless, students employed a variety of strategies to overcome the challenges they faced during online learning. For instance, to address the home learning environment problems, students talked to their family (e.g., S12, S24), transferred to a quieter place (e.g., S7, S 26), studied at late night where all family members are sleeping already (e.g., S51), and consulted with their classmates and teachers (e.g., S3, S9, S156, S193). To overcome the challenges in learning resources, students used the Internet (e.g., S20, S27, S54, S91), joined Facebook groups that share free resources (e.g., S5), asked help from family members (e.g., S16), used resources available at home (e.g., S32), and consulted with the teachers (e.g., S124). The varying strategies of students confirmed earlier reports on the active orientation that students take when faced with academic- and non-academic-related issues in an online learning space (see Fawaz et al., 2021 ). The specific strategies that each student adopted may have been shaped by different factors surrounding him/her, such as available resources, student personality, family structure, relationship with peers and teacher, and aptitude. To expand this study, researchers may further investigate this area and explore how and why different factors shape their use of certain strategies.

Several implications can be drawn from the findings of this study. First, this study highlighted the importance of emergency response capability and readiness of higher education institutions in case another crisis strikes again. Critical areas that need utmost attention include (but not limited to) national and institutional policies, protocol and guidelines, technological infrastructure and resources, instructional delivery, staff development, potential inequalities, and collaboration among key stakeholders (i.e., parents, students, teachers, school leaders, industry, government education agencies, and community). Second, the findings have expanded our understanding of the different challenges that students might confront when we abruptly shift to full online learning, particularly those from countries with limited resources, poor Internet infrastructure, and poor home learning environment. Schools with a similar learning context could use the findings of this study in developing and enhancing their respective learning continuity plans to mitigate the adverse impact of the pandemic. This study would also provide students relevant information needed to reflect on the possible strategies that they may employ to overcome the challenges. These are critical information necessary for effective policymaking, decision-making, and future implementation of online learning. Third, teachers may find the results useful in providing proper interventions to address the reported challenges, particularly in the most critical areas. Finally, the findings provided us a nuanced understanding of the interdependence of learning tools, learners, and learning outcomes within an online learning environment; thus, giving us a multiperspective of hows and whys of a successful migration to full online learning.

Some limitations in this study need to be acknowledged and addressed in future studies. One limitation of this study is that it exclusively focused on students’ perspectives. Future studies may widen the sample by including all other actors taking part in the teaching–learning process. Researchers may go deeper by investigating teachers’ views and experience to have a complete view of the situation and how different elements interact between them or affect the others. Future studies may also identify some teacher-related factors that could influence students’ online learning experience. In the case of students, their age, sex, and degree programs may be examined in relation to the specific challenges and strategies they experience. Although the study involved a relatively large sample size, the participants were limited to college students from a Philippine university. To increase the robustness of the findings, future studies may expand the learning context to K-12 and several higher education institutions from different geographical regions. As a final note, this pandemic has undoubtedly reshaped and pushed the education system to its limits. However, this unprecedented event is the same thing that will make the education system stronger and survive future threats.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Adarkwah, M. A. (2021). “I’m not against online teaching, but what about us?”: ICT in Ghana post Covid-19. Education and Information Technologies, 26 (2), 1665–1685.

Article   Google Scholar  

Almaiah, M. A., Al-Khasawneh, A., & Althunibat, A. (2020). Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic. Education and Information Technologies, 25 , 5261–5280.

Araujo, T., Wonneberger, A., Neijens, P., & de Vreese, C. (2017). How much time do you spend online? Understanding and improving the accuracy of self-reported measures of Internet use. Communication Methods and Measures, 11 (3), 173–190.

Barrot, J. S. (2016). Using Facebook-based e-portfolio in ESL writing classrooms: Impact and challenges. Language, Culture and Curriculum, 29 (3), 286–301.

Barrot, J. S. (2018). Facebook as a learning environment for language teaching and learning: A critical analysis of the literature from 2010 to 2017. Journal of Computer Assisted Learning, 34 (6), 863–875.

Barrot, J. S. (2020). Scientific mapping of social media in education: A decade of exponential growth. Journal of Educational Computing Research .  https://doi.org/10.1177/0735633120972010 .

Barrot, J. S. (2021). Social media as a language learning environment: A systematic review of the literature (2008–2019). Computer Assisted Language Learning . https://doi.org/10.1080/09588221.2021.1883673 .

Bergen, N., & Labonté, R. (2020). “Everything is perfect, and we have no problems”: Detecting and limiting social desirability bias in qualitative research. Qualitative Health Research, 30 (5), 783–792.

Birks, M., & Mills, J. (2011). Grounded theory: A practical guide . Sage.

Boelens, R., De Wever, B., & Voet, M. (2017). Four key challenges to the design of blended learning: A systematic literature review. Educational Research Review, 22 , 1–18.

Buehler, M. A. (2004). Where is the library in course management software? Journal of Library Administration, 41 (1–2), 75–84.

Carter, R. A., Jr., Rice, M., Yang, S., & Jackson, H. A. (2020). Self-regulated learning in online learning environments: Strategies for remote learning. Information and Learning Sciences, 121 (5/6), 321–329.

Cavanaugh, C. S., Barbour, M. K., & Clark, T. (2009). Research and practice in K-12 online learning: A review of open access literature. The International Review of Research in Open and Distributed Learning, 10 (1), 1–22.

Cicchetti, D., Bronen, R., Spencer, S., Haut, S., Berg, A., Oliver, P., & Tyrer, P. (2006). Rating scales, scales of measurement, issues of reliability: Resolving some critical issues for clinicians and researchers. The Journal of Nervous and Mental Disease, 194 (8), 557–564.

Copeland, W. E., McGinnis, E., Bai, Y., Adams, Z., Nardone, H., Devadanam, V., & Hudziak, J. J. (2021). Impact of COVID-19 pandemic on college student mental health and wellness. Journal of the American Academy of Child & Adolescent Psychiatry, 60 (1), 134–141.

Day, T., Chang, I. C. C., Chung, C. K. L., Doolittle, W. E., Housel, J., & McDaniel, P. N. (2021). The immediate impact of COVID-19 on postsecondary teaching and learning. The Professional Geographer, 73 (1), 1–13.

Donitsa-Schmidt, S., & Ramot, R. (2020). Opportunities and challenges: Teacher education in Israel in the Covid-19 pandemic. Journal of Education for Teaching, 46 (4), 586–595.

Drane, C., Vernon, L., & O’Shea, S. (2020). The impact of ‘learning at home’on the educational outcomes of vulnerable children in Australia during the COVID-19 pandemic. Literature Review Prepared by the National Centre for Student Equity in Higher Education. Curtin University, Australia.

Elaish, M., Shuib, L., Ghani, N., & Yadegaridehkordi, E. (2019). Mobile English language learning (MELL): A literature review. Educational Review, 71 (2), 257–276.

Fawaz, M., Al Nakhal, M., & Itani, M. (2021). COVID-19 quarantine stressors and management among Lebanese students: A qualitative study.  Current Psychology , 1–8.

Franchi, T. (2020). The impact of the Covid-19 pandemic on current anatomy education and future careers: A student’s perspective. Anatomical Sciences Education, 13 (3), 312–315.

Garcia, R., Falkner, K., & Vivian, R. (2018). Systematic literature review: Self-regulated learning strategies using e-learning tools for computer science. Computers & Education, 123 , 150–163.

Gonzalez, T., De La Rubia, M. A., Hincz, K. P., Comas-Lopez, M., Subirats, L., Fort, S., & Sacha, G. M. (2020). Influence of COVID-19 confinement on students’ performance in higher education. PLoS One, 15 (10), e0239490.

Hew, K. F., Jia, C., Gonda, D. E., & Bai, S. (2020). Transitioning to the “new normal” of learning in unpredictable times: Pedagogical practices and learning performance in fully online flipped classrooms. International Journal of Educational Technology in Higher Education, 17 (1), 1–22.

Huang, Q. (2019). Comparing teacher’s roles of F2F learning and online learning in a blended English course. Computer Assisted Language Learning, 32 (3), 190–209.

John Hopkins University. (2021). Global map . https://coronavirus.jhu.edu/

Kapasia, N., Paul, P., Roy, A., Saha, J., Zaveri, A., Mallick, R., & Chouhan, P. (2020). Impact of lockdown on learning status of undergraduate and postgraduate students during COVID-19 pandemic in West Bengal. India . Children and Youth Services Review, 116 , 105194.

Kebritchi, M., Lipschuetz, A., & Santiague, L. (2017). Issues and challenges for teaching successful online courses in higher education: A literature review. Journal of Educational Technology Systems, 46 (1), 4–29.

Khalil, R., Mansour, A. E., Fadda, W. A., Almisnid, K., Aldamegh, M., Al-Nafeesah, A., & Al-Wutayd, O. (2020). The sudden transition to synchronized online learning during the COVID-19 pandemic in Saudi Arabia: A qualitative study exploring medical students’ perspectives. BMC Medical Education, 20 (1), 1–10.

Matsumoto, K. (1994). Introspection, verbal reports and second language learning strategy research. Canadian Modern Language Review, 50 (2), 363–386.

Ng, W. (2012). Can we teach digital natives digital literacy? Computers & Education, 59 (3), 1065–1078.

Pham, T., & Nguyen, H. (2020). COVID-19: Challenges and opportunities for Vietnamese higher education. Higher Education in Southeast Asia and beyond, 8 , 22–24.

Google Scholar  

Rasheed, R. A., Kamsin, A., & Abdullah, N. A. (2020). Challenges in the online component of blended learning: A systematic review. Computers & Education, 144 , 103701.

Recker, M. M., Dorward, J., & Nelson, L. M. (2004). Discovery and use of online learning resources: Case study findings. Educational Technology & Society, 7 (2), 93–104.

Roblek, V., Mesko, M., Dimovski, V., & Peterlin, J. (2019). Smart technologies as social innovation and complex social issues of the Z generation. Kybernetes, 48 (1), 91–107.

Seplaki, C. L., Agree, E. M., Weiss, C. O., Szanton, S. L., Bandeen-Roche, K., & Fried, L. P. (2014). Assistive devices in context: Cross-sectional association between challenges in the home environment and use of assistive devices for mobility. The Gerontologist, 54 (4), 651–660.

Simbulan, N. (2020). COVID-19 and its impact on higher education in the Philippines. Higher Education in Southeast Asia and beyond, 8 , 15–18.

Singh, K., Srivastav, S., Bhardwaj, A., Dixit, A., & Misra, S. (2020). Medical education during the COVID-19 pandemic: a single institution experience. Indian Pediatrics, 57 (7), 678–679.

Singh, V., & Thurman, A. (2019). How many ways can we define online learning? A systematic literature review of definitions of online learning (1988–2018). American Journal of Distance Education, 33 (4), 289–306.

Spector, P. (1994). Using self-report questionnaires in OB research: A comment on the use of a controversial method. Journal of Organizational Behavior, 15 (5), 385–392.

Suryaman, M., Cahyono, Y., Muliansyah, D., Bustani, O., Suryani, P., Fahlevi, M., & Munthe, A. P. (2020). COVID-19 pandemic and home online learning system: Does it affect the quality of pharmacy school learning? Systematic Reviews in Pharmacy, 11 , 524–530.

Tallent-Runnels, M. K., Thomas, J. A., Lan, W. Y., Cooper, S., Ahern, T. C., Shaw, S. M., & Liu, X. (2006). Teaching courses online: A review of the research. Review of Educational Research, 76 (1), 93–135.

Tang, T., Abuhmaid, A. M., Olaimat, M., Oudat, D. M., Aldhaeebi, M., & Bamanger, E. (2020). Efficiency of flipped classroom with online-based teaching under COVID-19.  Interactive Learning Environments , 1–12.

Usher, M., & Barak, M. (2020). Team diversity as a predictor of innovation in team projects of face-to-face and online learners. Computers & Education, 144 , 103702.

Varea, V., & González-Calvo, G. (2020). Touchless classes and absent bodies: Teaching physical education in times of Covid-19.  Sport, Education and Society , 1–15.

Wallace, R. M. (2003). Online learning in higher education: A review of research on interactions among teachers and students. Education, Communication & Information, 3 (2), 241–280.

World Health Organization (2020). Coronavirus . https://www.who.int/health-topics/coronavirus#tab=tab_1

Xue, E., Li, J., Li, T., & Shang, W. (2020). China’s education response to COVID-19: A perspective of policy analysis.  Educational Philosophy and Theory , 1–13.

Download references

No funding was received in the conduct of this study.

Author information

Authors and affiliations.

College of Education, Arts and Sciences, National University, Manila, Philippines

Jessie S. Barrot, Ian I. Llenares & Leo S. del Rosario

You can also search for this author in PubMed   Google Scholar

Contributions

Jessie Barrot led the planning, prepared the instrument, wrote the report, and processed and analyzed data. Ian Llenares participated in the planning, fielded the instrument, processed and analyzed data, reviewed the instrument, and contributed to report writing. Leo del Rosario participated in the planning, fielded the instrument, processed and analyzed data, reviewed the instrument, and contributed to report writing.

Corresponding author

Correspondence to Jessie S. Barrot .

Ethics declarations

Ethics approval.

The study has undergone appropriate ethics protocol.

Consent to participate

Informed consent was sought from the participants.

Consent for publication

Authors consented the publication. Participants consented to publication as long as confidentiality is observed.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Barrot, J.S., Llenares, I.I. & del Rosario, L.S. Students’ online learning challenges during the pandemic and how they cope with them: The case of the Philippines. Educ Inf Technol 26 , 7321–7338 (2021). https://doi.org/10.1007/s10639-021-10589-x

Download citation

Received : 22 January 2021

Accepted : 17 May 2021

Published : 28 May 2021

Issue Date : November 2021

DOI : https://doi.org/10.1007/s10639-021-10589-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Remote learning
• Online learning
• Online learning strategies
• Higher education
• Find a journal
• Publish with us
• Track your research

• Open supplemental data
• Reference Manager
• Simple TEXT file

People also looked at

Original research article, how teachers conduct online teaching during the covid-19 pandemic: a case study of taiwan.

• Department of Science Communication, National Pingtung University, Pingtung, Taiwan

Although online teaching has been encouraged for many years, the COVID-19 pandemic has promoted it on a large scale. During the COVID-19 pandemic, students at all levels (college, secondary school, and elementary school) were unable to attend school. To maintain student learning, most schools have adopted online teaching. Therefore, the purpose of this study was to explore the design of online teaching activities and online teaching processes adopted by teachers at all levels during the pandemic. Online questionnaires were administered to teachers in Taiwan who had conducted online teaching (including during the formal suspension of classes or simulation exercises) due to the pandemic. According to a quantitative analysis and lag sequential analysis, the instructional behaviors most frequently performed by teachers were roll calls, lectures with a presentation screen, in-class task (assignment) allocation, and whole-class synchronous video-/audio-based discussion. Thus, there were six common significant sequential behaviors among teachers at all levels that were categorized into the four instructional stages of identifying the teaching environment, teaching the class, discussing and evaluating learning effectiveness. College teachers reminded students of some matters first and then called the roll after the students went online. Secondary school teachers were more likely to arrange practical or experimental courses and to use synchronous and asynchronous interactive activities. Finally, elementary school teachers were more likely to use homemade videos and share their screens for teaching and to arrange a large variety of teaching interactions. The differences among colleges, secondary schools, and elementary schools were identified, and suggestions were made accordingly.

Introduction

Since 1990, Internet-based distance teaching has become a global trend, and software, hardware and educational training have been evolving. Nouns related to e-learning, such as online learning, distance teaching, digital learning, mobile learning and recent massive open online courses (MOOCs), have shown a trend of learning via the Internet. However, despite active promotion by governments, there are still many limitations to the online educational environment from teaching and learning perspectives ( Meskhi et al., 2019 ; Sadeghi, 2019 ), such as the support of the administrative system, the establishment of a network bandwidth and teachers’ willingness to record e-Learning materials.

Since the first report of coronavirus disease 2019 (COVID-19) in Wuhan (China) in December 2019, COVID-19 has rapidly spread worldwide ( Zhu et al., 2020 ). The World Health Organization (WHO) declared a public health emergency of international concern on January 30, 2020 and named the disease COVID-19 on February 11, 2020. On March 11, 2020, the WHO declared COVID-19 a global pandemic ( Singhal, 2020 ; World Health Organization, 2020 ).

Due to the respiratory illness caused by COVID-19, many countries have suspended all types of face-to-face activities, including in-person education. The COVID-19 pandemic has forced many changes in most life domains to meet the repercussions of the pandemic control measures, and the education sector was no exception. In many countries, colleges, secondary schools and elementary schools have adopted the strategy of online education during the pandemic. As a result, teachers and students have had to quickly alter their teaching methods, regardless of whether they were experienced in and prepared for online education. Because of this situation, a proper term has appeared in the academic domain: emergency remote education.

Online education-related studies and models have been promoted for years ( Sun and Chen, 2016 ). Before the COVID-19 pandemic, most of these studies were focused on colleges, while teachers and students in elementary and secondary schools remained inexperienced in emergency remote education ( Lestari and Gunawan, 2020 ). For example, Taiwan has promoted digital course certification at the university level for many years, and universities have also supported teachers in recording e-learning materials. Therefore, university teachers are more experienced in online teaching. However, in primary and secondary schools, digital teaching plays only a supplementary role. The pre-epidemic model is for students to go to classrooms. Therefore, teachers in primary and secondary schools have insufficient experience in switching to online teaching.

In response to COVID-19, schools at all levels needed an immediate shift towards online education, which can be both an opportunity and a challenge ( Toquero, 2020 ). Therefore, some studies have been conducted to discuss emergency remote education during the COVID-19 pandemic. For example, Crawford et al. (2020) investigated 20 countries’ responses to the COVID-19 epidemic. They pointed out that the response to higher education is diverse, including nonresponse, campus social isolation strategies, and rapid response to fully online courses. Watermeyer et al. (2020) reported a survey from 1,148 academics working in universities in the United Kingdom. They suggested that online migration is engendering significant dysfunctionality and disturbance to their pedagogical roles and their personal lives. Loima (2020) compared socio educational policies and arguments in Sweden and Finland during the COVID-19 pandemic. The results showed that Swedish and Finnish policy obscured mandates and restricted information. However, remote learning was successful in epidemiologic and curricular senses in Finnish. Basilaia and Kvavadze (2020) conducted a case study in Georgia. The Google Meet platform was implemented for online education with 950 students. The results indicated that the quick transition to the online form of education went successful and that gained experience can be used in the future. Putra et al. (2020) visited 10 websites in Indonesia to explore students’ learning experiences during the COVID-19 pandemic. The results showed that student hardship in learning from home caused a lack of learning resources, such as not accessing the Internet and parents’ ability to support their children’s learning. In Cyprus, Souleles et al. (2020) believed that e-learning is not an add-on to existing teaching and learning practices and that disciplinary differences need to be considered. The provision of hurriedly set up workshops to enhance the skill gaps of teachers, although it is a necessary step, cannot replace the need for sustained training in both the pedagogical and technical areas. In Norway, Langford and Damsa (2020) discovered some phenomena, such as the Zoom revolution, a significant level of interactive online learning, innovations for involuntary teaching reform, collegial competence building and self-help, technological challenges and pedagogical insecurity. In Beijing, when the outbreak prevented people from going to school, the scholars of Peking University proposed the following five specific teaching strategies for online education in pandemic circumstances: 1) a high relevance between online instructional design and student learning; 2) the effective delivery of online instructional information; 3) adequate support provided by faculty and teaching assistants to students; 4) high-quality participation to improve the breadth and depth of students’ learning; and 5) contingency plans to address unexpected incidents on online education platforms ( Bao, 2020 ). In addition, many scholars in medical education have explored the challenges and future of online education in their own field. For example, Goh and Sandars (2020) indicated that major changes have been taking place in global medical education and that it is necessary to strengthen technological innovation to maintain teaching; they proposed that the use of artificial intelligence for adaptive learning and virtual reality might be future trends in medical education.

In addition to the abovementioned studies on overall education, there have been more studies that explore students’ opinions during emergency remote education. Abbasi et al. (2020) reported that when students were unable to go to school because of the epidemic, they did not like online learning as much as face-to-face teaching. Thus, school administrative departments and teachers should take the necessary measures to improve online educational environments. Based on a survey of 77 medical students in their classroom situations, Agarwal and Kaushik (2020) argued that students believed that online courses altered their normal procedures, saved a large amount of time and made it easy for them to obtain teaching materials. The main barriers to learning were the number of participants and technical failures during class conversations. Owusu-Fordjour et al. (2020) investigated online learning among 214 college students and found that the pandemic had a negative effect on their learning because many of them were not used to learning effectively on their own. As most of the students in this region could not access the Internet and lacked the technical knowledge of Internet devices, the learning platforms that were used also posed a challenge for them.

Most of the above studies on students’ opinions focused on college education because college students’ abilities for self-regulated learning in online education are better than those of primary and secondary students because of their age ( Heo and Han, 2018 ). However, when the pandemic began, all schools faced the challenge of switching to emergency remote education. Some studies have explored learning issues in elementary and secondary schools during the outbreak. For example, Sintema (2020) noted that Zambian primary and secondary schools enabled teachers and students to have classes via mobile phones and tablets by implementing e-learning and smart revision portals while increasing the number of mobile devices available for use. The study found that these teaching and learning methods helped teachers deliver teaching materials and students to be capable of self-regulated learning during the pandemic. In addition, Fauzi and Khusuma (2020) surveyed 45 elementary school students and identified problems in implementing online teaching, including 1) the availability of facilities, 2) network and Internet usage, 3) the planning, implementation, and evaluation of learning, and 4) collaboration with parents. The authors expected that online learning would be helpful to teachers during the COVID-19 pandemic, but their results indicated poor outcomes of online learning, with 80% of teachers reporting that they felt dissatisfied with online education.

Study Objectives

According to the abovementioned studies on the COVID-19 pandemic, teachers and students were forced to conduct online education regardless of their level of preparation for it. Most of the recent studies have investigated students’ feelings about online education and learning effectiveness, but there has been little discussion of teachers’ design of teaching activities when they had to switch to online teaching due to the pandemic. Accordingly, this study explored how teachers designed their teaching activities when they switched to online teaching due to the pandemic or how they conducted online teaching in the form of exercises to provide a reference for the future promotion of online education. As a result, the first objective of this study is to discuss teachers’ design of online teaching activity during the COVID-19 pandemic.

Moreover, our knowledge of teachers’ online teaching activities is based on online teaching activities in normal conditions. In addition, teaching activity plans are sequential ( Brown and Green, 2018 ). For example, Gagne’s model of instructional design includes 1) gaining attention, 2) informing the learner of the objective, 3) stimulating the recall of prerequisite learning, 4) presenting the stimulus material, 5) providing learning guidance, 6) eliciting the performance, 7) providing feedback, 8) assessing the performance, and 9) enhancing retention and transfer ( Khadjooi et al. (2011) . The second objective of this study is to explore which activities were carried out first and last and the order of teachers’ teaching activities. Thus, to understand the teaching activities adopted by teachers during the COVID-19 pandemic and the implementation of these teaching activities, this study used a lag sequential analysis to inform the discussion on this topic.

During the COVID-19 pandemic, students at all levels (college, secondary school and elementary school) were unable to attend school. Online teaching can continue to maintain learning activities when everyone is not going out. Therefore, to maintain students’ learning, most schools have adopted online teaching. In addition, for students of different ages, e.g., colleges, secondary schools and elementary schools, the teaching behaviors taken by teachers will be different ( Kennan et al., 2018 ). Understanding how teachers engage in online teaching behaviors at this emergency remote learning time can serve as a reference for the future promotion of e-learning. This study discusses teachers’ design of online teaching activity at all levels during the pandemic. The study explores the following two research questions:

What are the online teaching activities adopted by teachers due to the suspension of classroom teaching due to the COVID-19 pandemic? and

What are the similarities and differences among teachers from colleges, secondary schools and elementary schools in the design of their online teaching activity processes?

Methods and Materials

Data collection and participants.

This study mainly investigates teachers who had conducted online education (including during the formal suspension of classes and simulation exercises) because of the pandemic. Convenience sampling was adopted. Although many courses might have been changed to online teaching at the time that the teachers answered the questions, the study questionnaire asked about the teaching activity design of only one course. Data were collected from May 20 to June 30, 2020, by using a web-based questionnaire with a cross-sectional design. A total of 270 teachers answered the questionnaires, and 223 of the responses were valid. There were 23 college teachers (10.3%), 51 secondary school teachers (22.9%) and 149 elementary school teachers (66.8%).

In this study, a questionnaire on online teaching activities was developed based on the research purpose and some studies (i.e., Nilson and Goodson, 2017 ; Trust and Pektas, 2018 ; Sharoff, 2019 ). The questionnaire consisted of three major parts, namely, basic data (sex male and female), age (below 30 years old, 31–40 years old, 41–50 years old, 51–60 years old and over 61 years old), the served school (college or university, middle or high school, and elementary school), the years of teaching experience, online teaching experience (Were you experienced in online teaching prior to the pandemic (frequently, occasionally and never), Why did you conduct online teaching? (already in use, class suspension due to medical diagnosis and simulation exercises), and in most cases, which of the following methods do you choose for online teaching?) and the teaching process (synchronous teaching, asynchronous teaching and blended teaching). According to the various online teaching platforms and systems used (e.g., Google Classroom, iCAN, iLMS, Microsoft Teams, Moodle, Sunnet LMS, Adobe Connect, Cisco WebEx, CyberLink U Meeting, Google Meet, Jitsi Meet, JoinNet, LINE Chat, Zoom, YouTube Live broadcast, Facebook Live broadcast and Zuvio), the teaching processes were analyzed, summarized and then divided into the 4 categories of teaching (A), learning interaction (B), learning effectiveness (C) and others (D). After the online teaching activity questionnaire was prepared, three experts in online college education, one elementary school teacher, and one online education administrator of the education agency were invited to assist in the review of the questionnaire. The survey questionnaire was refined according to the suggestions received through the experts’ review. The instructional behaviors that comprise the teaching process are listed below.

 A1 Lecturing–presentation screen. A2 Lecturing–blackboard. A3 Sharing a screen with computer software. A4 Playing videos made by teachers. A5 Playing videos made by others. A6 Practical (experimental) demonstration.

B Learning Interaction

 B1 Whole-class synchronous text-based discussion. B2 Whole-class asynchronous text-based discussion. B3 Whole-class synchronous video-/audio-based discussion. B4 Whole-class asynchronous video-/audio-based discussion. B5 Whole-group synchronous text-based discussion. B6 Whole-group asynchronous text-based discussion. B7 Whole-group synchronous video-/audio-based discussion. B8 Whole-group asynchronous video-/audio-based discussion. B9 Whole-class whiteboard interaction. B10 Whole-group whiteboard interaction. B11 Student self-practice. B12 Operation by remote control. B13 Data collection and collation.

C Learning Effectiveness

 C1 In-class study experience. C2 In-class task (assignment) allocation. C3 In-class online test. C4 In-class online questionnaire. C5 In-class peer evaluation. C6 In-class work submission. C7 In-class assignment/work report. C8 After-class study experience. C9 After-class task (assignment) allocation. C10 After-class online test. C11 After-class online questionnaire. C12 After-class peer evaluation/voting. C13 After-class work submission.

 D1 Roll call D2 Inquiry about the status of hardware and software. D3 Reminders of other noncourse matters. D4 Others.

Data Analysis

In this study, descriptive statistics were used to analyze the basic data, the online teaching experience and the first research question. The second research question was analyzed through a lag sequential analysis ( Bakeman and Gottman, 1997 ). Lag sequential analysis ( Bakeman and Gottman, 1997 ) is used not only to explore a continuous sequence of behavioral coding categories (namely, an online teaching process) in which an initial behavioral coding category is followed by a subsequent category but also to visualize behavioral patterns. Researchers have mainly applied this method to the analysis of education issues. For example, Lin et al. (2020) developed a scaffolding-based collaborative problem-solving (CPS) learning environment to improve students’ learning in CPS activities. According to the study results, the learning performance was significantly better for the scaffolding mind tool group than for the study sheet group, and the scaffolding mind tool group showed more diverse cognitive process transitions in their behavioral patterns. Zarzour et al. (2020) investigated the behavioral patterns of students by using eBooks on Facebook for learning. The experimental results indicated significant behavioral learning sequences and revealed that the behaviors of liking, commenting, and sharing posts with peers showed the most significant differences between the students with higher and lower engagement. Wang and Liu (2020) discussed teachers’ current online teaching and students’ interaction and collaborative knowledge construction. According to the results, the design and organization of learning materials and the facilitation of discourse promoted students’ interaction, reduced the number of peripheral students, and supported students’ collaborative knowledge construction.

The following were the five steps in the lag sequential analysis: 1) calculating the number of transitions among the behavioral codes to obtain the transition frequency table; 2) calculating the conditional probability of the transitions among the codes based on the above sequential frequency matrix to produce the sequential transition conditional probability; 3) calculating the expected value of the overall transition process among the codes based on the sequential frequency matrix; 4) verifying whether all sequences were significantly continuous one-by-one based on the Z-score values of the transition frequency calculated from the above three matrices (adjusted residuals table); and 5) drawing the sequence transition association diagram with nodes that represent all coding behaviors connected by arrows for further inferential analysis.

Results and Discussion

Basic data and online teaching experience.

The Google online questionnaire was adopted in this study, and all questions must be answered to be valid. As shown in Table 1 , a total of 223 valid questionnaires were collected in this study. In terms of sex, there were 100 males (44.8%) and 123 females (55.2%), and there was virtually no difference in the numbers of males and females. Therefore, this study is not affected by gender differences. Regarding age, there were 23 people (13.3%) under 30 years old, 24 people (10.8%) between 31 and 40 years, 57 people (25.6%) between 41 and 50 years, 106 people (47.5%) between 51 and 60 years, and 36 people (16.1%) aged 61 years or over. Most of the respondents were between 41 and 60 years old. In the quartile of age, Q1 was 31–40 years, and Q2 (median) and Q3 were 41–50 years. Regarding the years of teaching experience, there were 12 teachers (5.4%) with less than 1 year of service, 26 teachers (11.7%) with 1–5 years of service, 21 teachers (9.4%) with 6–10 years of service, 54 teachers (24.2%) with 11–15 years of service, 57 teachers (25.6%) with 16–20 years of service, and 53 teachers (23.8%) with more than 21 years of service. In the quartile of teaching experience, Q1 is 1–5 years, Q2 (median) is 16–20 years, and Q3 is more than 21 years. Most teachers were found to have many years of experience. At the school level, there were 23 college teachers (10.3%), 51 secondary school teachers (22.9%), and 149 elementary school teachers (66.8%). Thus, most of the respondents were elementary school teachers, followed by secondary school teachers.

TABLE 1 . Participants’ characteristics, including their online teaching experience.

Then, the study examined whether teachers were experienced in online teaching prior to the pandemic. Fourteen teachers (6.3%) had frequently engaged in online teaching, 79 (35.4%) had engaged in it occasionally, and 130 (58.3%) had never engaged in it, which shows that more than half of the teachers had no experience in online teaching. As a result, the reason why online teaching had been adopted was explored. In total, 21 teachers had been teaching online prior to the pandemic (9.4%), seven taught online due to a medical diagnosis (3.1%), and 195 taught online as a part of simulation exercises (87.4%); these findings show that the primary reason for switching to online teaching was simulation exercises, as the COVID-19 pandemic in Taiwan was well controlled. Regarding the modes frequently used in online teaching, 89 teachers (39.9%) used synchronous teaching (teachers and students go online at the same time to carry out teaching and learning activities), 65 teachers (29.1%) used asynchronous teaching (teachers upload teaching materials to the network platform, and students can watch them online within a specified time and carry out learning activities), and 69 teachers (30.9%) used blended teaching (teaching and learning activities that combine both synchronous and asynchronous modes); thus, similar proportions of the teachers used the three teaching modes.

Teaching Activities

The 223 teachers who returned valid questionnaires had a total of 1,310 instructional behaviors, with an average of 5.87 instructional behaviors for each teacher. Table 2 shows the overall instructional behaviors, and the number and percentage of instructional behaviors in elementary schools, secondary schools, and colleges.

TABLE 2 . Number and percentage of various instructional behaviors.

Overall, there were 329 data points (25.11%) for teaching (A), 340 data points (25.95%) for learning interaction (B), 383 data points (29.24%) for learning effectiveness (C), and 258 data points (19.69%) for others (D). The proportion of other instructional behaviors was similar to but slightly lower than the proportions of the remaining three teaching categories. Among the four teaching categories, the top four behaviors were roll call (D1) with 132 data points (10.08%), lecturing with a presentation screen (A1) with 124 data points (9.47%), in-class task (assignment) allocation (C2) with 104 data points (7.94%), and whole-class synchronous video-/audio-based discussion (B3) with 103 data points (7.86%). Thus, the most common behavior in each category was teaching behavior.

Then, the four teaching categories were analyzed from an overall perspective. In teaching (A), lecturing with a presentation screen (A1) was the most frequently used ( N = 124, 9.47%), followed by sharing a screen with computer software (A3) (N = 101, 7.71%); this shows that most teachers frequently lectured with a presentation screen and shared their computer screens in online teaching. In learning interaction (B), whole-class synchronous video-/audio-based discussion (B3) was the most frequently used ( N = 103, 7.86%), followed by student self-practice (B11) ( N = 82, 6.26%); this indicates that the teachers often conducted a whole-class synchronous discussion after teaching and allowed students to become familiar with the teaching content through their own practice. In addition, we also found that the teachers conducted more activities in entire classes than in groups. Although group learning is a common teaching activity in classroom teaching, in the online teaching environment, group interaction is rarely adopted by teachers because of the limitations imposed by the functional design of the learning platform or system. In learning effectiveness (C), the most common and second-most common instructional behaviors both concerned task (assignment) allocation, including class-task (assignment) allocation (C2) with 104 data points (7.94%), and after-class task (assignment) allocation (C9) with 69 data points (5.27%). By comparing all behaviors in class and after class, we found that the frequency of all in-class behaviors ( N = 224, 17.11%) was larger than the frequency of after-class behaviors ( N = 159, 12.14%), which suggests that the teachers mostly evaluated teaching effectiveness in class. Finally, in the other category (D), the most common mode was roll call (D1) with 132 data points (10.08%), followed by inquiry about the status of hardware and software (D2) with 74 data points (5.65%). These two items were important preclass activities in online teaching, although they do not take much time in classroom teaching.

Finally, the study explored the similarities and differences among colleges, secondary schools, and elementary schools in the four categories. In terms of teaching (A), we found that lecturing with a presentation screen (A1) was the most frequently used, followed by sharing a screen with computer software (A3), regardless of the learning stage. In terms of playing videos, we found that most videos played in colleges were made by teachers (A4), while the videos played in secondary and elementary schools were made by others (A5); this shows that college teachers were more likely to make course videos for students to watch. Practical (experimental) demonstration (A6) was the least used. Although physical education courses and experimental courses still existed in the curriculum, the teachers seldom performed practice or experiments in the online teaching environment. In terms of learning interaction (B), we found that whole-class synchronous video-/audio-based discussion (B3) was the most frequently used, regardless of the learning stage. Moreover, unlike student practice (B11), whole-class synchronous text-based discussion (B1) was frequently used in colleges and secondary schools but was less frequently used in elementary schools, while whole-class whiteboard interaction (B9) was frequently used in elementary schools; this indicates that the teachers were more likely to arrange synchronous text-based discussion activities for older students. Finally, we found that data collection and collation (B13), a common activity in online teaching, was used in some secondary and elementary schools but not in colleges. In terms of learning effectiveness (C), we found that task (assignment) allocation (C2 and C9) was the most frequently used, regardless of the learning stage. Second, assignment and work reports (C7 and C13) were commonly used by college teachers for evaluation, online tests (C3 and C10) were commonly used by secondary and elementary teachers for evaluation, and there was almost no difference in their use between online teaching and the current situation in classroom teaching. In terms of the other category (D), based on the proportions of teachers who used the behaviors, we found that the most common behaviors were roll calls (D1), inquiries about the status of hardware and software (D2), and reminders of other noncourse matters (D3), regardless of the learning stage. These behaviors were important for online teaching, but the questionnaire did not dedicate many questions to these behaviors.

Teaching Behavioral Sequence

During the lag sequential analysis, the adjusted residuals table was calculated, where the columns represent initial behaviors, and the rows signify the behaviors that occurred immediately after the behaviors listed in the columns. A Z-score greater than 1.96 indicated that the sequence was significant. In this study, there were 49, 58, and 104 significant behavioral sequences for colleges, secondary schools and elementary schools, respectively (as shown in the Supplementary Appendix ). With the 36 instructional behaviors examined in this study, there were many significant behavioral sequences in each learning stage. To facilitate the discussion, the common significant behavioral sequences of colleges, secondary schools, and elementary schools were first extracted, and six significant behavioral sequences were identified in total. Second, to compare the differences among colleges, secondary schools, and elementary schools in the teaching process, significant behavioral sequences with Z-score values greater than five were discussed. There were 11, 10, and 15 significant behavioral sequences with Z-score values greater than five in colleges, secondary schools and elementary schools, respectively. The values shown in Table 3 are the Z-scores.

TABLE 3 . Significant behavioral sequences (similarities and differences Z-score>5).

There were six common significant behavioral sequences in colleges, secondary schools, and elementary schools ( Figure 1 ). The six significant behavioral sequences were divided into four stages. The first stage included roll calls and the confirmation of an effective online teaching environment (D1→D2). The next stage was teaching the class. The common teaching methods were presentation (A1) and screen sharing (A3). The next stage after teaching included text-based synchronous discussion (A1→B5 and A3→B1). The final stage was the evaluation of learning effectiveness (B5→C7 and C3→C4). Overall, the common significant behavioral sequences in colleges, secondary schools and elementary schools, namely, identifying the teaching environment, teaching the class, discussing and evaluating learning effectiveness, were similar to the usual teaching processes.

FIGURE 1 . Overall behavioral transfer diagram.

Then, the characteristics of the teaching processes in colleges, secondary schools and elementary schools were compared based on the significant behavioral sequences with Z-score values greater than 5. To provide a basis for comparison, the abovementioned phases, i.e., 1) identifying the teaching environment, 2) teaching the class, 3) discussing and 4) evaluating learning effectiveness, were used for discussion. First, colleges ( Figure 2 ) were more likely than secondary and elementary schools to use the following sequence: reminders for students of other noncurriculum matters (D3) → roll call (D1). This may be because, compared with secondary and elementary school teachers, college teachers are more likely to call roll after reminding students of matters during class and waiting for students to go online. This not only presents the actual situation of the physical classroom but also represents the teacher’s differences in class management for students of different ages. In the teaching class stage, there was one common behavioral sequence between college teachers and elementary school teachers, namely, lecturing with a blackboard (A2) → practical (experimental) demonstration (A6). This may be because some experimental course teachers are used to lecture with a blackboard and directly filme experimental courses with cameras. In the discussing stage, college teachers engaged in less interactive learning behaviors than secondary and elementary school teachers, but most of their behaviors were carried out in groups (A5→B6, B5→B10, B10→B3). Finally, in the evaluating learning effectiveness stage, college teachers had more diversified evaluation methods, including practice, tests, and questionnaires. Moreover, college teachers arranged many in-class and after-class evaluations (C1→C12, C3→C12 and C4→C12).

FIGURE 2 . Behavioral transfer diagram for colleges.

Second, in secondary schools ( Figure 3 ), teachers were more likely to arrange practical or experimental courses and then carry out interactive activities such as discussions or questionnaires (A6→B2, A6→B4 and A6→C11). In conducting interactive activities, teachers in secondary schools were more likely to use synchronous and asynchronous methods than teachers in colleges or elementary schools. Finally, in the stage of evaluating learning effectiveness, secondary school teachers had more diversified evaluation methods than college or elementary school teachers, including tests, questionnaires, and practice.

FIGURE 3 . Behavioral transfer diagram for secondary schools.

In elementary schools ( Figure 4 ), teachers were more likely to use homemade videos and share their screens while teaching and then conduct discussions (A3→B3, A5→B2, A5→B7). The teaching interactions arranged by elementary school teachers were diversified, and discussions containing audio and text were conducted with synchronous and asynchronous methods. Elementary school teachers, similar to college and secondary school teachers, used a variety of evaluation methods. In addition, elementary school teachers arranged many in-class evaluations, and after-class assignments, which is similar to general classroom teaching.

FIGURE 4 . Behavioral transfer diagram for elementary schools.

Discussion and Conclusion

During the COVID-19 pandemic, students at all levels (colleges, secondary schools, and elementary schools) were unable to attend school, and most schools switched to online teaching. To understand the design of online teaching activities among teachers at all levels, online questionnaires were adopted in this study to investigate teachers in Taiwan who had conducted online teaching due to the pandemic. There were 223 valid questionnaires.

The first objective was to explore teachers’ online teaching activities when classroom teaching was suspended due to COVID-19. Based on the results of the frequencies of behaviors in the teaching, learning interaction, learning effectiveness and other categories, the top four instructional behaviors were roll calls, lecturing with a presentation screen, in-class task (assignment) allocation and whole-class synchronous video-/audio-based discussion. Then, the study explored the similarities and differences among colleges, secondary schools, and elementary schools in the four categories. In terms of teaching, lecturing with a presentation screen was the most frequently used, regardless of the learning stage. In terms of playing videos, most videos played in colleges were made by teachers, while most videos played in secondary and elementary schools were made by others. In terms of learning interaction, we found that whole-class synchronous video-/audio-based discussion was the most frequently used, regardless of the learning stage. In addition, teachers’ arrangement of synchronous text-based discussions depended on the learning level. In terms of learning effectiveness, task (assignment) allocation was the most frequent behavior, regardless of the learning stage. Second, assignments and work reports were commonly used by college teachers for evaluation, while teachers in secondary and elementary schools were more likely to use online tests for evaluation. Finally, in terms of the other category, we found that roll calls and inquiries about the learning environment, such as the status of hardware and software, were necessary for online teaching, regardless of the learning stage.

Overall, more time was spent on roll calls and inquiries about the status of hardware and software in online teaching than in classroom teaching. This means that teachers’ technical capabilities for online teaching, students’ familiarity with digital platforms, and the software and hardware assistance provided by the school’s information center will all affect the quality of e-learning. Moreover, in terms of teaching, interaction and evaluation, the arrangement of these activities among teachers at all levels was slightly different from the arrangement of these activities in classroom teaching, and appropriate teaching activities could be designed according to the online teaching environment. Despite the limitations of online teaching platforms, online learning activities can still be carried out.

The second objective of this study was to explore the similarities and differences among college, secondary school and elementary school teachers in the design of the online teaching activity process. According to the sequential behavioral analysis, the common significant behavioral sequences of colleges, secondary school and elementary schools were divided into 1) roll calls and identification of the teaching environment, 2) teaching through presentation and screen demonstration, 3) synchronous text-based discussion, and 4) an effectiveness evaluation. Overall, the common significant behavioral sequences of colleges, secondary schools and elementary schools were similar to the usual teaching processes. In terms of the characteristics, some college teachers reminded students of some matters first and then called the roll after students went online. During class, some teachers in experimental or practical courses were used to lecture with a blackboard, and directly filme experimental courses with cameras. Moreover, college teachers engaged in less interactive learning behaviors, but most of their behaviors were carried out in groups. Second, secondary school teachers were more likely to arrange practical or experimental courses and to use synchronous and asynchronous interactive activities. Finally, elementary school teachers were more likely to use homemade videos and share their screens for teaching and to arrange a large variety of teaching interactions; in addition, discussions containing audio and text were conducted with both synchronous and asynchronous methods.

Overall, colleges, secondary schools, and elementary schools had common significant sequential behaviors, including roll calls and the identification of the teaching environment, teaching through presentation and screen sharing, synchronous text-based discussion and an effectiveness evaluation. Moreover, college, secondary, and elementary school teachers had similar characteristics in the design of their teaching activity processes. In addition to these similar characteristics, college, secondary, and elementary school teachers also have some different characteristics. These different characteristics show that teachers at different stages of learning vary in their teaching strategies. These differences, in addition to showing the current teaching situation, can also provide scholars with information for related follow-up research.

According to the conclusions generated based on the descriptive analysis and lag sequential analysis, the following suggestions can be made.

Despite the small proportion of online practical and experimental courses, as evidenced by the observed online instructional behaviors, such courses are arranged in classroom teaching. It is suggested that when relevant, teachers should consider in advance how to respond to challenges in implementing practical and experimental courses in online teaching.

Discussion is more important in the online teaching environment than in general classroom teaching ( Wu, 2016 ). This study found that whole-class synchronous video-/audio-based discussion was the most frequently used method. Thus, whether activities are conducted as a class or in groups and whether synchronous or asynchronous discussion is used, teachers should improve the online discussion layout and their online leadership skills ( Tseng et al., 2019 ).

In classroom teaching, problem-based learning (PBL) courses are often arranged, which require students to collect and collate data through the Internet ( Dolmans et al., 2016 ). However, in this study, the rate of data collection and collation was low, even in the online education environment, but the activities of data collection and collation in the online learning environment are more suitable for adoption. Therefore, it is suggested that teachers should design activities of data collection and collation for more diversified teaching activities.

Due to the pandemic, people have been restricted in their ability to leave home. Therefore, in addition to the synchronous activities in class during teaching time, it is suggested that teachers arrange after-class asynchronous activities so that students can carry out learning activities when they cannot go out.

In classroom teaching, it does not take much time to call roll or manage hardware and software. However, the two behaviors are important in the online teaching environment. Thus, both teachers and learning platforms or system developers should think about how to reduce the time spent on roll calls and the management of hardware and software.

In terms of the research limitations and suggestions for future studies, this study took Taiwan’s teachers as an example; it is suggested that cross-country comparisons be carried out in future studies. Second, this study mainly discussed the situations, similarities and differences of colleges, secondary schools and elementary schools in the teaching activities and processes affected by the pandemic. However, teaching activities are also influenced by the course that is being taught. Thus, it is suggested that future researchers base their discussions on various types of courses. Finally, teachers’ preparation for online teaching affects the quality of online education ( Hung, 2016 ), which was not analyzed in this study. Therefore, it is suggested that future researchers compare the differences in teachers’ experiences with online teaching.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author Contributions

The author contributed to the conception of the idea, implementing and analyzing the experimental results, and writing the manuscript.

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2021.675434/full#supplementary-material

Abbasi, S., Ayoob, T., Malik, A., and Memon, S. I. (2020). Perceptions of Students Regarding E-Learning during Covid-19 at a Private Medical College. Pak J. Med. Sci. 36, S57–S61. doi:10.12669/pjms.36.COVID19-S4.2766

PubMed Abstract | CrossRef Full Text | Google Scholar

Agarwal, S., and Kaushik, J. S. (2020). Student's Perception of Online Learning during COVID Pandemic. Indian J. Pediatr. 87 (7), 554. doi:10.1007/s12098-020-03327-7

Bakeman, R., and Gottman, J. M. (1997). Observing Interaction: An Introduction to Lag Sequential Analysis . 2nd ed. United Kingdom: Cambridge University Press . doi:10.1017/cbo9780511527685

CrossRef Full Text

Bao, W. (2020). COVID ‐19 and Online Teaching in Higher Education: A Case Study of Peking University. Hum. Behav Emerg Tech 2 (2), 113–115. doi:10.1002/hbe2.191

CrossRef Full Text | Google Scholar

Basilaia, G., and Kvavadze, D. (2020). Transition to Online Education in Schools during a SARS-CoV-2 Coronavirus (COVID-19) Pandemic in Georgia. Pedagogical Res. 5 (4), 1–9. doi:10.29333/pr/7937

Brown, A. H., and Green, T. D. (2018). Beyond Teaching Instructional Design Models: Exploring the Design Process to advance Professional Development and Expertise. J. Comput. High Educ. 30 (1), 176–186. doi:10.1007/s12528-017-9164-y

Crawford, J., Butler-Henderson, K., Rudolph, J., Malkawi, B., Glowatz, M., Burton, R., and Lam, S. (2020). COVID-19: 20 Countries' Higher Education Intra-period Digital Pedagogy Responses. J. Appl. Learn. Teach. 3 (1), 1–20. doi:10.37074/jalt.2020.3.1.7

Dolmans, D. H. J. M., Loyens, S. M. M., Marcq, H., and Gijbels, D. (2016). Deep and Surface Learning in Problem-Based Learning: a Review of the Literature. Adv. Health Sci. Educ. 21 (5), 1087–1112. doi:10.1007/s10459-015-9645-6

Fauzi, I., and Sastra Khusuma, I. H. (2020). Teachers' Elementary School in Online Learning of COVID-19 Pandemic Conditions. J. Iqra. 5 (1), 58–70. doi:10.25217/ji.v5i1.914

Goh, P.-S., and Sandars, J. (2020). A Vision of the Use of Technology in Medical Education after the COVID-19 Pandemic. MedEdPublish 9, 1. doi:10.15694/mep.2020.000049.1

Heo, J., and Han, S. (2018). Effects of Motivation, Academic Stress and Age in Predicting Self-Directed Learning Readiness (SDLR): Focused on Online College Students. Educ. Inf. Technol. 23 (1), 61–71. doi:10.1007/s10639-017-9585-2

Hung, M.-L. (2016). Teacher Readiness for Online Learning: Scale Development and Teacher Perceptions. Comput. Educ. 94, 120–133. doi:10.1016/j.compedu.2015.11.012

Kennan, S., Bigatel, P., Stockdale, S., and Hoewe, J. (2018). The (Lack of) Influence of Age and Class Standing on Preferred Teaching Behaviors for Online Students. Online Learn. 22 (1), 163–181. doi:10.24059/olj.v22i1.1086

Khadjooi, K., Rostami, K., and Ishaq, S. (2011). How to Use Gagne's Model of Instructional Design in Teaching Psychomotor Skills. Gastroenterol. Hepatol. Bed Bench 4 (3), 116–119.

PubMed Abstract Google Scholar

Langford, M., and Damsa, C. (2020). Online Teaching in the Time of COVID-19: Academic Teachers’ Experiences in Norway . Centre for Experiential Legal Learning (CELL), University of Oslo .

Lestari, P. A. S., and Gunawan, G. (2020). The Impact of Covid-19 Pandemic on Learning Implementation of Primary and Secondary School Levels. Indonesian J. Elem. Child. Educ. 1 (2), 58–63.

Google Scholar

Lin, P.-C., Hou, H.-T., and Chang, K.-E. (2020). The Development of a Collaborative Problem Solving Environment that Integrates a Scaffolding Mind Tool and Simulation-Based Learning: an Analysis of Learners' Performance and Their Cognitive Process in Discussion. Interactive Learn. Environments . doi:10.1080/10494820.2020.1719163

Loima, J. (2020). Socio-Educational Policies and Covid-19 - A Case Study on Finland and Sweden in the Spring 2020. Int. J. Edu. Literacy. Studies. 8 (3), 59–75. doi:10.7575/aiac.ijels.v.8n.3p.59

Meskhi, B., Ponomareva, S., and Ugnich, E. (2019). E-learning in Higher Inclusive Education: Needs, Opportunities and Limitations. Int. J. Edu. Manag. 33 (3), 424–437. doi:10.1108/IJEM-09-2018-0282

Nilson, L. B., and Goodson, L. A. (2017). Online Teaching at its Best: Merging Instructional Design with Teaching and Learning Research . Hoboken, NJ: John Wiley & Sons .

Owusu-Fordjour, C., Koomson, C. K., and Hanson, D. (2020). The Impact of Covid-19 on Learning-The Perspective of the Ghanaian Student. Eur. J. Educ. Stud. 7 (3), 88–101. doi:10.5281/zenodo.3753

Putra, P., Liriwati, F. Y., Tahrim, T., Syafrudin, S., and Aslan, A. (2020). The Students Learning from home Experiences during Covid-19 School Closures Policy in Indonesia. J. Iqra. 5 (2), 30–42. doi:10.25217/ji.v5i2.1019

Sadeghi, M. (2019). A Shift from Classroom to Distance Learning: Advantages and Limitations. Int. J. Res. English. Edu. 4 (1), 80–88. doi:10.29252/ijree.4.1.80

Sharoff, L. (2019). Creative and Innovative Online Teaching Strategies: Facilitation for Active Participation. Jeo 16 (2), 2. doi:10.9743/jeo.2019.16.2.9

Singhal, T. (2020). A Review of Coronavirus Disease-2019 (COVID-19). Indian J. Pediatr. 87 (4), 281–286. doi:10.1007/s12098-020-03263-6

Sintema, E. J. (2020). E-learning and Smart Revision Portal for Zambian Primary and Secondary School Learners: A Digitalized Virtual Classroom in the COVID-19 Era and beyond. Aquademia , 4(2), ep20017. doi:10.29333/aquademia/8253

Souleles, N., Laghos, A., and Savva, S. (2020). “From Face-To-Face to Online: Assessing the Effectiveness of the Rapid Transition of Higher Education Due to the Coronavirus Outbreak,” in 15th International Technology, Education and Development Conference , Cyprus , November 9–10, 2020 . doi:10.21125/iceri.2020.0274

Sun, A., and Chen, X. (2016). Online Education and its Effective Practice: A Research Review. JITE:Research 15, 157–190. doi:10.28945/3502

Toquero, C. M. (2020). Challenges and Opportunities for Higher Education amid the COVID-19 Pandemic: The Philippine Context. Pedagogical Res. 5 (4), em0063. doi:10.29333/pr/7947

Trust, T., and Pektas, E. (2018). Using the ADDIE Model and Universal Design for Learning Principles to Develop an Open Online Course for Teacher Professional Development. J. Digital Learn. Teach. Educ. 34 (4), 219–233. doi:10.1080/21532974.2018.1494521

Tseng, H., Yi, X., and Yeh, H.-T. (2019). Learning-related Soft Skills Among Online Business Students in Higher Education: Grade Level and Managerial Role Differences in Self-Regulation, Motivation, and Social Skill. Comput. Hum. Behav. 95, 179–186. doi:10.1016/j.chb.2018.11.035

Wang, Y., and Liu, Q. (2020). Effects of Online Teaching Presence on Students' Interactions and Collaborative Knowledge Construction. J. Comput. Assist. Learn. 36 (3), 370–382. doi:10.1111/jcal.12408

Watermeyer, R., Crick, T., Knight, C., and Goodall, J. (2020). COVID-19 and Digital Disruption in UK Universities: Afflictions and Affordances of Emergency Online Migration. High Educ. (Dordr) 81, 623–641. doi:10.1007/s10734-020-00561-y

World Health Organization (2020). Coronavirus Disease (COVID-2019) Situation Reports (Situation report - 51). Available at: www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports .

Wu, S.-Y. (2016). The Effect of Teaching Strategies and Students' Cognitive Style on the Online Discussion Environment. Asia-pacific Edu Res. 25 (2), 267–277. doi:10.1007/s40299-015-0259-9

Zarzour, H., Bendjaballah, S., and Harirche, H. (2020). Exploring the Behavioral Patterns of Students Learning with a Facebook-Based E-Book Approach. Comput. Educ. 156, 103957. doi:10.1016/j.compedu.2020.103957

Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., et al. (2020). A Novel Coronavirus from Patients with Pneumonia in China, 2019. N. Engl. J. Med. 382, 727–733. doi:10.1056/NEJMoa2001017

Keywords: COVID-19, e-learning, online teaching, lag sequential analysis (LSA), emergency remote education (ERE)

Citation: Wu S-Y (2021) How Teachers Conduct Online Teaching During the COVID-19 Pandemic: A Case Study of Taiwan. Front. Educ. 6:675434. doi: 10.3389/feduc.2021.675434

Received: 03 March 2021; Accepted: 06 May 2021; Published: 28 May 2021.

Reviewed by:

Copyright © 2021 Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sheng-Yi Wu, [email protected]

• Share full article

Advertisement

Supported by

Student Opinion

Is Online Learning Effective?

A new report found that the heavy dependence on technology during the pandemic caused “staggering” education inequality. What was your experience?

By Natalie Proulx

During the coronavirus pandemic, many schools moved classes online. Was your school one of them? If so, what was it like to attend school online? Did you enjoy it? Did it work for you?

In “ Dependence on Tech Caused ‘Staggering’ Education Inequality, U.N. Agency Says ,” Natasha Singer writes:

In early 2020, as the coronavirus spread, schools around the world abruptly halted in-person education. To many governments and parents, moving classes online seemed the obvious stopgap solution. In the United States, school districts scrambled to secure digital devices for students. Almost overnight, videoconferencing software like Zoom became the main platform teachers used to deliver real-time instruction to students at home. Now a report from UNESCO , the United Nations’ educational and cultural organization, says that overreliance on remote learning technology during the pandemic led to “staggering” education inequality around the world. It was, according to a 655-page report that UNESCO released on Wednesday, a worldwide “ed-tech tragedy.” The report, from UNESCO’s Future of Education division, is likely to add fuel to the debate over how governments and local school districts handled pandemic restrictions, and whether it would have been better for some countries to reopen schools for in-person instruction sooner. The UNESCO researchers argued in the report that “unprecedented” dependence on technology — intended to ensure that children could continue their schooling — worsened disparities and learning loss for hundreds of millions of students around the world, including in Kenya, Brazil, Britain and the United States. The promotion of remote online learning as the primary solution for pandemic schooling also hindered public discussion of more equitable, lower-tech alternatives, such as regularly providing schoolwork packets for every student, delivering school lessons by radio or television — and reopening schools sooner for in-person classes, the researchers said. “Available evidence strongly indicates that the bright spots of the ed-tech experiences during the pandemic, while important and deserving of attention, were vastly eclipsed by failure,” the UNESCO report said. The UNESCO researchers recommended that education officials prioritize in-person instruction with teachers, not online platforms, as the primary driver of student learning. And they encouraged schools to ensure that emerging technologies like A.I. chatbots concretely benefited students before introducing them for educational use. Education and industry experts welcomed the report, saying more research on the effects of pandemic learning was needed. “The report’s conclusion — that societies must be vigilant about the ways digital tools are reshaping education — is incredibly important,” said Paul Lekas, the head of global public policy for the Software & Information Industry Association, a group whose members include Amazon, Apple and Google. “There are lots of lessons that can be learned from how digital education occurred during the pandemic and ways in which to lessen the digital divide. ” Jean-Claude Brizard, the chief executive of Digital Promise, a nonprofit education group that has received funding from Google, HP and Verizon, acknowledged that “technology is not a cure-all.” But he also said that while school systems were largely unprepared for the pandemic, online education tools helped foster “more individualized, enhanced learning experiences as schools shifted to virtual classrooms.” ​Education International, an umbrella organization for about 380 teachers’ unions and 32 million teachers worldwide, said the UNESCO report underlined the importance of in-person, face-to-face teaching. “The report tells us definitively what we already know to be true, a place called school matters,” said Haldis Holst, the group’s deputy general secretary. “Education is not transactional nor is it simply content delivery. It is relational. It is social. It is human at its core.”

Students, read the entire article and then tell us:

What findings from the report, if any, surprised you? If you participated in online learning during the pandemic, what in the report reflected your experience? If the researchers had asked you about what remote learning was like for you, what would you have told them?

At this point, most schools have returned to in-person teaching, but many still use technology in the classroom. How much tech is involved in your day-to-day education? Does this method of learning work well for you? If you had a say, would you want to spend more or less time online while in school?

What are some of the biggest benefits you have seen from technology when it comes to your education? What are some of the biggest drawbacks?

Haldis Holst, UNESCO’s deputy general secretary, said: “The report tells us definitively what we already know to be true, a place called school matters. Education is not transactional nor is it simply content delivery. It is relational. It is social. It is human at its core.” What is your reaction to that statement? Do you agree? Why or why not?

As a student, what advice would you give to schools that are already using or are considering using educational technology?

Students 13 and older in the United States and Britain, and 16 and older elsewhere, are invited to comment. All comments are moderated by the Learning Network staff, but please keep in mind that once your comment is accepted, it will be made public and may appear in print.

Find more Student Opinion questions here. Teachers, check out this guide to learn how you can incorporate these prompts into your classroom.

Natalie Proulx joined The Learning Network as a staff editor in 2017 after working as an English language arts teacher and curriculum writer. More about Natalie Proulx

The pandemic has had devastating impacts on learning. What will it take to help students catch up?

Subscribe to the brown center on education policy newsletter, megan kuhfeld , megan kuhfeld senior research scientist - nwea @megankuhfeld jim soland , jim soland assistant professor, school of education and human development - university of virginia, affiliated research fellow - nwea @jsoland karyn lewis , and karyn lewis director, center for school and student progress - nwea @karynlew emily morton emily morton research scientist - nwea @emily_r_morton.

March 3, 2022

As we reach the two-year mark of the initial wave of pandemic-induced school shutdowns, academic normalcy remains out of reach for many students, educators, and parents. In addition to surging COVID-19 cases at the end of 2021, schools have faced severe staff shortages , high rates of absenteeism and quarantines , and rolling school closures . Furthermore, students and educators continue to struggle with mental health challenges , higher rates of violence and misbehavior , and concerns about lost instructional time .

As we outline in our new research study released in January, the cumulative impact of the COVID-19 pandemic on students’ academic achievement has been large. We tracked changes in math and reading test scores across the first two years of the pandemic using data from 5.4 million U.S. students in grades 3-8. We focused on test scores from immediately before the pandemic (fall 2019), following the initial onset (fall 2020), and more than one year into pandemic disruptions (fall 2021).

Average fall 2021 math test scores in grades 3-8 were 0.20-0.27 standard deviations (SDs) lower relative to same-grade peers in fall 2019, while reading test scores were 0.09-0.18 SDs lower. This is a sizable drop. For context, the math drops are significantly larger than estimated impacts from other large-scale school disruptions, such as after Hurricane Katrina—math scores dropped 0.17 SDs in one year for New Orleans evacuees .

Even more concerning, test-score gaps between students in low-poverty and high-poverty elementary schools grew by approximately 20% in math (corresponding to 0.20 SDs) and 15% in reading (0.13 SDs), primarily during the 2020-21 school year. Further, achievement tended to drop more between fall 2020 and 2021 than between fall 2019 and 2020 (both overall and differentially by school poverty), indicating that disruptions to learning have continued to negatively impact students well past the initial hits following the spring 2020 school closures.

These numbers are alarming and potentially demoralizing, especially given the heroic efforts of students to learn and educators to teach in incredibly trying times. From our perspective, these test-score drops in no way indicate that these students represent a “ lost generation ” or that we should give up hope. Most of us have never lived through a pandemic, and there is so much we don’t know about students’ capacity for resiliency in these circumstances and what a timeline for recovery will look like. Nor are we suggesting that teachers are somehow at fault given the achievement drops that occurred between 2020 and 2021; rather, educators had difficult jobs before the pandemic, and now are contending with huge new challenges, many outside their control.

Clearly, however, there’s work to do. School districts and states are currently making important decisions about which interventions and strategies to implement to mitigate the learning declines during the last two years. Elementary and Secondary School Emergency Relief (ESSER) investments from the American Rescue Plan provided nearly $200 billion to public schools to spend on COVID-19-related needs. Of that sum, $22 billion is dedicated specifically to addressing learning loss using “evidence-based interventions” focused on the “ disproportionate impact of COVID-19 on underrepresented student subgroups. ” Reviews of district and state spending plans (see Future Ed , EduRecoveryHub , and RAND’s American School District Panel for more details) indicate that districts are spending their ESSER dollars designated for academic recovery on a wide variety of strategies, with summer learning, tutoring, after-school programs, and extended school-day and school-year initiatives rising to the top.

Comparing the negative impacts from learning disruptions to the positive impacts from interventions

To help contextualize the magnitude of the impacts of COVID-19, we situate test-score drops during the pandemic relative to the test-score gains associated with common interventions being employed by districts as part of pandemic recovery efforts. If we assume that such interventions will continue to be as successful in a COVID-19 school environment, can we expect that these strategies will be effective enough to help students catch up? To answer this question, we draw from recent reviews of research on high-dosage tutoring , summer learning programs , reductions in class size , and extending the school day (specifically for literacy instruction) . We report effect sizes for each intervention specific to a grade span and subject wherever possible (e.g., tutoring has been found to have larger effects in elementary math than in reading).

Figure 1 shows the standardized drops in math test scores between students testing in fall 2019 and fall 2021 (separately by elementary and middle school grades) relative to the average effect size of various educational interventions. The average effect size for math tutoring matches or exceeds the average COVID-19 score drop in math. Research on tutoring indicates that it often works best in younger grades, and when provided by a teacher rather than, say, a parent. Further, some of the tutoring programs that produce the biggest effects can be quite intensive (and likely expensive), including having full-time tutors supporting all students (not just those needing remediation) in one-on-one settings during the school day. Meanwhile, the average effect of reducing class size is negative but not significant, with high variability in the impact across different studies. Summer programs in math have been found to be effective (average effect size of .10 SDs), though these programs in isolation likely would not eliminate the COVID-19 test-score drops.

Figure 1: Math COVID-19 test-score drops compared to the effect sizes of various educational interventions

Source: COVID-19 score drops are pulled from Kuhfeld et al. (2022) Table 5; reduction-in-class-size results are from pg. 10 of Figles et al. (2018) Table 2; summer program results are pulled from Lynch et al (2021) Table 2; and tutoring estimates are pulled from Nictow et al (2020) Table 3B. Ninety-five percent confidence intervals are shown with vertical lines on each bar.

Notes: Kuhfeld et al. and Nictow et al. reported effect sizes separately by grade span; Figles et al. and Lynch et al. report an overall effect size across elementary and middle grades. We were unable to find a rigorous study that reported effect sizes for extending the school day/year on math performance. Nictow et al. and Kraft & Falken (2021) also note large variations in tutoring effects depending on the type of tutor, with larger effects for teacher and paraprofessional tutoring programs than for nonprofessional and parent tutoring. Class-size reductions included in the Figles meta-analysis ranged from a minimum of one to minimum of eight students per class.

Figure 2 displays a similar comparison using effect sizes from reading interventions. The average effect of tutoring programs on reading achievement is larger than the effects found for the other interventions, though summer reading programs and class size reduction both produced average effect sizes in the ballpark of the COVID-19 reading score drops.

Figure 2: Reading COVID-19 test-score drops compared to the effect sizes of various educational interventions

Source: COVID-19 score drops are pulled from Kuhfeld et al. (2022) Table 5; extended-school-day results are from Figlio et al. (2018) Table 2; reduction-in-class-size results are from pg. 10 of Figles et al. (2018) ; summer program results are pulled from Kim & Quinn (2013) Table 3; and tutoring estimates are pulled from Nictow et al (2020) Table 3B. Ninety-five percent confidence intervals are shown with vertical lines on each bar.

Notes: While Kuhfeld et al. and Nictow et al. reported effect sizes separately by grade span, Figlio et al. and Kim & Quinn report an overall effect size across elementary and middle grades. Class-size reductions included in the Figles meta-analysis ranged from a minimum of one to minimum of eight students per class.

There are some limitations of drawing on research conducted prior to the pandemic to understand our ability to address the COVID-19 test-score drops. First, these studies were conducted under conditions that are very different from what schools currently face, and it is an open question whether the effectiveness of these interventions during the pandemic will be as consistent as they were before the pandemic. Second, we have little evidence and guidance about the efficacy of these interventions at the unprecedented scale that they are now being considered. For example, many school districts are expanding summer learning programs, but school districts have struggled to find staff interested in teaching summer school to meet the increased demand. Finally, given the widening test-score gaps between low- and high-poverty schools, it’s uncertain whether these interventions can actually combat the range of new challenges educators are facing in order to narrow these gaps. That is, students could catch up overall, yet the pandemic might still have lasting, negative effects on educational equality in this country.

Given that the current initiatives are unlikely to be implemented consistently across (and sometimes within) districts, timely feedback on the effects of initiatives and any needed adjustments will be crucial to districts’ success. The Road to COVID Recovery project and the National Student Support Accelerator are two such large-scale evaluation studies that aim to produce this type of evidence while providing resources for districts to track and evaluate their own programming. Additionally, a growing number of resources have been produced with recommendations on how to best implement recovery programs, including scaling up tutoring , summer learning programs , and expanded learning time .

Ultimately, there is much work to be done, and the challenges for students, educators, and parents are considerable. But this may be a moment when decades of educational reform, intervention, and research pay off. Relying on what we have learned could show the way forward.

Related Content

Megan Kuhfeld, Jim Soland, Beth Tarasawa, Angela Johnson, Erik Ruzek, Karyn Lewis

December 3, 2020

Lindsay Dworkin, Karyn Lewis

October 13, 2021

Education Policy K-12 Education

Governance Studies

Brown Center on Education Policy

Annelies Goger, Katherine Caves, Hollis Salway

May 16, 2024

Sofoklis Goulas, Isabelle Pula

Melissa Kay Diliberti, Elizabeth D. Steiner, Ashley Woo

Tackle Challenges of Online Classes Due to COVID-19

College students should proactively contact professors or support staff with any questions about the transition, experts say.

Tackling Online Classes During COVID-19

Hero Images | Getty Images

College students in online classes face challenges like distractions, technical issues and lack of in-person interaction.

Like many college students nationwide, 21-year-old Alyssa Ashford is facing the challenges of unexpectedly switching to online classes due to the coronavirus outbreak. The junior premed student at Washington University in St. Louis is finishing the semester remotely from her home in St. Louis.

During the week, Ashford's father goes to work for an essential job, while her mother stays home to watch her 4-year-old and 7-month-old cousins, which can cause distractions while she's studying. She sometimes goes to her grandfather's house so she can focus. Juggling family responsibilities with her coursework is also a challenge, which is why Ashford carefully plans out every day.

"I write down my most important tasks for the entire month, and then I also write a weekly planner that lists my goals, and then I write a daily planner to accomplish those goals," Ashford says. "And with the transition from in person to online, I had to make sure that I kept a rigid schedule now that I really do have to go to class on my own time."

In the middle of the spring semester, undergraduate students across the U.S. had to suddenly pack up their belongings and finish their courses away from campus , an adjustment for many who are accustomed to in-person classes. Some colleges have announced they will continue offering only online classes through the summer semester, but many have yet to decide on the fall.

In a survey of more than 400 college students whose schools recently switched over to online education – conducted in March by Barnes & Noble College Insights – 60% of students said they felt at least somewhat prepared for the change. This was particularly true among students who previously took an online course. Still, 64% of survey respondents expressed concerns about being able to focus and maintaining the self-discipline needed to study remotely.

More recent polling from College Reaction/Axios in April showed that 77% of more than 800 college students surveyed said they felt distance learning is worse or much worse than in-person classes.

Here are some of the most common challenges undergraduate students are currently facing with online classes along with specific tips on how to address them:

• Technical issues.
• Distractions and time management.
• Staying motivated.
• Understanding course expectations.
• Lack of in-person interaction.
• Adapting to unfamiliar technology.
• Uncertainty about the future.

Technical Issues

Unfortunately, experts say, technical issues are bound to happen in an online-only environment. Ashford says that while attending one of her classes live through videoconferencing, her computer suddenly shut down and she needed to restart the device. There are also moments when her Wi-Fi is spotty.

The solution: The most important step is to stay in touch with professors and inform them about what's happening, experts say. They will hopefully understand and be flexible about the situation, perhaps even recording class sessions as a backup.

"There will be technology issues, and I think it's important that every student understands they're not alone in that, to allow themselves the patience to work through the problem," says Dawn Coder, director of academic advising and student disability services at the online Pennsylvania State University—World Campus . She adds that there's usually a fix for whatever issue arises. A school's technical support services can be a valuable resource, Coder says.

Distractions and Time Management

While studying from home or wherever students may be, there can be more distractions than usual, especially with family and possibly younger siblings around, says Reggie Smith III, CEO and executive director of the nonprofit United States Distance Learning Association.

As a result of these distractions – and possibly having additional responsibilities – time management becomes more challenging.

The solution: "Try to think about building a schedule – figuring out when you're going to do what you're going to do and then sharing that with the other people in your house," says Beth Martin, senior lecturer in environmental studies at Washington University in St. Louis. Students should still prioritize their physical and mental health , even if life is busier than usual, she adds.

Students should also try to identify a quiet time and place in their house to complete their coursework, if possible – even if that time is late at night, Smith says. If their other responsibilities become too overwhelming, students should consider talking with their academic adviser about course load options for the semester, he says.

For instance, some schools are allowing students to switch at least some classes to a pass-fail grading system for the spring, which could help ease some anxieties, experts say – though the policy changes vary across colleges.

Staying Motivated

Given that students may not be attending class at a set time on a physical campus, finding the motivation to get started on coursework can be difficult, experts say.

"When you don't see your home as a space of work, it's kind of a struggle to get in that mindset," says Emily Effren, a senior at Texas Tech University majoring in journalism as well as electronic media and communications. "But I have different places in my house, where my room will be my little oasis, but my downstairs kitchen table is where I'll sit down and get my work done."

The solution: In addition to creating a daily schedule and finding a productive workspace, Coder says it can also help to simply focus on the ultimate goal.

"At the end of the day, look back on the day and check mark off all of those items that you've completed. Knowing that you did will help to motivate you as well," Coder says.

She adds that staying in touch with classmates, in addition to reaching out to faculty or academic staff as needed, can also help motivate students.

Understanding Course Expectations

The sudden switch to online learning has left some students confused about some course requirements for the rest of the semester. They may wonder, for instance, if a final group presentation is still happening given that students can no longer meet on campus, or if they need to complete labs for science classes.

Students may also wonder whether their classes will have live lectures through videoconferencing at a set time on a certain day, or whether students are expected to learn the material on their own time.

The solution: Experts say students should be proactive in asking their professors questions about course expectations for the spring and whether there are any changes to requirements given the transition. Whether classes will be held live varies depending on the school, professor and discipline.

"Knowing the expectations as an online learner will help with time management because, again, you can plan out and schedule what's really needed week after week," Coder says.

Lack of In-Person Interaction

The lack of in-person interaction with both instructors and classmates can be particularly challenging. Allison Proszowski, a senior at Rutgers University—New Brunswick , is taking her spring classes online from her off-campus residence near the school. The chemical engineering major usually leads a study group for younger students on campus.

On campus, "It would be about me and 20 students taking the class. So you have that in-person, face-to-face interaction; it's a smaller group, you talk to the students, they talk to each other," Proszowski says. "And now transitioning that to an online environment has just not been the same."

The adjustment can be particularly difficult for students taking classes that are better suited for the face-to-face format, like those with science lab components.

"I'm a hands-on person," says Ashford, who now watches physics labs in a digital recording and then takes a quiz afterward. "I consider myself a visual learner as well, but I prefer to play around with the materials as well as converse with other students to understand the material better."

The solution: Experts say students should take advantage of the tools at their disposal. While not ideal for all learners, the best alternative to actual face-to-face interaction may be videoconferencing programs like Zoom, Skype or FaceTime. Talking on the phone with classmates or a professor is also an option.

Proszowski says she has attended virtual office hours to speak with her professors directly. "You have your video on, the professor has their video on, and you can kind of talk to them and get a little bit of additional help," she says.

Adapting to Unfamiliar Technology

Given the transition to online classes, Martin and her students are now adapting to some digital tools, she says.

"I think all of us have had to learn to use technology in the last couple months that some of us have never heard of, some of us may have used just a little bit of," says Martin, who typically teaches classes on campus.

The solution: Use the resources available through the school, Coder says. While this can include reaching out to technical support, students should determine whether they can save themselves time by looking up answers to their technology questions online or watching a video tutorial.

Uncertainty About the Future

The sudden switch to online classes for the spring semester – and the summer, in some cases – has caused anxiety and raised questions among students about their academic futures. Some are considering taking the fall semester off if their school continues to stick with online classes, for instance, while others are concerned about upholding a full course load while juggling family responsibilities at home.

The solution: Smith recommends students speak with an adviser or student support services as needed to determine whether adjustments can be made to their spring course schedule or a future semester if needed. For example, he says, a student may want to take fewer course credits in a future semester if his or her school continues offering only online classes and the student finds this format challenging.

Regardless of the challenges that come with the transition to online classes, students should remember that assistance is available, Coder says.

"It can be a difficult transition," Coder says. "But it doesn't have to be because there are many people who are willing and able to help with it."

Searching for a college? Get our complete rankings of Best Colleges.

How Undergrads Can Navigate COVID-19

Tags: colleges , online education , students , Coronavirus

2024 Best Colleges

Search for your perfect fit with the U.S. News rankings of colleges and universities.

College Admissions: Get a Step Ahead!

Sign up to receive the latest updates from U.S. News & World Report and our trusted partners and sponsors. By clicking submit, you are agreeing to our Terms and Conditions & Privacy Policy .

Ask an Alum: Making the Most Out of College

You May Also Like

Premedical programs: what to know.

Sarah Wood May 21, 2024

How Geography Affects College Admissions

Cole Claybourn May 21, 2024

Q&A: College Alumni Engagement

LaMont Jones, Jr. May 20, 2024

10 Destination West Coast College Towns

Cole Claybourn May 16, 2024

Scholarships for Lesser-Known Sports

Sarah Wood May 15, 2024

Should Students Submit Test Scores?

Sarah Wood May 13, 2024

Poll: Antisemitism a Problem on Campus

Lauren Camera May 13, 2024

Federal vs. Private Parent Student Loans

Erika Giovanetti May 9, 2024

14 Colleges With Great Food Options

Sarah Wood May 8, 2024

Colleges With Religious Affiliations

Anayat Durrani May 8, 2024

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

COVID-19’s impacts on the scope, effectiveness, and interaction characteristics of online learning: A social network analysis

Roles Data curation, Formal analysis, Methodology, Writing – review & editing

¶ ‡ JZ and YD are contributed equally to this work as first authors.

Affiliation School of Educational Information Technology, South China Normal University, Guangzhou, Guangdong, China

Roles Data curation, Formal analysis, Methodology, Writing – original draft

Affiliations School of Educational Information Technology, South China Normal University, Guangzhou, Guangdong, China, Hangzhou Zhongce Vocational School Qiantang, Hangzhou, Zhejiang, China

Roles Data curation, Writing – original draft

Roles Data curation

Roles Writing – original draft

Affiliation Faculty of Education, Shenzhen University, Shenzhen, Guangdong, China

Roles Conceptualization, Supervision, Writing – review & editing

* E-mail: [email protected] (JH); [email protected] (YZ)

• Junyi Zhang, 
• Yigang Ding, 
• Xinru Yang, 
• Jinping Zhong, 
• XinXin Qiu, 
• Zhishan Zou, 
• Yujie Xu, 
• Xiunan Jin, 
• Xiaomin Wu, 

• Published: August 23, 2022
• https://doi.org/10.1371/journal.pone.0273016
• Reader Comments

The COVID-19 outbreak brought online learning to the forefront of education. Scholars have conducted many studies on online learning during the pandemic, but only a few have performed quantitative comparative analyses of students’ online learning behavior before and after the outbreak. We collected review data from China’s massive open online course platform called icourse.163 and performed social network analysis on 15 courses to explore courses’ interaction characteristics before, during, and after the COVID-19 pan-demic. Specifically, we focused on the following aspects: (1) variations in the scale of online learning amid COVID-19; (2a) the characteristics of online learning interaction during the pandemic; (2b) the characteristics of online learning interaction after the pandemic; and (3) differences in the interaction characteristics of social science courses and natural science courses. Results revealed that only a small number of courses witnessed an uptick in online interaction, suggesting that the pandemic’s role in promoting the scale of courses was not significant. During the pandemic, online learning interaction became more frequent among course network members whose interaction scale increased. After the pandemic, although the scale of interaction declined, online learning interaction became more effective. The scale and level of interaction in Electrodynamics (a natural science course) and Economics (a social science course) both rose during the pan-demic. However, long after the pandemic, the Economics course sustained online interaction whereas interaction in the Electrodynamics course steadily declined. This discrepancy could be due to the unique characteristics of natural science courses and social science courses.

Citation: Zhang J, Ding Y, Yang X, Zhong J, Qiu X, Zou Z, et al. (2022) COVID-19’s impacts on the scope, effectiveness, and interaction characteristics of online learning: A social network analysis. PLoS ONE 17(8): e0273016. https://doi.org/10.1371/journal.pone.0273016

Editor: Heng Luo, Central China Normal University, CHINA

Received: April 20, 2022; Accepted: July 29, 2022; Published: August 23, 2022

Copyright: © 2022 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data underlying the results presented in the study were downloaded from https://www.icourse163.org/ and are now shared fully on Github ( https://github.com/zjyzhangjunyi/dataset-from-icourse163-for-SNA ). These data have no private information and can be used for academic research free of charge.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

The development of the mobile internet has spurred rapid advances in online learning, offering novel prospects for teaching and learning and a learning experience completely different from traditional instruction. Online learning harnesses the advantages of network technology and multimedia technology to transcend the boundaries of conventional education [ 1 ]. Online courses have become a popular learning mode owing to their flexibility and openness. During online learning, teachers and students are in different physical locations but interact in multiple ways (e.g., via online forum discussions and asynchronous group discussions). An analysis of online learning therefore calls for attention to students’ participation. Alqurashi [ 2 ] defined interaction in online learning as the process of constructing meaningful information and thought exchanges between more than two people; such interaction typically occurs between teachers and learners, learners and learners, and the course content and learners.

Massive open online courses (MOOCs), a 21st-century teaching mode, have greatly influenced global education. Data released by China’s Ministry of Education in 2020 show that the country ranks first globally in the number and scale of higher education MOOCs. The COVID-19 outbreak has further propelled this learning mode, with universities being urged to leverage MOOCs and other online resource platforms to respond to government’s “School’s Out, But Class’s On” policy [ 3 ]. Besides MOOCs, to reduce in-person gatherings and curb the spread of COVID-19, various online learning methods have since become ubiquitous [ 4 ]. Though Lederman asserted that the COVID-19 outbreak has positioned online learning technologies as the best way for teachers and students to obtain satisfactory learning experiences [ 5 ], it remains unclear whether the COVID-19 pandemic has encouraged interaction in online learning, as interactions between students and others play key roles in academic performance and largely determine the quality of learning experiences [ 6 ]. Similarly, it is also unclear what impact the COVID-19 pandemic has had on the scale of online learning.

Social constructivism paints learning as a social phenomenon. As such, analyzing the social structures or patterns that emerge during the learning process can shed light on learning-based interaction [ 7 ]. Social network analysis helps to explain how a social network, rooted in interactions between learners and their peers, guides individuals’ behavior, emotions, and outcomes. This analytical approach is especially useful for evaluating interactive relationships between network members [ 8 ]. Mohammed cited social network analysis (SNA) as a method that can provide timely information about students, learning communities and interactive networks. SNA has been applied in numerous fields, including education, to identify the number and characteristics of interelement relationships. For example, Lee et al. also used SNA to explore the effects of blogs on peer relationships [ 7 ]. Therefore, adopting SNA to examine interactions in online learning communities during the COVID-19 pandemic can uncover potential issues with this online learning model.

Taking China’s icourse.163 MOOC platform as an example, we chose 15 courses with a large number of participants for SNA, focusing on learners’ interaction characteristics before, during, and after the COVID-19 outbreak. We visually assessed changes in the scale of network interaction before, during, and after the outbreak along with the characteristics of interaction in Gephi. Examining students’ interactions in different courses revealed distinct interactive network characteristics, the pandemic’s impact on online courses, and relevant suggestions. Findings are expected to promote effective interaction and deep learning among students in addition to serving as a reference for the development of other online learning communities.

2. Literature review and research questions

Interaction is deemed as central to the educational experience and is a major focus of research on online learning. Moore began to study the problem of interaction in distance education as early as 1989. He defined three core types of interaction: student–teacher, student–content, and student–student [ 9 ]. Lear et al. [ 10 ] described an interactivity/ community-process model of distance education: they specifically discussed the relationships between interactivity, community awareness, and engaging learners and found interactivity and community awareness to be correlated with learner engagement. Zulfikar et al. [ 11 ] suggested that discussions initiated by the students encourage more students’ engagement than discussions initiated by the instructors. It is most important to afford learners opportunities to interact purposefully with teachers, and improving the quality of learner interaction is crucial to fostering profound learning [ 12 ]. Interaction is an important way for learners to communicate and share information, and a key factor in the quality of online learning [ 13 ].

Timely feedback is the main component of online learning interaction. Woo and Reeves discovered that students often become frustrated when they fail to receive prompt feedback [ 14 ]. Shelley et al. conducted a three-year study of graduate and undergraduate students’ satisfaction with online learning at universities and found that interaction with educators and students is the main factor affecting satisfaction [ 15 ]. Teachers therefore need to provide students with scoring justification, support, and constructive criticism during online learning. Some researchers examined online learning during the COVID-19 pandemic. They found that most students preferred face-to-face learning rather than online learning due to obstacles faced online, such as a lack of motivation, limited teacher-student interaction, and a sense of isolation when learning in different times and spaces [ 16 , 17 ]. However, it can be reduced by enhancing the online interaction between teachers and students [ 18 ].

Research showed that interactions contributed to maintaining students’ motivation to continue learning [ 19 ]. Baber argued that interaction played a key role in students’ academic performance and influenced the quality of the online learning experience [ 20 ]. Hodges et al. maintained that well-designed online instruction can lead to unique teaching experiences [ 21 ]. Banna et al. mentioned that using discussion boards, chat sessions, blogs, wikis, and other tools could promote student interaction and improve participation in online courses [ 22 ]. During the COVID-19 pandemic, Mahmood proposed a series of teaching strategies suitable for distance learning to improve its effectiveness [ 23 ]. Lapitan et al. devised an online strategy to ease the transition from traditional face-to-face instruction to online learning [ 24 ]. The preceding discussion suggests that online learning goes beyond simply providing learning resources; teachers should ideally design real-life activities to give learners more opportunities to participate.

As mentioned, COVID-19 has driven many scholars to explore the online learning environment. However, most have ignored the uniqueness of online learning during this time and have rarely compared pre- and post-pandemic online learning interaction. Taking China’s icourse.163 MOOC platform as an example, we chose 15 courses with a large number of participants for SNA, centering on student interaction before and after the pandemic. Gephi was used to visually analyze changes in the scale and characteristics of network interaction. The following questions were of particular interest:

• (1) Can the COVID-19 pandemic promote the expansion of online learning?
• (2a) What are the characteristics of online learning interaction during the pandemic?
• (2b) What are the characteristics of online learning interaction after the pandemic?
• (3) How do interaction characteristics differ between social science courses and natural science courses?

3. Methodology

3.1 research context.

We selected several courses with a large number of participants and extensive online interaction among hundreds of courses on the icourse.163 MOOC platform. These courses had been offered on the platform for at least three semesters, covering three periods (i.e., before, during, and after the COVID-19 outbreak). To eliminate the effects of shifts in irrelevant variables (e.g., course teaching activities), we chose several courses with similar teaching activities and compared them on multiple dimensions. All course content was taught online. The teachers of each course posted discussion threads related to learning topics; students were expected to reply via comments. Learners could exchange ideas freely in their responses in addition to asking questions and sharing their learning experiences. Teachers could answer students’ questions as well. Conversations in the comment area could partly compensate for a relative absence of online classroom interaction. Teacher–student interaction is conducive to the formation of a social network structure and enabled us to examine teachers’ and students’ learning behavior through SNA. The comment areas in these courses were intended for learners to construct knowledge via reciprocal communication. Meanwhile, by answering students’ questions, teachers could encourage them to reflect on their learning progress. These courses’ successive terms also spanned several phases of COVID-19, allowing us to ascertain the pandemic’s impact on online learning.

3.2 Data collection and preprocessing

To avoid interference from invalid or unclear data, the following criteria were applied to select representative courses: (1) generality (i.e., public courses and professional courses were chosen from different schools across China); (2) time validity (i.e., courses were held before during, and after the pandemic); and (3) notability (i.e., each course had at least 2,000 participants). We ultimately chose 15 courses across the social sciences and natural sciences (see Table 1 ). The coding is used to represent the course name.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0273016.t001

To discern courses’ evolution during the pandemic, we gathered data on three terms before, during, and after the COVID-19 outbreak in addition to obtaining data from two terms completed well before the pandemic and long after. Our final dataset comprised five sets of interactive data. Finally, we collected about 120,000 comments for SNA. Because each course had a different start time—in line with fluctuations in the number of confirmed COVID-19 cases in China and the opening dates of most colleges and universities—we divided our sample into five phases: well before the pandemic (Phase I); before the pandemic (Phase Ⅱ); during the pandemic (Phase Ⅲ); after the pandemic (Phase Ⅳ); and long after the pandemic (Phase Ⅴ). We sought to preserve consistent time spans to balance the amount of data in each period ( Fig 1 ).

https://doi.org/10.1371/journal.pone.0273016.g001

3.3 Instrumentation

Participants’ comments and “thumbs-up” behavior data were converted into a network structure and compared using social network analysis (SNA). Network analysis, according to M’Chirgui, is an effective tool for clarifying network relationships by employing sophisticated techniques [ 25 ]. Specifically, SNA can help explain the underlying relationships among team members and provide a better understanding of their internal processes. Yang and Tang used SNA to discuss the relationship between team structure and team performance [ 26 ]. Golbeck argued that SNA could improve the understanding of students’ learning processes and reveal learners’ and teachers’ role dynamics [ 27 ].

To analyze Question (1), the number of nodes and diameter in the generated network were deemed as indicators of changes in network size. Social networks are typically represented as graphs with nodes and degrees, and node count indicates the sample size [ 15 ]. Wellman et al. proposed that the larger the network scale, the greater the number of network members providing emotional support, goods, services, and companionship [ 28 ]. Jan’s study measured the network size by counting the nodes which represented students, lecturers, and tutors [ 29 ]. Similarly, network nodes in the present study indicated how many learners and teachers participated in the course, with more nodes indicating more participants. Furthermore, we investigated the network diameter, a structural feature of social networks, which is a common metric for measuring network size in SNA [ 30 ]. The network diameter refers to the longest path between any two nodes in the network. There has been evidence that a larger network diameter leads to greater spread of behavior [ 31 ]. Likewise, Gašević et al. found that larger networks were more likely to spread innovative ideas about educational technology when analyzing MOOC-related research citations [ 32 ]. Therefore, we employed node count and network diameter to measure the network’s spatial size and further explore the expansion characteristic of online courses. Brief introduction of these indicators can be summarized in Table 2 .

https://doi.org/10.1371/journal.pone.0273016.t002

To address Question (2), a list of interactive analysis metrics in SNA were introduced to scrutinize learners’ interaction characteristics in online learning during and after the pandemic, as shown below:

• (1) The average degree reflects the density of the network by calculating the average number of connections for each node. As Rong and Xu suggested, the average degree of a network indicates how active its participants are [ 33 ]. According to Hu, a higher average degree implies that more students are interacting directly with each other in a learning context [ 34 ]. The present study inherited the concept of the average degree from these previous studies: the higher the average degree, the more frequent the interaction between individuals in the network.
• (2) Essentially, a weighted average degree in a network is calculated by multiplying each degree by its respective weight, and then taking the average. Bydžovská took the strength of the relationship into account when determining the weighted average degree [ 35 ]. By calculating friendship’s weighted value, Maroulis assessed peer achievement within a small-school reform [ 36 ]. Accordingly, we considered the number of interactions as the weight of the degree, with a higher average degree indicating more active interaction among learners.
• (3) Network density is the ratio between actual connections and potential connections in a network. The more connections group members have with each other, the higher the network density. In SNA, network density is similar to group cohesion, i.e., a network of more strong relationships is more cohesive [ 37 ]. Network density also reflects how much all members are connected together [ 38 ]. Therefore, we adopted network density to indicate the closeness among network members. Higher network density indicates more frequent interaction and closer communication among students.
• (4) Clustering coefficient describes local network attributes and indicates that two nodes in the network could be connected through adjacent nodes. The clustering coefficient measures users’ tendency to gather (cluster) with others in the network: the higher the clustering coefficient, the more frequently users communicate with other group members. We regarded this indicator as a reflection of the cohesiveness of the group [ 39 ].
• (5) In a network, the average path length is the average number of steps along the shortest paths between any two nodes. Oliveres has observed that when an average path length is small, the route from one node to another is shorter when graphed [ 40 ]. This is especially true in educational settings where students tend to become closer friends. So we consider that the smaller the average path length, the greater the possibility of interaction between individuals in the network.
• (6) A network with a large number of nodes, but whose average path length is surprisingly small, is known as the small-world effect [ 41 ]. A higher clustering coefficient and shorter average path length are important indicators of a small-world network: a shorter average path length enables the network to spread information faster and more accurately; a higher clustering coefficient can promote frequent knowledge exchange within the group while boosting the timeliness and accuracy of knowledge dissemination [ 42 ]. Brief introduction of these indicators can be summarized in Table 3 .

https://doi.org/10.1371/journal.pone.0273016.t003

To analyze Question 3, we used the concept of closeness centrality, which determines how close a vertex is to others in the network. As Opsahl et al. explained, closeness centrality reveals how closely actors are coupled with their entire social network [ 43 ]. In order to analyze social network-based engineering education, Putnik et al. examined closeness centrality and found that it was significantly correlated with grades [ 38 ]. We used closeness centrality to measure the position of an individual in the network. Brief introduction of these indicators can be summarized in Table 4 .

https://doi.org/10.1371/journal.pone.0273016.t004

3.4 Ethics statement

This study was approved by the Academic Committee Office (ACO) of South China Normal University ( http://fzghb.scnu.edu.cn/ ), Guangzhou, China. Research data were collected from the open platform and analyzed anonymously. There are thus no privacy issues involved in this study.

4.1 COVID-19’s role in promoting the scale of online courses was not as important as expected

As shown in Fig 2 , the number of course participants and nodes are closely correlated with the pandemic’s trajectory. Because the number of participants in each course varied widely, we normalized the number of participants and nodes to more conveniently visualize course trends. Fig 2 depicts changes in the chosen courses’ number of participants and nodes before the pandemic (Phase II), during the pandemic (Phase III), and after the pandemic (Phase IV). The number of participants in most courses during the pandemic exceeded those before and after the pandemic. But the number of people who participate in interaction in some courses did not increase.

https://doi.org/10.1371/journal.pone.0273016.g002

In order to better analyze the trend of interaction scale in online courses before, during, and after the pandemic, the selected courses were categorized according to their scale change. When the number of participants increased (decreased) beyond 20% (statistical experience) and the diameter also increased (decreased), the course scale was determined to have increased (decreased); otherwise, no significant change was identified in the course’s interaction scale. Courses were subsequently divided into three categories: increased interaction scale, decreased interaction scale, and no significant change. Results appear in Table 5 .

https://doi.org/10.1371/journal.pone.0273016.t005

From before the pandemic until it broke out, the interaction scale of five courses increased, accounting for 33.3% of the full sample; one course’s interaction scale declined, accounting for 6.7%. The interaction scale of nine courses decreased, accounting for 60%. The pandemic’s role in promoting online courses thus was not as important as anticipated, and most courses’ interaction scale did not change significantly throughout.

No courses displayed growing interaction scale after the pandemic: the interaction scale of nine courses fell, accounting for 60%; and the interaction scale of six courses did not shift significantly, accounting for 40%. Courses with an increased scale of interaction during the pandemic did not maintain an upward trend. On the contrary, the improvement in the pandemic caused learners’ enthusiasm for online learning to wane. We next analyzed several interaction metrics to further explore course interaction during different pandemic periods.

4.2 Characteristics of online learning interaction amid COVID-19

4.2.1 during the covid-19 pandemic, online learning interaction in some courses became more active..

Changes in course indicators with the growing interaction scale during the pandemic are presented in Fig 3 , including SS5, SS6, NS1, NS3, and NS8. The horizontal ordinate indicates the number of courses, with red color representing the rise of the indicator value on the vertical ordinate and blue representing the decline.

https://doi.org/10.1371/journal.pone.0273016.g003

Specifically: (1) The average degree and weighted average degree of the five course networks demonstrated an upward trend. The emergence of the pandemic promoted students’ enthusiasm; learners were more active in the interactive network. (2) Fig 3 shows that 3 courses had increased network density and 2 courses had decreased. The higher the network density, the more communication within the team. Even though the pandemic accelerated the interaction scale and frequency, the tightness between learners in some courses did not improve. (3) The clustering coefficient of social science courses rose whereas the clustering coefficient and small-world property of natural science courses fell. The higher the clustering coefficient and the small-world property, the better the relationship between adjacent nodes and the higher the cohesion [ 39 ]. (4) Most courses’ average path length increased as the interaction scale increased. However, when the average path length grew, adverse effects could manifest: communication between learners might be limited to a small group without multi-directional interaction.

When the pandemic emerged, the only declining network scale belonged to a natural science course (NS2). The change in each course index is pictured in Fig 4 . The abscissa indicates the size of the value, with larger values to the right. The red dot indicates the index value before the pandemic; the blue dot indicates its value during the pandemic. If the blue dot is to the right of the red dot, then the value of the index increased; otherwise, the index value declined. Only the weighted average degree of the course network increased. The average degree, network density decreased, indicating that network members were not active and that learners’ interaction degree and communication frequency lessened. Despite reduced learner interaction, the average path length was small and the connectivity between learners was adequate.

https://doi.org/10.1371/journal.pone.0273016.g004

4.2.2 After the COVID-19 pandemic, the scale decreased rapidly, but most course interaction was more effective.

Fig 5 shows the changes in various courses’ interaction indicators after the pandemic, including SS1, SS2, SS3, SS6, SS7, NS2, NS3, NS7, and NS8.

https://doi.org/10.1371/journal.pone.0273016.g005

Specifically: (1) The average degree and weighted average degree of most course networks decreased. The scope and intensity of interaction among network members declined rapidly, as did learners’ enthusiasm for communication. (2) The network density of seven courses also fell, indicating weaker connections between learners in most courses. (3) In addition, the clustering coefficient and small-world property of most course networks decreased, suggesting little possibility of small groups in the network. The scope of interaction between learners was not limited to a specific space, and the interaction objects had no significant tendencies. (4) Although the scale of course interaction became smaller in this phase, the average path length of members’ social networks shortened in nine courses. Its shorter average path length would expedite the spread of information within the network as well as communication and sharing among network members.

Fig 6 displays the evolution of course interaction indicators without significant changes in interaction scale after the pandemic, including SS4, SS5, NS1, NS4, NS5, and NS6.

https://doi.org/10.1371/journal.pone.0273016.g006

Specifically: (1) Some course members’ social networks exhibited an increase in the average and weighted average. In these cases, even though the course network’s scale did not continue to increase, communication among network members rose and interaction became more frequent and deeper than before. (2) Network density and average path length are indicators of social network density. The greater the network density, the denser the social network; the shorter the average path length, the more concentrated the communication among network members. However, at this phase, the average path length and network density in most courses had increased. Yet the network density remained small despite having risen ( Table 6 ). Even with more frequent learner interaction, connections remained distant and the social network was comparatively sparse.

https://doi.org/10.1371/journal.pone.0273016.t006

In summary, the scale of interaction did not change significantly overall. Nonetheless, some course members’ frequency and extent of interaction increased, and the relationships between network members became closer as well. In the study, we found it interesting that the interaction scale of Economics (a social science course) course and Electrodynamics (a natural science course) course expanded rapidly during the pandemic and retained their interaction scale thereafter. We next assessed these two courses to determine whether their level of interaction persisted after the pandemic.

4.3 Analyses of natural science courses and social science courses

4.3.1 analyses of the interaction characteristics of economics and electrodynamics..

Economics and Electrodynamics are social science courses and natural science courses, respectively. Members’ interaction within these courses was similar: the interaction scale increased significantly when COVID-19 broke out (Phase Ⅲ), and no significant changes emerged after the pandemic (Phase Ⅴ). We hence focused on course interaction long after the outbreak (Phase V) and compared changes across multiple indicators, as listed in Table 7 .

https://doi.org/10.1371/journal.pone.0273016.t007

As the pandemic continued to improve, the number of participants and the diameter long after the outbreak (Phase V) each declined for Economics compared with after the pandemic (Phase IV). The interaction scale decreased, but the interaction between learners was much deeper. Specifically: (1) The weighted average degree, network density, clustering coefficient, and small-world property each reflected upward trends. The pandemic therefore exerted a strong impact on this course. Interaction was well maintained even after the pandemic. The smaller network scale promoted members’ interaction and communication. (2) Compared with after the pandemic (Phase IV), members’ network density increased significantly, showing that relationships between learners were closer and that cohesion was improving. (3) At the same time, as the clustering coefficient and small-world property grew, network members demonstrated strong small-group characteristics: the communication between them was deepening and their enthusiasm for interaction was higher. (4) Long after the COVID-19 outbreak (Phase V), the average path length was reduced compared with previous terms, knowledge flowed more quickly among network members, and the degree of interaction gradually deepened.

The average degree, weighted average degree, network density, clustering coefficient, and small-world property of Electrodynamics all decreased long after the COVID-19 outbreak (Phase V) and were lower than during the outbreak (Phase Ⅲ). The level of learner interaction therefore gradually declined long after the outbreak (Phase V), and connections between learners were no longer active. Although the pandemic increased course members’ extent of interaction, this rise was merely temporary: students’ enthusiasm for learning waned rapidly and their interaction decreased after the pandemic (Phase IV). To further analyze the interaction characteristics of course members in Economics and Electrodynamics, we evaluated the closeness centrality of their social networks, as shown in section 4.3.2.

4.3.2 Analysis of the closeness centrality of Economics and Electrodynamics.

The change in the closeness centrality of social networks in Economics was small, and no sharp upward trend appeared during the pandemic outbreak, as shown in Fig 7 . The emergence of COVID-19 apparently fostered learners’ interaction in Economics albeit without a significant impact. The closeness centrality changed in Electrodynamics varied from that of Economics: upon the COVID-19 outbreak, closeness centrality was significantly different from other semesters. Communication between learners was closer and interaction was more effective. Electrodynamics course members’ social network proximity decreased rapidly after the pandemic. Learners’ communication lessened. In general, Economics course showed better interaction before the outbreak and was less affected by the pandemic; Electrodynamics course was more affected by the pandemic and showed different interaction characteristics at different periods of the pandemic.

(Note: "****" indicates the significant distinction in closeness centrality between the two periods, otherwise no significant distinction).

https://doi.org/10.1371/journal.pone.0273016.g007

5. Discussion

We referred to discussion forums from several courses on the icourse.163 MOOC platform to compare online learning before, during, and after the COVID-19 pandemic via SNA and to delineate the pandemic’s effects on online courses. Only 33.3% of courses in our sample increased in terms of interaction during the pandemic; the scale of interaction did not rise in any courses thereafter. When the courses scale rose, the scope and frequency of interaction showed upward trends during the pandemic; and the clustering coefficient of natural science courses and social science courses differed: the coefficient for social science courses tended to rise whereas that for natural science courses generally declined. When the pandemic broke out, the interaction scale of a single natural science course decreased along with its interaction scope and frequency. The amount of interaction in most courses shrank rapidly during the pandemic and network members were not as active as they had been before. However, after the pandemic, some courses saw declining interaction but greater communication between members; interaction also became more frequent and deeper than before.

5.1 During the COVID-19 pandemic, the scale of interaction increased in only a few courses

The pandemic outbreak led to a rapid increase in the number of participants in most courses; however, the change in network scale was not significant. The scale of online interaction expanded swiftly in only a few courses; in others, the scale either did not change significantly or displayed a downward trend. After the pandemic, the interaction scale in most courses decreased quickly; the same pattern applied to communication between network members. Learners’ enthusiasm for online interaction reduced as the circumstances of the pandemic improved—potentially because, during the pandemic, China’s Ministry of Education declared “School’s Out, But Class’s On” policy. Major colleges and universities were encouraged to use the Internet and informational resources to provide learning support, hence the sudden increase in the number of participants and interaction in online courses [ 46 ]. After the pandemic, students’ enthusiasm for online learning gradually weakened, presumably due to easing of the pandemic [ 47 ]. More activities also transitioned from online to offline, which tempered learners’ online discussion. Research has shown that long-term online learning can even bore students [ 48 ].

Most courses’ interaction scale decreased significantly after the pandemic. First, teachers and students occupied separate spaces during the outbreak, had few opportunities for mutual cooperation and friendship, and lacked a sense of belonging [ 49 ]. Students’ enthusiasm for learning dissipated over time [ 50 ]. Second, some teachers were especially concerned about adapting in-person instructional materials for digital platforms; their pedagogical methods were ineffective, and they did not provide learning activities germane to student interaction [ 51 ]. Third, although teachers and students in remote areas were actively engaged in online learning, some students could not continue to participate in distance learning due to inadequate technology later in the outbreak [ 52 ].

5.2 Characteristics of online learning interaction during and after the COVID-19 pandemic

5.2.1 during the covid-19 pandemic, online interaction in most courses did not change significantly..

The interaction scale of only a few courses increased during the pandemic. The interaction scope and frequency of these courses climbed as well. Yet even as the degree of network interaction rose, course network density did not expand in all cases. The pandemic sparked a surge in the number of online learners and a rapid increase in network scale, but students found it difficult to interact with all learners. Yau pointed out that a greater network scale did not enrich the range of interaction between individuals; rather, the number of individuals who could interact directly was limited [ 53 ]. The internet facilitates interpersonal communication. However, not everyone has the time or ability to establish close ties with others [ 54 ].

In addition, social science courses and natural science courses in our sample revealed disparate trends in this regard: the clustering coefficient of social science courses increased and that of natural science courses decreased. Social science courses usually employ learning approaches distinct from those in natural science courses [ 55 ]. Social science courses emphasize critical and innovative thinking along with personal expression [ 56 ]. Natural science courses focus on practical skills, methods, and principles [ 57 ]. Therefore, the content of social science courses can spur large-scale discussion among learners. Some course evaluations indicated that the course content design was suboptimal as well: teachers paid close attention to knowledge transmission and much less to piquing students’ interest in learning. In addition, the thread topics that teachers posted were scarcely diversified and teachers’ questions lacked openness. These attributes could not spark active discussion among learners.

5.2.2 Online learning interaction declined after the COVID-19 pandemic.

Most courses’ interaction scale and intensity decreased rapidly after the pandemic, but some did not change. Courses with a larger network scale did not continue to expand after the outbreak, and students’ enthusiasm for learning paled. The pandemic’s reduced severity also influenced the number of participants in online courses. Meanwhile, restored school order moved many learning activities from virtual to in-person spaces. Face-to-face learning has gradually replaced online learning, resulting in lower enrollment and less interaction in online courses. Prolonged online courses could have also led students to feel lonely and to lack a sense of belonging [ 58 ].

The scale of interaction in some courses did not change substantially after the pandemic yet learners’ connections became tighter. We hence recommend that teachers seize pandemic-related opportunities to design suitable activities. Additionally, instructors should promote student-teacher and student-student interaction, encourage students to actively participate online, and generally intensify the impact of online learning.

5.3 What are the characteristics of interaction in social science courses and natural science courses?

The level of interaction in Economics (a social science course) was significantly higher than that in Electrodynamics (a natural science course), and the small-world property in Economics increased as well. To boost online courses’ learning-related impacts, teachers can divide groups of learners based on the clustering coefficient and the average path length. Small groups of students may benefit teachers in several ways: to participate actively in activities intended to expand students’ knowledge, and to serve as key actors in these small groups. Cultivating students’ keenness to participate in class activities and self-management can also help teachers guide learner interaction and foster deep knowledge construction.

As evidenced by comments posted in the Electrodynamics course, we observed less interaction between students. Teachers also rarely urged students to contribute to conversations. These trends may have arisen because teachers and students were in different spaces. Teachers might have struggled to discern students’ interaction status. Teachers could also have failed to intervene in time, to design online learning activities that piqued learners’ interest, and to employ sound interactive theme planning and guidance. Teachers are often active in traditional classroom settings. Their roles are comparatively weakened online, such that they possess less control over instruction [ 59 ]. Online instruction also requires a stronger hand in learning: teachers should play a leading role in regulating network members’ interactive communication [ 60 ]. Teachers can guide learners to participate, help learners establish social networks, and heighten students’ interest in learning [ 61 ]. Teachers should attend to core members in online learning while also considering edge members; by doing so, all network members can be driven to share their knowledge and become more engaged. Finally, teachers and assistant teachers should help learners develop knowledge, exchange topic-related ideas, pose relevant questions during course discussions, and craft activities that enable learners to interact online [ 62 ]. These tactics can improve the effectiveness of online learning.

As described, network members displayed distinct interaction behavior in Economics and Electrodynamics courses. First, these courses varied in their difficulty: the social science course seemed easier to understand and focused on divergent thinking. Learners were often willing to express their views in comments and to ponder others’ perspectives [ 63 ]. The natural science course seemed more demanding and was oriented around logical thinking and skills [ 64 ]. Second, courses’ content differed. In general, social science courses favor the acquisition of declarative knowledge and creative knowledge compared with natural science courses. Social science courses also entertain open questions [ 65 ]. Natural science courses revolve around principle knowledge, strategic knowledge, and transfer knowledge [ 66 ]. Problems in these courses are normally more complicated than those in social science courses. Third, the indicators affecting students’ attitudes toward learning were unique. Guo et al. discovered that “teacher feedback” most strongly influenced students’ attitudes towards learning social science courses but had less impact on students in natural science courses [ 67 ]. Therefore, learners in social science courses likely expect more feedback from teachers and greater interaction with others.

6. Conclusion and future work

Our findings show that the network interaction scale of some online courses expanded during the COVID-19 pandemic. The network scale of most courses did not change significantly, demonstrating that the pandemic did not notably alter the scale of course interaction. Online learning interaction among course network members whose interaction scale increased also became more frequent during the pandemic. Once the outbreak was under control, although the scale of interaction declined, the level and scope of some courses’ interactive networks continued to rise; interaction was thus particularly effective in these cases. Overall, the pandemic appeared to have a relatively positive impact on online learning interaction. We considered a pair of courses in detail and found that Economics (a social science course) fared much better than Electrodynamics (a natural science course) in classroom interaction; learners were more willing to partake in-class activities, perhaps due to these courses’ unique characteristics. Brint et al. also came to similar conclusions [ 57 ].

This study was intended to be rigorous. Even so, several constraints can be addressed in future work. The first limitation involves our sample: we focused on a select set of courses hosted on China’s icourse.163 MOOC platform. Future studies should involve an expansive collection of courses to provide a more holistic understanding of how the pandemic has influenced online interaction. Second, we only explored the interactive relationship between learners and did not analyze interactive content. More in-depth content analysis should be carried out in subsequent research. All in all, the emergence of COVID-19 has provided a new path for online learning and has reshaped the distance learning landscape. To cope with associated challenges, educational practitioners will need to continue innovating in online instructional design, strengthen related pedagogy, optimize online learning conditions, and bolster teachers’ and students’ competence in online learning.

• View Article
• Google Scholar
• PubMed/NCBI
• 30. Serrat O. Social network analysis. Knowledge solutions: Springer; 2017. p. 39–43. https://doi.org/10.1007/978-981-10-0983-9_9
• 33. Rong Y, Xu E, editors. Strategies for the Management of the Government Affairs Microblogs in China Based on the SNA of Fifty Government Affairs Microblogs in Beijing. 14th International Conference on Service Systems and Service Management 2017.
• 34. Hu X, Chu S, editors. A comparison on using social media in a professional experience course. International Conference on Social Media and Society; 2013.
• 35. Bydžovská H. A Comparative Analysis of Techniques for Predicting Student Performance. Proceedings of the 9th International Conference on Educational Data Mining; Raleigh, NC, USA: International Educational Data Mining Society2016. p. 306–311.
• 40. Olivares D, Adesope O, Hundhausen C, et al., editors. Using social network analysis to measure the effect of learning analytics in computing education. 19th IEEE International Conference on Advanced Learning Technologies 2019.
• 41. Travers J, Milgram S. An experimental study of the small world problem. Social Networks: Elsevier; 1977. p. 179–197. https://doi.org/10.1016/B978-0-12-442450-0.50018–3
• 43. Okamoto K, Chen W, Li X-Y, editors. Ranking of closeness centrality for large-scale social networks. International workshop on frontiers in algorithmics; 2008; Springer, Berlin, Heidelberg: Springer.
• 47. Ding Y, Yang X, Zheng Y, editors. COVID-19’s Effects on the Scope, Effectiveness, and Roles of Teachers in Online Learning Based on Social Network Analysis: A Case Study. International Conference on Blended Learning; 2021: Springer.
• 64. Boys C, Brennan J., Henkel M., Kirkland J., Kogan M., Youl P. Higher Education and Preparation for Work. Jessica Kingsley Publishers. 1988. https://doi.org/10.1080/03075079612331381467

How Effective Is Online Learning? What the Research Does and Doesn’t Tell Us

• Share article

Editor’s Note: This is part of a series on the practical takeaways from research.

The times have dictated school closings and the rapid expansion of online education. Can online lessons replace in-school time?

Clearly online time cannot provide many of the informal social interactions students have at school, but how will online courses do in terms of moving student learning forward? Research to date gives us some clues and also points us to what we could be doing to support students who are most likely to struggle in the online setting.

The use of virtual courses among K-12 students has grown rapidly in recent years. Florida, for example, requires all high school students to take at least one online course. Online learning can take a number of different forms. Often people think of Massive Open Online Courses, or MOOCs, where thousands of students watch a video online and fill out questionnaires or take exams based on those lectures.

In the online setting, students may have more distractions and less oversight, which can reduce their motivation.

Most online courses, however, particularly those serving K-12 students, have a format much more similar to in-person courses. The teacher helps to run virtual discussion among the students, assigns homework, and follows up with individual students. Sometimes these courses are synchronous (teachers and students all meet at the same time) and sometimes they are asynchronous (non-concurrent). In both cases, the teacher is supposed to provide opportunities for students to engage thoughtfully with subject matter, and students, in most cases, are required to interact with each other virtually.

Coronavirus and Schools

Online courses provide opportunities for students. Students in a school that doesn’t offer statistics classes may be able to learn statistics with virtual lessons. If students fail algebra, they may be able to catch up during evenings or summer using online classes, and not disrupt their math trajectory at school. So, almost certainly, online classes sometimes benefit students.

In comparisons of online and in-person classes, however, online classes aren’t as effective as in-person classes for most students. Only a little research has assessed the effects of online lessons for elementary and high school students, and even less has used the “gold standard” method of comparing the results for students assigned randomly to online or in-person courses. Jessica Heppen and colleagues at the American Institutes for Research and the University of Chicago Consortium on School Research randomly assigned students who had failed second semester Algebra I to either face-to-face or online credit recovery courses over the summer. Students’ credit-recovery success rates and algebra test scores were lower in the online setting. Students assigned to the online option also rated their class as more difficult than did their peers assigned to the face-to-face option.

Most of the research on online courses for K-12 students has used large-scale administrative data, looking at otherwise similar students in the two settings. One of these studies, by June Ahn of New York University and Andrew McEachin of the RAND Corp., examined Ohio charter schools; I did another with colleagues looking at Florida public school coursework. Both studies found evidence that online coursetaking was less effective.

About this series

This essay is the fifth in a series that aims to put the pieces of research together so that education decisionmakers can evaluate which policies and practices to implement.

The conveners of this project—Susanna Loeb, the director of Brown University’s Annenberg Institute for School Reform, and Harvard education professor Heather Hill—have received grant support from the Annenberg Institute for this series.

To suggest other topics for this series or join in the conversation, use #EdResearchtoPractice on Twitter.

Read the full series here .

It is not surprising that in-person courses are, on average, more effective. Being in person with teachers and other students creates social pressures and benefits that can help motivate students to engage. Some students do as well in online courses as in in-person courses, some may actually do better, but, on average, students do worse in the online setting, and this is particularly true for students with weaker academic backgrounds.

Students who struggle in in-person classes are likely to struggle even more online. While the research on virtual schools in K-12 education doesn’t address these differences directly, a study of college students that I worked on with Stanford colleagues found very little difference in learning for high-performing students in the online and in-person settings. On the other hand, lower performing students performed meaningfully worse in online courses than in in-person courses.

But just because students who struggle in in-person classes are even more likely to struggle online doesn’t mean that’s inevitable. Online teachers will need to consider the needs of less-engaged students and work to engage them. Online courses might be made to work for these students on average, even if they have not in the past.

Just like in brick-and-mortar classrooms, online courses need a strong curriculum and strong pedagogical practices. Teachers need to understand what students know and what they don’t know, as well as how to help them learn new material. What is different in the online setting is that students may have more distractions and less oversight, which can reduce their motivation. The teacher will need to set norms for engagement—such as requiring students to regularly ask questions and respond to their peers—that are different than the norms in the in-person setting.

Online courses are generally not as effective as in-person classes, but they are certainly better than no classes. A substantial research base developed by Karl Alexander at Johns Hopkins University and many others shows that students, especially students with fewer resources at home, learn less when they are not in school. Right now, virtual courses are allowing students to access lessons and exercises and interact with teachers in ways that would have been impossible if an epidemic had closed schools even a decade or two earlier. So we may be skeptical of online learning, but it is also time to embrace and improve it.

A version of this article appeared in the April 01, 2020 edition of Education Week as How Effective Is Online Learning?

Sign Up for EdWeek Tech Leader

Edweek top school jobs.

Sign Up & Sign In

Reflections on Online Teaching amidst the COVID-19 pandemic | Blog | IED, Pakistan | The Aga Khan University   //

• Our Leadership
• Our Faculty
• Areas of Focus
• Network of Professionals
• In the Media
• Work for Us
• Giving Opportunities
• Bachelor of Education
• MPhil in Education
• PhD in Education
• Financial Assistance
• Frequently Asked Questions
• Continuing Professional Education
• Publications
• Teaching License Policy
• 12th International Conference
• Kazim Bacchus Research Seminars
• Pandemic Perspectives
• AKU-IED Libraries
• Development Projects
• ​Professional Development Centre, Karachi
• ​Professional Development Centre, North
• ​Professional Development Centre, Chitral
• Aanchal ECD Podcast
• AKU-IED Blogs
• Bite-sized Maths
• Climate Change & Environmental Sustainability
• Conversations with the Dean
• Doctoral Lecture Series
• Public Lecture Series
• Education in COVID-19

​Reflections on online teaching amidst the COVID pandemic 

The beginning​.

Entering the Maze of Online Teaching: The Why and Whereof

The Key to the Maze

I tried game-based activities like Kahoot, Mentimeter, Quizziz and Nearpod.  I used whiteboard and annotations on Zoom, cloud on Mentimeter and Padlet’s collaborative boards. My students enjoyed working in break-out rooms, doing virtual seminars, and tele-simulations. I also tried virtual labs to teach students assessment skills, making home-based task trainers for practising lung examination (I showed them how to make a body torso by wrapping a cloth piece on a 19-gallon water bottle, and drawing anatomical landmarks on it). The students re-demonstrated the learnt assessment skills on their handmade torso while I gave them feedback online. It became a jointly exciting endeavour. In fact, now that we have learnt the basics, I am willing to do it again with more confidence, hopefully with fewer trials and tribulations.

Surviving the Maze: The Indelible Moments

No matter how and where I conducted online classes from, there was this additional uncertainty of whether real students are sitting behind the screen or were those just on-screen selfies. How can I forget those awkward instances: somebody calling you in the background, children crying and fighting, hard- of- hearing parents calling to check if you are still online, doorbells ringing, hawkers announcing their arrival in neighborhood, pets and stray birds seeking your attention; and of course, the sound of a toilet flushing which served as an embarrassing reminder of the need for an urgent ‘break time’; instances that cause untold mortification in the moment and are only humorous in retrospect. 

Recalling the stories of my remote students is the most sobering of memories. These students were unable to access university learning management system, due to no internet connectivity or adequate bandwidth at their homes. They had to travel long distances, request for escorts, arrange transport to reach the local hub campuses, for downloading e-resources, and transferring files into their USBs. Learning was a painstaking journey for them. Driven by the need for equity and fairness, I sent voice messages to them on their phones, held calls in the middle of night when connections were better so they could share their queries on my recorded lectures.

Continuing the Journey 

A call for action

Let us innovate, put our thinking caps on, and redesign. Do not just go back to face-to-face teaching only nor struggle with the isolated fully virtual classes but create a blend of synchronous classes with asynchr​onous activities. For teaching practical skills, we can offer face- to-face value-based sessions with a combination of demonstrations, virtual and tele-simulation learning. Let us transform the future of education. Let us redesign education for this era. We have stayed within the status quo for too long. I am for the blended bichronous mode of teaching: 2021 and beyond!  ​

About the author

Shanaz Cassum is an Assistant Professor at the Aga Khan University School of Nursing and Midwifery. She is an inaugural member of the AKU Teachers’ Academy, and a Fellow of the Higher Education Academy, UK. She received the Award for Innovation in Teaching Practice in recognition of her outstanding teaching and academic pursuits at AKU’s Global Convocation 2021. 

• Work & Careers
• Life & Arts
• Currently reading: FT executive education rankings highlight comeback of online learning
• Learning to mind the gaps in multigenerational workplaces
• Charting the course of executive education
• AI is already changing management — companies must decide how
• Canada’s First Nations people and business schools partner on leadership
• Thirst for AI knowledge drives growth in executive courses
• The leadership courses aiming to change women and workplaces

FT executive education rankings highlight comeback of online learning

• FT executive education rankings highlight comeback of online learning on x (opens in a new window)
• FT executive education rankings highlight comeback of online learning on facebook (opens in a new window)
• FT executive education rankings highlight comeback of online learning on linkedin (opens in a new window)
• FT executive education rankings highlight comeback of online learning on whatsapp (opens in a new window)

Andrew Jack

Simply sign up to the Business education myFT Digest -- delivered directly to your inbox.

Companies have asked for a growing share of executive education courses to be delivered online in recent months, in a reversal of a post-pandemic trend to return to in-person training, according to the latest FT rankings of leading business schools.

The proportion of tailored “custom” programmes provided online by schools to corporate clients rose sharply from 19 per cent in 2022 to 30 per cent last year. A further 22 per cent of courses were offered in “blended” format, with in-person and online components.

Insead, based in France and Singapore, was ranked first by the FT among 90 providers of custom executive education courses , ahead of Iese of Spain, IMD of Switzerland, and Duke Corporate Education of the US.

Executive Education Rankings 2024

Read the rankings of custom and open-enrolment programmes

Among the 80 schools providing open enrolment courses , France’s HEC Paris returned to top place, ahead of Iese and Esade in Spain and London Business School in the UK.

Ilya Breyman, head of Coursalytics, a company that analyses the executive education market, said: “Longer programmes must incorporate online elements to maintain student engagement. This shift is partly a consequence of the Covid-19 pandemic, which necessitated the adoption of online learning, making it a new reality in education.”

Data from his analysis suggested that 38 per cent of open enrolment programmes were provided online or in blended format among leading business schools, rising to 50 per cent among those in North America.

Winfried Ruigrok, former dean of the Executive School of Management, Technology & Law at the University of St Gallen in Switzerland, reports a nuanced picture. “The vast majority of clients want to go back to face-to-face, but we occasionally offer blended learning,” he said. “Clients always ask if you provide it online and schools have to be able to deliver it in ways that are attractive.”

Demand for non-diploma business courses for executives remains strong, notably on topics around leadership, sustainability and digital transformation . Josh Bersin, a human resources adviser, said: “Executive education is still a big market. It’s seen almost as a reward. People also want the brand of a business school on their resume.”

However, business schools have been forced to step up innovation and adapt to competition from commercial training companies. Sharmla Chetty, chief executive of Duke CE, said: “Our clients want practical and applied insights, and co-creating performance metrics with us. They are saying that, now we are adopting AI, what about you?”

Antoine Poincaré, vice-president of training at AXA Climate, a subsidiary of the French insurer which provides training on sustainability, said the alternative provider’s approach had received strong demand from companies. He focuses on providing specialist insights into the specific economic sectors in which his clients operate.

But Ruigrok at St Gallen said many business schools were more broadly holding their own in competition with non-academic rivals. “The others offer customer proximity and people who know how to deliver,” he said. “What they often lack is depth, independence and the ability to issue official paper. I don’t have the impression that consultants are always winning.”

Promoted Content

Explore the series.

Follow the topics in this article

• Business education Add to myFT
• Switzerland Add to myFT
• France Add to myFT
• Spain Add to myFT
• Andrew Jack Add to myFT

International Edition

• Skip to Navigation
• Skip to Main Content
• Skip to Related Content
• Today's news
• Reviews and deals
• Climate change
• 2024 election
• Fall allergies
• Health news
• Mental health
• Sexual health
• Family health
• So mini ways
• Unapologetically
• Buying guides

Entertainment

• How to Watch
• My watchlist
• Stock market
• Biden economy
• Personal finance
• Stocks: most active
• Stocks: gainers
• Stocks: losers
• Trending tickers
• World indices
• US Treasury bonds
• Top mutual funds
• Highest open interest
• Highest implied volatility
• Currency converter
• Basic materials
• Communication services
• Consumer cyclical
• Consumer defensive
• Financial services
• Industrials
• Real estate
• Mutual funds
• Credit cards
• Credit card rates
• Balance transfer credit cards
• Business credit cards
• Cash back credit cards
• Rewards credit cards
• Travel credit cards
• Checking accounts
• Online checking accounts
• High-yield savings accounts
• Money market accounts
• Personal loans
• Student loans
• Car insurance
• Home buying
• Options pit
• Investment ideas
• Research reports
• Fantasy football
• Pro Pick 'Em
• College Pick 'Em
• Fantasy baseball
• Fantasy hockey
• Fantasy basketball
• Download the app
• Daily fantasy
• Scores and schedules
• GameChannel
• World Baseball Classic
• Premier League
• CONCACAF League
• Champions League
• Motorsports
• Horse racing
• Newsletters

New on Yahoo

• Privacy Dashboard

• Yahoo Sports AM
• College Sports
• Fantasy Sports
• Horse Racing
• Scores/Schedule
• Playoff and Bowl Games
• Pacers lose Haliburton in Game 2
• Barcelona sacks Xavi Hernandez
• Logan Paul files defamation suit
• Charles Barkley calls TNT leaders 'clowns'
• Officer disciplined in Scheffler incident

NCAA sidelines Fridley student-athlete's football dreams over online pandemic class

(FOX 9) - A local student-athlete is fighting for his football dreams after the NCAA sidelined him over an online English class he took during the height of the pandemic.

Fridley High School graduate Terrell Davis tells FOX 9, it is not fair to be punished for a misunderstanding over his high school transcript, three years later. He hopes to get back on the gridiron in time for summer workouts.

"I am kind of frustrated. I am kind of sad," Davis said. "But at the end of the day, you got to keep your head up high. You got to motivate yourself. And the grind does not stop. I continue to get 1% better every day."

Davis’ dreams of playing Division One football were about to come true as a wide receiver for the University of New Mexico Lobos until the recent developments.

"I really, really want it bad. I just hope everything gets cleared because I just really want to touch the field with the guys," said Davis. "It would feel really, really nice for me to go out there with the ‘Bos and hopefully go to a bowl game and win a championship. That is the goal."

Davis graduated from Fridley High School with the class of 2021. He kept trying out for the UNM football team, finally making it with a roster invitation this spring when a new head coach was hired. It was his third attempt to play as what’s known as a "walk-on."

"I tried every year. I tried out every single year, and this is the year I actually made it. And when I made it, the coaches loved me."

Davis appeared to be thriving during the offseason with his acrobatic pass-catching highlighted on the Lobos’ public Instagram account. But in recent days, Davis has been sidelined with an academic eligibility issue involving his high school transcript and the NCAA.

"It was literally one class, literally one class. It was ling and lit (linguistics and literature) A and B," explained Davis.

The way Davis and his family tell it, the college sports governing body flagged the online English class he took during the height of the pandemic. With his grandmother battling cancer, Davis elected for online learning to avoid contact with others. So, he was stunned when academic compliance officers on the Albuquerque campus told him he would be ineligible to play for the Lobos this fall unless they could sort out the issue.

"I am kind of frustrated, I am kind of sad," he said. "But at the end of the day, you got to keep your head up high."

Adding to his frustrations, Davis reports that he is carrying at least a 3.0 grade point average as a condition of an academic scholarship he has received from the University of New Mexico, where he is majoring in business finance and carrying an economics minor. After two full years on campus, he argues his classroom success in college should be enough to satisfy any NCAA academic requirements.

A frantic effort with the high school, the university and the NCAA was underway this week as Davis desperately wants to be part of summer practices kicking off in June, so he can solidify his hold on a coveted roster spot and prepare for the season.

"When you get so close to something, like, it is just like, ‘wow,’ and then it gets taken away for something you cannot control. That is sad. It is not even about skill or anything. It is something you cannot control," Davis lamented.

FOX 9 spoke to an NCAA eligibility center official on Thursday. While she would not confirm specifics of why Davis’ high school transcript was flagged, she said the association is working with the university and Fridley High School to resolve the issue. A NCAA review of Davis’ file is currently underway with clarity on the young man’s football future expected sometime next week.

Recommended Stories

Rangers are stalling, james wood getting close, orioles-white sox controversial ending & harper’s prom-posal.

Jake Mintz & Jordan Shusterman discuss the Rangers not playing so hot this season, super prospect James Wood getting closer to the bigs, the controversial ending in the White Sox-Orioles game and Bryce Harper’s prom-posal help.

Rich Paul says Bronny James will not sign two-way NBA contract: 'Teams know that. I'm not doing that'

James is expected to be a second-round pick in June's NBA Draft.

AEW Double or Nothing predictions, preview: Mercedes Moné says 'I'm ready to go' ahead of in-ring debut for Tony Khan's company

Mercedes Moné will make her AEW in-ring debut at 'Double or Nothing.' It's also her first match since suffering a severe ankle injury in 2023.

2024 Kicker rankings for fantasy football

The Yahoo Fantasy football analysts reveal their first kicker rankings for the 2024 NFL season.

2024 Indy 500: TV channels, streaming, starting grid, news, weather more

The "Greatest Spectacle in Racing" is scheduled to take place Sunday with the 108th running of the Indianapolis 500.

Travis Kelce on Harrison Butker's controversial speech: 'When it comes down to his views ... those are his'

Kelce addressed Butker's comments on this week's episode of the "New Heights" podcast with his brother Jason.

Logan Paul announces defamation lawsuit against Ryan Garcia, later claims boxer fabricated 'kids in Gaza' text

Ryan Garcia's month has gone off the rails.

White Sox discover yet another way to lose a game: Infield fly rule interference

It is apparently possible to interfere with an infield fly.

Kayla Harrison's UFC jump, Conor McGregor's BKFC stake highlight PFL's core challenge of competing with an MMA giant

Seems like maybe PFL CEO Donn Davis felt a little more stung by the recent departure of Kayla Harrison than we first realized.

Peyton Manning has 1 wish for drafted QBs: Break his rookie interception record

The Hall of Famer was joking. We think.

Charles Barkley calls TNT leaders 'clowns,' suggests his production company could take over 'Inside The NBA'

Charles Barkley wants to keep the crew together.

Nine things we can't wait for next NFL season, Lamar's weight loss, possible 18th game | Zero Blitz

Jason Fitz, Charles Robinson and Frank Schwab talk about what they’re most looking forward to in the 2024 NFL season, as well as taking a look at Lamar Jackson’s weight loss, the NFL possibly adding an 18th game and Aaron Rodgers almost taking a VP nomination.

Gymnast Oksana Chusovitina's quest to qualify for record 9th Olympics at age 48 ends with injury

This may be the end for Chusovitina's Olympic career, but she made her mark on the sport.

10 ADPs to watch this summer: Dolphins RBs + Chiefs WRs have major questions | Yahoo Fantasy Football Show

What a player's ADP is heading into Memorial Day weekend is likely not what it could be heading into Labor Day weekend. But with the start of summer fast approaching, there's no better to identify the ADPs that are worth monitoring over the next few months. Fantasy football expert Tera Roberts joins Matt Harmon to identify 10 ADP situations you need to keep an eye on this summer.

Mavericks take Game 1, Cavaliers fire J.B. Bickerstaff & 76ers plan to pursue third star | No Cap Room

Dan Devine and Jake Fischer recap the action from Game 1 of the Western Conference Finals, the Cavaliers firing J.B. Bickerstaff and the 76ers plans to pursue another star this offseason

Antonio Brown files for bankruptcy despite a reported $80 million in NFL career earnings

Former NFL receiver Antonio Brown filed for bankruptcy in federal court as he reportedly owes nearly $3 million to creditors. Brown made a reported $80 million during his 12-year career.

NBA reportedly closes investigation into Josh Giddey over alleged relationship with minor

Newport Beach police said in January Giddey wouldn't face criminal charges.

West Ham's Lucas Paquetá charged by English FA for allegedly breaching betting rules

The charges are related to four matches over his first two seasons with the club.

MVP Nikola Jokić headlines All-NBA teams alongside Shai Gilgeous-Alexander, Luka Dončić

The All-NBA selection was the sixth for Jokić, who was awarded his third MVP trophy in four seasons on May 8.

Chiefs' Patrick Mahomes, Andy Reid stand by Harrison Butker after controversial commencement address

"We're not always going to agree, and there's certain things that he said that I don't necessarily agree with, but I understand the person that he is," Mahomes said.

Commencement 2024

• Graduate profiles
• Climate Solutions

Celebrating the Class of 2024

Reflecting on a joyous day

Graduates gathered with family, classmates, and faculty in Harvard Yard to celebrate, reminisce, and mark the University’s 373rd Commencement.

Honorary degrees

Recipients include an educator, a conductor, a theoretical physicist, an advocate for the elderly, a writer, and a Nobel laureate.

Baccalaureate

In his Baccalaureate address, interim President Garber praised the fortitude and resilience of the Class of 2024.

Reserve Officers' Training Corps ceremony

Harvard’s 23 new military officers took their oath of service in front of friends and family.

Class Day ceremonies

Class Day speakers, including leaders in business and government, offered graduates perspective and inspiration.

Photos from the day

Caps, gowns, joy, and bright futures

Stephanie Mitchell/Harvard Staff Photographer

Coming together to celebrate the hard work and scholarship of the Class of 2024.

Niles Singer/Harvard Staff Photographer

Harvard Kennedy School graduates celebrate with inflatable globes.

An inspiring message from a Harvard Graduate School of Education student.

Students pose in Tercentenary Theatre.

Kris Snibbe/Harvard Staff Photographer

Harvard T.H. Chan School of Public Health graduates celebrate with clappers.

Honorary degree recipients Gustavo Adolfo Dudamel Ramírez, Jennie Chin Hansen, Sylvester James Gate Jr., and Joy Harjo-Sapulpa during the ceremony.

Jon Chase/Harvard Staff Photographer

Harvard Law School students celebrating graduation with gavels.

Introducing the graduates

Meet our graduating class of 2024

More from the celebration

"This is the tipping-point year"

Learn more about Maria Ressa, the 2021 Nobel Peace Prize winner and principal speaker for Harvard’s 373rd Commencement.

The 373rd Commencement began with the ringing of bells

Sonia Sotomayor is the 2024 Radcliffe Medal recipient

The Class of 2024 got a do-over on the prom that the pandemic took away

The Harvard Medal was awarded to three extraordinary community members

Student orators spoke about equity, uncertainty, and connection

School events

Harvard College and the graduate and professional Schools host additional commencement events unique to their communities.

Harvard College

Harvard Business School

Harvard Divinity School

Harvard Extension School

Harvard Graduate School of Design

Harvard Graduate School of Education

Harvard John A. Paulson School of Engineering and Applied Sciences

Harvard Kennedy School

Harvard Kenneth C. Griffin Graduate School of Arts and Sciences

Harvard Law School

Harvard Medical School

Harvard School of Dental Medicine

Harvard T.H. Chan School of Public Health