case study in language learning

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Case study research: Making language learning complexities visible - 2020

Duff, P. (2020). Case study research: Making language learning complexities visible -. In J. McKinley & H. Rose (Eds.), The Routledge handbook of research methods in applied linguistics (pp. 144-153). New York: Routledge. ABSTRACT: Case study research has played a crucial role in the development of the field of applied linguistics especially in the subdomains of second language (L2) acquisition, L2 writing, L2 teacher development and identity, transnationalism and multilingualism, and (L2) academic discourse socialization, among other areas. This chapter documents how and why case studies in our field have evolved in recent decades and the implications of such changes for contemporary case study research. Examples of recent case studies drawn from the broad field of second language studies illustrate such changes as well as enduring, core features and advantages of this methodological and rhetorical approach to research and scholarship. In addition, the chapter provides recommendations for researchers wishing to conduct case study research either on its own or in conjunction with other approaches, and criteria by which case study research can be assessed.

Related Papers

Tim Anderson

Patricia (Patsy) Duff

Duff, P. (2014). Case study research on language learning and use. Annual Review of Applied Linguistics, 34, 233-255. doi:10.1017/S0267190514000051. (*Note page numbers on the downloadable version are not correct). ABSTRACT Case study research has played a very important role in applied linguistics since the field was established, particularly in studies of language teaching, learning,and use. The case in such studies generally has been a person (e.g., a teacher,learner, speaker, writer, or interlocutor) or a small number of individuals on their own or in a group (e.g., a family, a class, a work team, or a community of practice). The cases are normally studied in depth in order to provide an under-standing of individuals’ experiences, issues, insights, developmental pathways,or performance within a particular linguistic, social, or educational context. Rather than discuss constructs, hypotheses, and findings in terms of statistical patterns or trends derived from a larger sample or survey of a population of language learners, as in some quantitative research, a qualitative case study of a person presents a contextualized human profile. Case study has contributed substantially to theory development, generating new perspectives or offering a refutation or refinement of earlier theories in applied linguistics by analyzing linguistic, cultural, and social phenomena associated with children, adolescents, young adults, and older adults. In recent years, the purview of case studies in applied linguistics has expanded to include many previously underrepresented topics, linguistic situations, theoretical perspectives, and populations. This article provides an overview of some traditional areas of coverage and then newer foci in terms of methodology, thematic areas, and findings pertaining to language learners in transnational, multilingual, and diaspora contexts especially.

Etain Casey

Matthew Bronson

Patricia (Patsy) Duff , Tim Anderson

Duff, P. & Anderson, T. (2016). In JD Brown & C. Coombs (Eds.), The Cambridge guide to research in language teaching and learning (pp. 112-118). Cambridge, UK: Cambridge University Press.

Adolfo Tanzi Neto

Language and Education

Nancy Hornberger

Second Language Research

Larry Selinker

We suggest a combined research methodology for studying SLA in real-life and important contexts, a methodology sensitive to the demands of such contexts. For studying IL learning in context, our suggested methodology combines and integrates aspects from three fields: grounded ethnography in ethnomethodol ogy ; subject-specialist informant procedures in language for specific purposes; and rhetorical/grammatical strategies in discourse analysis. We first present evidence for the importance of devising a research approach to contexually based SLA. Then we sketch the suggested research methodology and present two extended case studies which illustrate the methodology. We view such research as complimentary to universal approaches to core IL grammar. Finally, we sketch our theoretical approach, showing a possible link between research in universal and contextually-based SLA.

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Duolingo PLG Strategy: The Growth Flywheel of Learning a New Language 

• Growth Marketing

Duolingo is one of the most popular and widely used language-learning platforms, with over 500 million total users worldwide, offering language courses ranging from French to Catalan. 

What you may not know is that while many companies grow through paid marketing, Duolingo actually acquired ~80% of their users organically. More specifically, Duolingo did this by leveraging Product-led Growth as a lever to expand its consumer base — providing an interactive and gamified approach to language learning, making it accessible to a broad range of users, from beginners to more advanced learners.

Product-led growth is a go-to-market strategy that relies on the company to create and iterate on a product that turns customers into loyal fans who will then introduce the product to their friends, creating a viral growth loop. This approach prioritizes creating a great product that solves a real need for customers; relying on the product to drive word-of-mouth referrals and organic growth. It is an incredibly powerful strategy as current advocates for the product help generate a growth loop of new users that distinctly reduces customer acquisition costs, especially for startups.

Duolingo’s product-led growth was based on 3 main tenets:

Gamification

• Community-based learning

A learning-driven team obsessed with A/B testing

One of the biggest and most important PLG levers for Duolingo is gamification. With learning anything, the teacher-student bond and internal motivation to reach a better understanding of the subject are key factors in a student’s learning journey. In order to replicate this, Duolingo heavily depends on gamification to not only make the language-learning experience enjoyable for users but also create a sense of achievement and motivation that keeps users coming back regularly, in turn increasing user engagement and retention.

As part of their growth, the team at Duolingo very quickly and smartly saw parallels between the entertainment that gaming apps provided and the consistent learning required for languages. Top digital games, in general, had much higher retention rates across industries. Seeking early inspiration from games like Angry Birds and Clash Royale, Duolingo incorporated key gamification elements, such as points, streaks, and rewards, to make the learning process fun and addictive. 

To start off, Duolingo rewards users with XP (Experience Points) and gems, the game’s in-app currency for completing lessons, exercises, and daily challenges. Earning experience points is a physical representation of a user’s progress, giving learners a more concrete sense of accomplishment which in turn motivates them to continue learning to increase their scores. Gems, on the other hand, gamify the learning experience even further since it acts as a virtual currency used to repair lost streaks, refill hearts, and unlock bonus lessons or content. 

An important piece of PLG that Duolingo tapped into within their product was the idea of instant gratification. Duolingo learners can correct courses in real time, building a robust foundation for their language skills. Not only is a user’s learning instantaneously reinforced through immediate feedback on exercises, which is a proven tactic in habit formation and learning; but their commitment to learning immediately sees reward via these points and gems. Layering this on with additional product features such as levels, progress bars, and leagues, these features provided learners with visual feedback and a visceral representation of their learning journey as they completed lessons and exercises.

Additionally, Duolingo offers a variety of mini-challenges and badges for completing specific tasks or milestones, such as mastering a skill or reaching a certain level. These virtual rewards transformed mastering a language from a daunting task with benefits that can only be reaped long term into a simple, fun rewarding game that provided users with an immediate boost of serotonin as they feel a sense of accomplishment. Seeking inspiration from how gamification was able to boost their engagement and retention rates beyond what they had imagined, the Duolingo team was now inspired to build a growth engine with gamification at its core. 

While Duolingo had its “leveling up” model figured out, it now had to integrate gamification into every nook and cranny of its app. Enter Streaks. As part of the company’s growth, retention was a key metric that Duolingo was hyperfocused on. It was crucial for Duolingo’s business model, since it was based on ads and subscriptions, that users keep coming back to their application and use it as much as possible. Duolingo encouraged daily practice by implementing streaks. Users are rewarded for consecutive days of learning, and breaking a streak can serve as a disincentive for missing a day.

This keeps learners engaged and committed to regular practice. As part of the gamification of streaks, Duolingo used lifecycle marketing to implement a key growth tool in the form of streak-saver notification— a notification at the end of every day that alerted users if they were about to lose their streak. This late-night notification proved to have considerable upside. Students who were having trouble committing a language to practice in their everyday lives saw their commitment to learning reignited with this simple feature alone. With this, Duolingo shortly doubled down on several improvements around streaks, including calendar views of the number of days in a streak achieved, animations, streak freezes, and rewards. Each generated substantial improvements in user engagement and retention. 

Community-Driven Learning 

Duolingo may offer courses in over 40 languages, but the app itself has to speak in a universal tongue with an experience that transcends any cultural or linguistic barriers. One of the first challenges of PLG that Duolingo faced was rebuilding the crucial classroom-teacher-student rigor and community that helps students stay motivated to learn, as it would have a direct impact on retention within the app. While most language courses are top-down curriculums, Duolingo had to completely rethink language learning from the bottom up, with PLG at the core of the curriculum product features that they rolled out. 

The first thing that Duolingo did was to focus their users on bite-sized mastery. Duolingo ingeniously distills language learning into digestible chunks, eliminating the intimidation often associated with mastering a new language. Instead of being just another dry task that a user had to cross off their checklist each day, each lesson is a mini-adventure, a game of linguistic discovery that keeps users engrossed. Beyond leveling up and competing against themselves, Duolingo, more importantly, realized the effect of community and how current advocates of the app could influence and encourage new users to continue learning on the app.

Duolingo started to opt users into leagues where users were able to compete with other learners. Users could progress to the top of the first league by merely engaging consistently in their regular language study. They also had the opportunity to move through a series of league levels (Bronze, Silver, and Gold league) that let them compete against varying segments of engaged users, which granted them a greater sense of progress and reward – another integral element that the Duolingo team borrowed from game design.

By employing leaderboards, Duolingo not only fostered a greater sense of a learning community but also fueled a friendly rivalry among learners. The sense of community within the app served the function of recreating the classroom-teacher-student emotional bond, but the competitive edge added another layer of engagement to the user experience. 

To take community engagement further, Duolingo continued fostering a sense of community among users through discussion forums and language clubs, allowing learners to connect with others who share their language-learning goals. Throughout the app, Duolingo also makes sure to embed push notifications and prompts encouraging users to share their language-learning achievements and progress on social media platforms like Instagram and Twitter. This word-of-mouth marketing through social sharing helps attract new users and promotes the platform’s positive reputation.

Duolingo created a machine that fed learners with serotonin and reinforced the positive impact of users continuously coming back to the app as engaged users moved up to more competitive leagues week after week. The realization that Duolingo could borrow from gaming instead of simply benchmarking itself against other edtech companies enabled the team to experiment with new approaches to learning.

The ideal PLG loop is led by product, powered by data, and assisted by channels. From the get-go, the Duolingo team knew that users do not know what they want — rather, well-thought-out hypotheses backed by data were the key foundations in order to achieve the growth they needed. Testing is a game of instinct, and the Duolingo team knew that they had to constantly iterate and use quick AB testing to drive growth.

One of the first things that Duolingo AB tested was its soft-walled signup page. Duolingo realized that while it could acquire users, it experienced a major leak at the very onset — the sign-up page. However, it was easy to retain users once they got into the learning experience. Instead, they experimented with the concept of soft-walled signup pages where users could begin learning instantly but encountered soft walls in the form of optional signup screens that could be skipped to continue learning.

With this experiment alone, the Duolingo team managed to increase DAUs by 20%. In addition, their freemium model, where users do not incur any upfront cost, enables users to access a significant portion of the platform’s features and content right away for free, which further encourages user acquisition and engagement. Product usage then becomes the primary lever for product education and user nurturing which is much more powerful and direct. This anchors the Duolingo team on pushing for quick activation and meaningful product usage, which are the key success factors for generating PQLs.

Another key area that Duolingo experimented heavily in was Push Notifications. Well known for its official mascot, an evergreen-colored owl Duo, Duolingo had the perfect excuse in the form of their mascot, which they positioned as their learner’s #1 fan and biggest cheerleader who isn’t shy about checking in or laying on a guilt trip. Openly addressed by Duolingo in their tone and voice documentation of Duo, Duo is a very persistent mascot whose “only thing he loves more than Duolingo is seeing our learners succeed.”

With that, Duolingo created the perfect facade to call users back. But its real power move was in its intentional goal setting at the heart of streaks, where learners were able to select their goal of how committed they were to learning each day. With learners giving Duolingo a reason to call them back, Duo was now able to double down on its notifications.

Duolingo didn’t just send notifications — they conducted a simple A/B test of adding a tiny red dot to the app icon when folks had been notified to practice, which went on to demonstrate a 1.5% increase in DAU.

Duolingo’s product-led growth strategy, which emphasizes gamification, community-based learning, and data-driven A/B testing, has been instrumental in helping the company acquire millions of users who are not only engaged but are firm advocates of the platform. By implementing Product Led Growth tactics that tapped directly into common human motivators of competitiveness and progression, Duolingo has not only made the language-learning experience enjoyable but also created a sense of achievement, competition, and motivation that keeps users engaged and returning to the platform regularly — truly creating the world’s best free,  fun, and effective way to learn a language! 

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Language Acquisition vs. Language Learning

• First Online: 01 January 2014

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 Language learning is the teaching about a language (its use, its speaker, its structure), with the hope that the student will learn enough to actually be able to speak the target language. Language acquisition, in its current sense, tries to expose the student to the target language in meaningful ways so that he or she acquires the language’s structure through actual use. Language learning follows from the official language model, whereas Native American languages are taught as foreign language in their own native community. This helps to commodify the heritage language; makes studying it an artificial exercise; and occupies the time, money, and effort of the population that could be better spent in doing language acquisition and achieving real results of the revitalization of their heritage language.

“If you talk to a man in a language he understands, that goes to his head. If you talk to him in his own language, that goes to his heart.” —Nelson Mandela

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Shaul, D.L. (2014). Language Acquisition vs. Language Learning. In: Linguistic Ideologies of Native American Language Revitalization. SpringerBriefs in Anthropology(). Springer, Cham. https://doi.org/10.1007/978-3-319-05293-9_3

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Computer Science > Machine Learning

Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field.

Title: Advancing Real-time Pandemic Forecasting Using Large Language Models: A COVID-19 Case Study

Abstract: Forecasting the short-term spread of an ongoing disease outbreak is a formidable challenge due to the complexity of contributing factors, some of which can be characterized through interlinked, multi-modality variables such as epidemiological time series data, viral biology, population demographics, and the intersection of public policy and human behavior. Existing forecasting model frameworks struggle with the multifaceted nature of relevant data and robust results translation, which hinders their performances and the provision of actionable insights for public health decision-makers. Our work introduces PandemicLLM, a novel framework with multi-modal Large Language Models (LLMs) that reformulates real-time forecasting of disease spread as a text reasoning problem, with the ability to incorporate real-time, complex, non-numerical information that previously unattainable in traditional forecasting models. This approach, through a unique AI-human cooperative prompt design and time series representation learning, encodes multi-modal data for LLMs. The model is applied to the COVID-19 pandemic, and trained to utilize textual public health policies, genomic surveillance, spatial, and epidemiological time series data, and is subsequently tested across all 50 states of the U.S. Empirically, PandemicLLM is shown to be a high-performing pandemic forecasting framework that effectively captures the impact of emerging variants and can provide timely and accurate predictions. The proposed PandemicLLM opens avenues for incorporating various pandemic-related data in heterogeneous formats and exhibits performance benefits over existing models. This study illuminates the potential of adapting LLMs and representation learning to enhance pandemic forecasting, illustrating how AI innovations can strengthen pandemic responses and crisis management in the future.

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