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• How to Write a Literature Review | Guide, Examples, & Templates

How to Write a Literature Review | Guide, Examples, & Templates

Published on January 2, 2023 by Shona McCombes . Revised on September 11, 2023.

What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research that you can later apply to your paper, thesis, or dissertation topic .

There are five key steps to writing a literature review:

• Search for relevant literature
• Evaluate sources
• Identify themes, debates, and gaps
• Outline the structure
• Write your literature review

A good literature review doesn’t just summarize sources—it analyzes, synthesizes , and critically evaluates to give a clear picture of the state of knowledge on the subject.

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Table of contents

What is the purpose of a literature review, examples of literature reviews, step 1 – search for relevant literature, step 2 – evaluate and select sources, step 3 – identify themes, debates, and gaps, step 4 – outline your literature review’s structure, step 5 – write your literature review, free lecture slides, other interesting articles, frequently asked questions, introduction.

• Quick Run-through
• Step 1 & 2

When you write a thesis , dissertation , or research paper , you will likely have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:

• Demonstrate your familiarity with the topic and its scholarly context
• Develop a theoretical framework and methodology for your research
• Position your work in relation to other researchers and theorists
• Show how your research addresses a gap or contributes to a debate
• Evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.

Writing literature reviews is a particularly important skill if you want to apply for graduate school or pursue a career in research. We’ve written a step-by-step guide that you can follow below.

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Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.

• Example literature review #1: “Why Do People Migrate? A Review of the Theoretical Literature” ( Theoretical literature review about the development of economic migration theory from the 1950s to today.)
• Example literature review #2: “Literature review as a research methodology: An overview and guidelines” ( Methodological literature review about interdisciplinary knowledge acquisition and production.)
• Example literature review #3: “The Use of Technology in English Language Learning: A Literature Review” ( Thematic literature review about the effects of technology on language acquisition.)
• Example literature review #4: “Learners’ Listening Comprehension Difficulties in English Language Learning: A Literature Review” ( Chronological literature review about how the concept of listening skills has changed over time.)

You can also check out our templates with literature review examples and sample outlines at the links below.

Download Word doc Download Google doc

Before you begin searching for literature, you need a clearly defined topic .

If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research problem and questions .

Make a list of keywords

Start by creating a list of keywords related to your research question. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list as you discover new keywords in the process of your literature search.

• Social media, Facebook, Instagram, Twitter, Snapchat, TikTok
• Body image, self-perception, self-esteem, mental health
• Generation Z, teenagers, adolescents, youth

Search for relevant sources

Use your keywords to begin searching for sources. Some useful databases to search for journals and articles include:

• Your university’s library catalogue
• Google Scholar
• Project Muse (humanities and social sciences)
• Medline (life sciences and biomedicine)
• EconLit (economics)
• Inspec (physics, engineering and computer science)

You can also use boolean operators to help narrow down your search.

Make sure to read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.

You likely won’t be able to read absolutely everything that has been written on your topic, so it will be necessary to evaluate which sources are most relevant to your research question.

For each publication, ask yourself:

• What question or problem is the author addressing?
• What are the key concepts and how are they defined?
• What are the key theories, models, and methods?
• Does the research use established frameworks or take an innovative approach?
• What are the results and conclusions of the study?
• How does the publication relate to other literature in the field? Does it confirm, add to, or challenge established knowledge?
• What are the strengths and weaknesses of the research?

Make sure the sources you use are credible , and make sure you read any landmark studies and major theories in your field of research.

You can use our template to summarize and evaluate sources you’re thinking about using. Click on either button below to download.

Take notes and cite your sources

As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.

It is important to keep track of your sources with citations to avoid plagiarism . It can be helpful to make an annotated bibliography , where you compile full citation information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.

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To begin organizing your literature review’s argument and structure, be sure you understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:

• Trends and patterns (in theory, method or results): do certain approaches become more or less popular over time?
• Themes: what questions or concepts recur across the literature?
• Debates, conflicts and contradictions: where do sources disagree?
• Pivotal publications: are there any influential theories or studies that changed the direction of the field?
• Gaps: what is missing from the literature? Are there weaknesses that need to be addressed?

This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.

• Most research has focused on young women.
• There is an increasing interest in the visual aspects of social media.
• But there is still a lack of robust research on highly visual platforms like Instagram and Snapchat—this is a gap that you could address in your own research.

There are various approaches to organizing the body of a literature review. Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).

Chronological

The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarizing sources in order.

Try to analyze patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.

If you have found some recurring central themes, you can organize your literature review into subsections that address different aspects of the topic.

For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.

Methodological

If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:

• Look at what results have emerged in qualitative versus quantitative research
• Discuss how the topic has been approached by empirical versus theoretical scholarship
• Divide the literature into sociological, historical, and cultural sources

Theoretical

A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.

You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.

Like any other academic text , your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.

The introduction should clearly establish the focus and purpose of the literature review.

Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.

As you write, you can follow these tips:

• Summarize and synthesize: give an overview of the main points of each source and combine them into a coherent whole
• Analyze and interpret: don’t just paraphrase other researchers — add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
• Critically evaluate: mention the strengths and weaknesses of your sources
• Write in well-structured paragraphs: use transition words and topic sentences to draw connections, comparisons and contrasts

In the conclusion, you should summarize the key findings you have taken from the literature and emphasize their significance.

When you’ve finished writing and revising your literature review, don’t forget to proofread thoroughly before submitting. Not a language expert? Check out Scribbr’s professional proofreading services !

This article has been adapted into lecture slides that you can use to teach your students about writing a literature review.

Scribbr slides are free to use, customize, and distribute for educational purposes.

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If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

There are several reasons to conduct a literature review at the beginning of a research project:

• To familiarize yourself with the current state of knowledge on your topic
• To ensure that you’re not just repeating what others have already done
• To identify gaps in knowledge and unresolved problems that your research can address
• To develop your theoretical framework and methodology
• To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

The literature review usually comes near the beginning of your thesis or dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other  academic texts , with an introduction , a main body, and a conclusion .

An  annotated bibliography is a list of  source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a  paper .  

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Neag School of Education

Educational Research Basics by Del Siegle

Experimental research.

The major feature that distinguishes experimental research from other types of research is that the researcher manipulates the independent variable.  There are a number of experimental group designs in experimental research. Some of these qualify as experimental research, others do not.

• In true experimental research , the researcher not only manipulates the independent variable, he or she also randomly assigned individuals to the various treatment categories (i.e., control and treatment).
• In quasi experimental research , the researcher does not randomly assign subjects to treatment and control groups. In other words, the treatment is not distributed among participants randomly. In some cases, a researcher may randomly assigns one whole group to treatment and one whole group to control. In this case, quasi-experimental research involves using intact groups in an experiment, rather than assigning individuals at random to research conditions. (some researchers define this latter situation differently. For our course, we will allow this definition).
• In causal comparative ( ex post facto ) research, the groups are already formed. It does not meet the standards of an experiment because the independent variable in not manipulated.

The statistics by themselves have no meaning. They only take on meaning within the design of your study. If we just examine stats, bread can be deadly . The term validity is used three ways in research…

• I n the sampling unit, we learn about external validity (generalizability).
• I n the survey unit, we learn about instrument validity .
• In this unit, we learn about internal validity and external validity . Internal validity means that the differences that we were found between groups on the dependent variable in an experiment were directly related to what the researcher did to the independent variable, and not due to some other unintended variable (confounding variable). Simply stated, the question addressed by internal validity is “Was the study done well?” Once the researcher is satisfied that the study was done well and the independent variable caused the dependent variable (internal validity), then the research examines external validity (under what conditions [ecological] and with whom [population] can these results be replicated [Will I get the same results with a different group of people or under different circumstances?]). If a study is not internally valid, then considering external validity is a moot point (If the independent did not cause the dependent, then there is no point in applying the results [generalizing the results] to other situations.). Interestingly, as one tightens a study to control for treats to internal validity, one decreases the generalizability of the study (to whom and under what conditions one can generalize the results).

There are several common threats to internal validity in experimental research. They are described in our text.  I have review each below (this material is also included in the  PowerPoint Presentation on Experimental Research for this unit):

• Subject Characteristics (Selection Bias/Differential Selection) — The groups may have been different from the start. If you were testing instructional strategies to improve reading and one group enjoyed reading more than the other group, they may improve more in their reading because they enjoy it, rather than the instructional strategy you used.
• Loss of Subjects ( Mortality ) — All of the high or low scoring subject may have dropped out or were missing from one of the groups. If we collected posttest data on a day when the honor society was on field trip at the treatment school, the mean for the treatment group would probably be much lower than it really should have been.
• Location — Perhaps one group was at a disadvantage because of their location.  The city may have been demolishing a building next to one of the schools in our study and there are constant distractions which interferes with our treatment.
• Instrumentation Instrument Decay — The testing instruments may not be scores similarly. Perhaps the person grading the posttest is fatigued and pays less attention to the last set of papers reviewed. It may be that those papers are from one of our groups and will received different scores than the earlier group’s papers
• Data Collector Characteristics — The subjects of one group may react differently to the data collector than the other group. A male interviewing males and females about their attitudes toward a type of math instruction may not receive the same responses from females as a female interviewing females would.
• Data Collector Bias — The person collecting data my favors one group, or some characteristic some subject possess, over another. A principal who favors strict classroom management may rate students’ attention under different teaching conditions with a bias toward one of the teaching conditions.
• Testing — The act of taking a pretest or posttest may influence the results of the experiment. Suppose we were conducting a unit to increase student sensitivity to prejudice. As a pretest we have the control and treatment groups watch Shindler’s List and write a reaction essay. The pretest may have actually increased both groups’ sensitivity and we find that our treatment groups didn’t score any higher on a posttest given later than the control group did. If we hadn’t given the pretest, we might have seen differences in the groups at the end of the study.
• History — Something may happen at one site during our study that influences the results. Perhaps a classmate dies in a car accident at the control site for a study teaching children bike safety. The control group may actually demonstrate more concern about bike safety than the treatment group.
• Maturation –There may be natural changes in the subjects that can account for the changes found in a study. A critical thinking unit may appear more effective if it taught during a time when children are developing abstract reasoning.
• Hawthorne Effect — The subjects may respond differently just because they are being studied. The name comes from a classic study in which researchers were studying the effect of lighting on worker productivity. As the intensity of the factor lights increased, so did the work productivity. One researcher suggested that they reverse the treatment and lower the lights. The productivity of the workers continued to increase. It appears that being observed by the researchers was increasing productivity, not the intensity of the lights.
• John Henry Effect — One group may view that it is competition with the other group and may work harder than than they would under normal circumstances. This generally is applied to the control group “taking on” the treatment group. The terms refers to the classic story of John Henry laying railroad track.
• Resentful Demoralization of the Control Group — The control group may become discouraged because it is not receiving the special attention that is given to the treatment group. They may perform lower than usual because of this.
• Regression ( Statistical Regression) — A class that scores particularly low can be expected to score slightly higher just by chance. Likewise, a class that scores particularly high, will have a tendency to score slightly lower by chance. The change in these scores may have nothing to do with the treatment.
• Implementation –The treatment may not be implemented as intended. A study where teachers are asked to use student modeling techniques may not show positive results, not because modeling techniques don’t work, but because the teacher didn’t implement them or didn’t implement them as they were designed.
• Compensatory Equalization of Treatmen t — Someone may feel sorry for the control group because they are not receiving much attention and give them special treatment. For example, a researcher could be studying the effect of laptop computers on students’ attitudes toward math. The teacher feels sorry for the class that doesn’t have computers and sponsors a popcorn party during math class. The control group begins to develop a more positive attitude about mathematics.
• Experimental Treatment Diffusion — Sometimes the control group actually implements the treatment. If two different techniques are being tested in two different third grades in the same building, the teachers may share what they are doing. Unconsciously, the control may use of the techniques she or he learned from the treatment teacher.

When planning a study, it is important to consider the threats to interval validity as we finalize the study design. After we complete our study, we should reconsider each of the threats to internal validity as we review our data and draw conclusions.

Del Siegle, Ph.D. Neag School of Education – University of Connecticut [email protected] www.delsiegle.com

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Models and Impacts of Science Research Experiences: A Review of the Literature of CUREs, UREs, and TREs

• Jessica S. Krim
• Laleh E. Coté
• Renée S. Schwartz
• Elisa M. Stone
• Jessica J. Cleeves
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• Lawrence Horvath
• John M. Keller
• Soon Chun Lee
• Sharon M. Locke
• Bryan M. Rebar

*Address correspondence to: Jessica S. Krim ( E-mail Address: [email protected] ).

Curriculum and Instruction, Southern Illinois University, Edwardsville, Edwardsville, IL 62026

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In efforts to increase scientific literacy and enhance the preparation of learners to pursue careers in science, there are growing opportunities for students and teachers to engage in scientific research experiences, including course-based undergraduate research experiences (CUREs), undergraduate research experiences (UREs), and teacher research experiences (TREs). Prior literature reviews detail a variety of models, benefits, and challenges and call for the continued examination of program elements and associated impacts. This paper reports a comprehensive review of 307 papers published between 2007 and 2017 that include CURE, URE, and TRE programs, with a special focus on research experiences for K–12 teachers. A research-supported conceptual model of science research experiences was used to develop a coding scheme, including participant demographics, theoretical frameworks, methodology, and reported outcomes. We summarize recent reports on program impacts and identify gaps or misalignments between goals and measured outcomes. The field of biology was the predominant scientific disciplinary focus. Findings suggest a lack of studies explicitly targeting 1) participation and outcomes related to learners from underrepresented populations, 2) a theoretical framework that guides program design and analysis, and, for TREs, 3) methods for translation of research experiences into K–12 instructional practices, and 4) measurement of impact on K–12 instructional practices.

INTRODUCTION

As today’s world continues to be shaped by science and technology, there is a pressing need to improve public understanding of what constitutes “science” and scientific practices ( National Research Council [NRC], 2012 ). A report commissioned by the NRC ( Duschl et al. , 2007 ) emphasized the need to improve science, technology, engineering, and mathematics (STEM) literacy among all citizens while also encouraging underrepresented groups to aspire to and succeed in STEM fields. Teachers are called to engage students in investigative practices modeled after those of scientists, engineers, and mathematicians; to enhance students’ conceptions of nature of science and the nature of scientific, engineering, and mathematics practices; and to foster STEM identities to educate learners of all ages toward these proficiencies. A Framework for K–12 Science Education ( NRC, 2012 ) and the Next Generation Science Standards ( NGSS Lead States, 2013 ), encourage educators in the United States to make science learning experiences more authentic by challenging their students to creatively solve real-world problems and address current scientific issues.

Learning experiences need to reflect the relevance, curiosity, and inspiration of the learners themselves to attract them to STEM fields and retain them through degree completion. For science, much of this work has focused on “real” or “authentic” science experiences, by which educators often mean science that reflects practices of scientists and engineers and engagement in scientific habits of mind ( Roth, 1995 ). To meet the challenge of helping students “learn science in a way that reflects how science actually works” ( NRC, 1996 , p. 214), many countries around the world are recommending learners be engaged in designing and conducting scientific investigations ( Hasson and Yarden, 2012 ; Crawford, 2014 ). Among the most recent recommendations from the United States, the NGSS emphasize a three-dimensional approach to K–12 science education focused on core disciplinary ideas, scientific practices, and cross-cutting concepts.

Despite decades of recommendations to involve learners in scientific activities that model authentic science, K–12 teachers still struggle to integrate these practices across their curricula ( Capps and Crawford, 2013 ; Crawford, 2014 ). One commonly stated reason for teachers’ continued challenges is they typically lack firsthand scientific research experience in an authentic setting, either in laboratory classes or within academic or industry research settings ( Schwartz and Crawford, 2004 ; Sadler et al. , 2010 ). In response, multiple programs have engaged teachers and future teachers in scientific research experiences ( Sadler et al. , 2010 ). For many years the National Science Foundation (NSF) has funded Research Experiences for Teachers (RET) programs to engage practicing teachers in authentic science experiences that will translate into enhanced learning experiences for K–12 students. In parallel, a growing number of programs involve undergraduate students in scientific research projects in a laboratory or industrial setting—undergraduate research experiences (UREs; Linn et al. , 2015 ; National Academies of Sciences, Engineering, and Medicine [NASEM], 2017 ), also referred to as research experiences for undergraduates ( National Science Foundation, 2019 ), research apprenticeships ( Sadler et al. , 2010 ), or research internships ( Auchincloss et al. , 2014 )—or in higher education classrooms—course-based undergraduate research experiences (CUREs; Corwin et al. , 2015 ). Because a number of undergraduate students in UREs and CUREs may become science teachers after additional teacher preparation and credentialing, either as undergraduates or in postbaccalaureate programs, what they learn in these research experiences is critical for their future careers as teachers. Also, a variety of programs place teachers in industry jobs over the summer with the intention of providing expanded opportunities designed to increase teachers’ abilities to bring workplace-related 21st-century skills to their classrooms ( Ignited, 2018 ; University of Arizona, 2018 ; Virginia Science and Technology Campus, 2018 ).

Calls to Action

Calls to action for more intentional program and course design, as well as systematic research targeting CUREs, UREs, and teacher research experiences (TREs, including NSF RET programs and other teacher programs funded by other sources), come from several comprehensive reviews of published studies in these areas ( Sadler et al. , 2010 ; Corwin et al. , 2015 ; Linn et al. , 2015 ) and reports ( Auchincloss et al. , 2014 ; NASEM, 2017 ). Sadler et al. ’s (2010) review reported on 53 empirical papers published between 1961 and 2008 that focused on science research experiences for secondary students, undergraduates, and teachers, both preservice and in-service. Sadler and colleagues examined learning outcomes associated with participation in these research activities, including career aspirations, nature of science, scientific content knowledge, self-efficacy, intellectual development, skills, satisfaction, discourse practices, collaboration, and changes in teacher practices. Moreover, Sadler and McKinney (2010) highlighted studies specifically involving undergraduate students and reported similar gains in career goals, confidence, content knowledge, and skills. Both of these reviews reported that science apprenticeships or research programs vary substantially regarding the length of experience, student epistemic engagement, and embedded supports for learning. They recommended that further studies use valid and reliable instruments to explore relationships among program components and outcomes and to identify and design experiences that provide epistemic challenges to participants in order to maximize potential gains.

The Corwin et al. (2015) review examined 14 studies on CUREs and 25 studies on UREs published between 2006 and 2014. The authors determined the level of empirical support within each study and the alignment of activities and outcomes in order to create minimodels based on common outcome themes. They then developed a more extensive, comprehensive model containing “hubs” that provided a framework for better understanding the research. Corwin et al. (2015) emphasized the importance of developing a program model before selecting which outcomes to measure and using a three-phase evaluation system in which a variety of outcomes may be measured at appropriate time points.

In their 2015 review, Linn and colleagues examined 60 empirical studies published between 2010 and 2015 that described UREs ( Linn et al. , 2015 ). They found that most of the studies relied on self-reported gains gathered through surveys or interviews. Only four studies used additional instruments to measure gains in undergraduates’ research capabilities or conceptual understanding. The authors called for rigorous research methods to identify ways to design UREs that promote integrated understanding, specifically those with systematic, iterative studies that build in multiple indicators of success.

A recent NASEM report (2017) outlines a set of recommendations that reiterate the need for well-designed studies that have the capacity to surface causal relationships among components of UREs and for evidence-based URE design and refinement. The report provides suggestions for improved data collection; professional development opportunities for faculty; and mentor training for graduate students, postdoctoral researchers, and faculty who work with undergraduate researchers. The report also calls for strengthening collaborative partnerships within and across institutions to sustain URE efforts.

Moreover, policy makers have tasked educators with repairing and broadening the pipeline into STEM careers, including opening accessibility to the STEM pipeline across underrepresented groups. Engaging all students in authentic science is critical due to the widely acknowledged achievement gap between students of low and high socioeconomic status ( Astin and Oseguera, 2004 ; Hoxby and Avery, 2013 ) and the underrepresentation of women, persons of color, and persons with disabilities in science and engineering ( National Academy of Sciences, National Academy of Engineering, and Institute of Medicine, 2011 ). Linn et al. (2015) identify elements to strengthen mentoring and ensure that research experiences meet the needs of diverse students, including socialization, emotional support, cultural norming, remedying gaps in preparation, and promoting science identity. NASEM (2017) summarizes literature that supports a correlation between the quality and frequency of mentee–mentorship interactions with positive undergraduate student outcomes, including for underrepresented students, and calls for additional research in this area. Sadler et al. (2010) call attention to four studies in particular ( Nagda et al. , 1998 ; Davis, 1999 ; Campbell, 2002 ; Ponjuan et al. , 2007 ) that highlight the need for program designers and evaluators to give explicit attention to program features and experiences that target women and students of color.

Research also shows that learners’ science content knowledge, skills, beliefs, and attitudes toward science, including interests in careers in science fields, can be enhanced through engaging in science research experiences ( Sadler et al. , 2010 ). While there are indications that teachers’ participation in research impacts K–12 student learning ( Silverstein et al. , 2009 ), the research demonstrates that teacher or future teacher participants generally require more than a research experience, no matter how long, to impact instructional practice ( Schwartz and Crawford, 2004 ; Schwartz et al. , 2004 , 2010 ; Sadler et al. , 2010 ; Enderle et al. , 2014 ). TRE programs that purposefully guide teachers to consider connections between authentic science contexts and their science teaching and provide support for the translation of science research experiences into lesson development are more likely to impact teaching practices in ways that enhance student engagement in scientific practices ( Sadler et al. , 2010 ). Together, these calls to action focus on clarifying impactful program elements through valid measures and identifying models that are effective for a variety of participants in order to increase interest and participation in STEM.

THE COLLABORATIVE AROUND RESEARCH EXPERIENCES FOR TEACHERS

The work presented here is the outcome of efforts coordinated by the Collaborative Around Research Experiences for Teachers (CARET). This collaborative began in January 2015 and has subsequently grown to include representatives from 11 institutions of higher education, two nonprofit institutions, and one national laboratory, who aim to better understand the impacts of research and industry experiences on teacher effectiveness and retention. Toward this end, CARET developed a preliminary model for TREs involving both preservice and in-service teachers ( Figure 1 ; discussed below). The group also recognized that many future teachers engage in research opportunities through UREs and CUREs. Consequently, this review includes publications with respect to CUREs, UREs, and TREs, with particular emphasis on teacher development. In addition to this literature review, the collaborative has created and is currently piloting a shared metric instrument for use across multiple TRE programs (data not included in this review).

FIGURE 1. The CARET model describes elements of STEM TREs and their intended outcomes. Many STEM teachers engage in STEM research experiences, in either academic or industry settings. They do so as in-service teachers (primarily during the summer when they are not teaching in the classroom) and as preservice teachers (primarily as undergraduates before becoming classroom teachers). Undergraduate and teacher researchers are guided to engage in research with experienced researchers in a Community of Practice (e.g., faculty, postdoctoral researchers, and graduate students in a laboratory setting). Outcomes of these experiences include increased STEM knowledge and experience, scientific research practices, career awareness, and STEM self-efficacy and identity. Programs designed for TREs typically are guided to translate their research into classroom practices that include curricular development by a Professional Learning Community, which leads to improvements in STEM teaching and learning, represented here as a “21st Century Teacher,” and include outcomes such as increased persistence in STEM teaching and pedagogical content knowledge. Solid arrows indicate a direct connection; red outline includes direct outcomes from research experiences; open arrows indicate feedback loops; and light green shading represents the individuals or groups involved (green shading deepens for initial and longer-term outcomes).

PURPOSE OF THE REVIEW

The purpose of this comprehensive literature review is to gain an understanding of reported CURE, URE, and TRE program features, targets, and outcomes. We examined relevant papers published from 2007 to 2017 to capture a “state of the field” with respect to types of programs, participants, program elements, assessment measures, outcomes, and theoretical frameworks. In particular, this review targets studies of TRE programs published since the review by Sadler et al. in 2010. Moreover, attention is given to CURE, URE, and TRE studies published between 2014 and 2017 to enable comparison across the most recent literature for these three program types.

The research questions that guided this literature review are 1) How has the research literature about science research experiences for teachers and undergraduates changed over the past decade? 2) What is the involvement of, and impact on, participants from underrepresented groups in science research experiences? 3) How are science research experiences impacting teachers and science teaching? 4) How are studies of research experience programs being conducted and what type of data sources are in use? From these results, we identify trends and gaps and make recommendations for future research.

THEORETICAL FRAMEWORK

Situated learning and communities of practice serve as the theoretical frameworks guiding this review ( Lave and Wenger, 1991 ; Wenger, 1998 ). A “community of practice” framework ( Crawford, 2014 ), in which learners can engage in a variety of social and scientific activities (e.g., questioning, investigating, communicating, critiquing), is used to describe the laboratory or industry community in which undergraduate or teacher researchers develop an understanding of their scientific research practices. Situating participants in a scientific community of practice provides an opportunity for learning in an authentic context and is essential for transitioning from the periphery to community membership ( Lave and Wenger, 1991 ).

Considering these frameworks, and the results of the prior literature reviews, we developed the CARET model. The CARET model posits that teachers or aspiring (preservice) teachers who engage in STEM research or STEM industry experience will demonstrate shifts in professional and pedagogical practices and identity/self-efficacy ( Figure 1 ). The CARET model was developed by the collaborative research team to provide guidance in the literature search and review. We do not assert that the model is inclusive of all impactful features or strategies associated with research or industry experiences for teachers; nor is this study a systematic test of the model. For our purposes, the model serves as a starting point to articulate, challenge, and refine our understanding of CUREs, UREs, and TREs in order to examine the literature.

While most of the past literature and reviews cited here focus on science research experiences, and in particular, research experiences in the field of biology, we chose to use the broader concept of “STEM” in the CARET model to be inclusive of the scientific, technological, engineering, and mathematical disciplinary focus areas for both the experiences as well as the variety of subject areas taught by K–12 teacher participants. By doing so, we acknowledge that research experiences can include multidisciplinary and integrated experiences that are important for professional teacher learning ( Roehrig et al. , 2012 ).

Critical components of the CARET model are the STEM research or STEM industry experience itself and a “translation to practice” experience. Within the model, common characteristics must be present in a STEM research or industry experience, like exposure to scientific practices, collaboration, iteration, discovery, and relevant research. STEM research experiences can take place in any setting in which scientists and teachers conduct original STEM research, and likewise, STEM industry experiences in settings in which STEM skills are applied to advance the interests or goals of the industry.

The CARET model identifies a community of practice ( Lave and Wenger, 1991 ), that is, a group of people involved in the research and who are participants in authentic scientific research, such as mentors who are faculty or senior scientists, postdoctoral researchers, graduate students, as an essential component of the research experience. This model posits that a community of practice is vital in achieving immediate STEM-­related outcomes for teachers, such as increased understanding of STEM knowledge and practices, which can lead to enhanced STEM self-efficacy and STEM-positive identity. Note that goals and intended outcomes of the STEM research or industry experience for teachers are parallel to those for undergraduate researchers (e.g., see “Goals for students participating in UREs,” NASEM, 2017 , p. 71).

Unique to the TRE experience, teacher researchers must also have the opportunity to translate their knowledge and skills into their classrooms ( Schwartz and Crawford, 2004 ; Sampson et al. , 2011 ). This experience can be a formal process that involves actions such as structured reflections, group discussions, and supported lesson development. The experience may also be less formal, or on an individual basis, such as teachers being prompted to consider how they might translate this knowledge and skills into their classrooms. The model also acknowledges the role that professional learning communities (PLCs) can play in supporting and facilitating instructional change ( Dogan et al. , 2016 ) by way of a variety of activities, such as collaborative curricular development or networking and communicating among peers who are participating in the experience together.

The idea of a “21st-century teacher” is named in the spirit of the Framework for 21st Century Learning ( Partnership for 21st Century Learning, 2018 ) and the Next Generation Science Standards ( NGSS Lead States, 2013 ; NRC, 2013 ). This term implies that the teacher, after the STEM research or industry experience, may be better equipped to teach in a manner that not only facilitates student learning outcomes in his or her key STEM discipline but also in such areas as critical thinking, evaluating perspectives, and integrating the use of supportive technologies.

As a result of the research experience and support to translate the experience into instructional practice, teachers become part of a PLC ( Dogan et al. , 2016 ) that enables them to more effectively integrate science subject matter and science and engineering practices within their localized contexts. The CARET model includes PLCs as a transformative factor needed for developing pedagogical content knowledge and other teacher outcomes like self-efficacy and positive STEM identity. The CARET model also highlights the long-term goal of research experiences for teachers: outcomes associated with K–12 students, such as STEM learning, identity, and persistence. The model illustrates feedback loops that point to the iterative nature of teacher growth and experience as continuous cycles, as well as two key sociocultural agents, communities of practice and PLCs.

METHODOLOGY

Inclusion and exclusion process.

A systematic search for papers relevant to research experiences was undertaken using the following key words in various combinations: “science,” “STEM,” “math,” “biology,” “chemistry,” “physical sciences,” “geology,” “geosciences,” “physics,” “earth sciences,” “engineering,” “energy,” “computer science,” “medical,” “materials sciences,” “course-based,” “undergraduate(s),” “teacher(s),” “research,” “classroom-based,” “experience(s),” and “internship(s).” Search engines used were ERIC, ERIC-­EBSCOhost, ERIC Proquest, Google Scholar, and Web of Science. An organizational spreadsheet was created that outlined a search hierarchy, guided the overall search process, and avoided redundancy. This search hierarchy specified the order in which search engine and key word combinations productively targeted relevant papers. Examples of productive combinations of key words in different search engines included “chemistry undergraduate research experiences” (Web of Science), “science undergraduate research experiences” (Web of Science), STEM “undergraduate research internships” (Google Scholar), “research experiences for teachers” (ERIC-EBSCO).

To narrow the field, the search included only papers published since the most recent reviews; for TREs, search terms containing the key word “teacher” included papers between 2007 and 2017 ( Sadler et al. , 2010 ), and for CUREs and UREs, search terms containing the key word “undergraduate” included papers between 2014 and 2017 (e.g., Auchincloss et al. , 2014 ; Corwin et al. , 2015 ; Linn et al. , 2015 ). The initial database search yielded more than 600 unique papers based solely on the titles of the papers and were further screened based on the abstracts. This preliminary collection of 600 papers was refined to 450 and then 307 based on inclusion criteria of 1) peer reviewed; 2) related to CURE, URE or TRE programs; and 3) focused on STEM disciplinary research. The most common reasons for exclusion at this stage included the publication not being related to a CURE, URE or TRE program; the full paper was not accessible (steps were taken to obtain the full text whenever possible); the data collection or analysis had not yet taken place; the publication was a full book (and coding was beyond our capacity). “Data cleaning” was achieved by several team members examining any categories (e.g., item 7: study methods) with blank or unclear entries and filling these in whenever possible by revisiting the relevant paper. Further refinements were based on year of publication to allow fair comparison (2014–2017; n = 268) and study type (data driven; n = 177). Details of these additional refinements are provided in the following sections.

At least one of 13 members of the CARET coding team (“coders”) coded each of the 450 papers. An initial coding scheme was developed by building on categories used by Linn et al. (2015) on undergraduate research. A number of new categories and codes were added to account for the broadened scope of this literature review, which includes TREs. The group refined the coding guide to align with the CARET model ( Figure 1 ) by introducing several additional codes to account for emergent themes we deemed relevant but the existing scheme had not yet captured.

Using a group calibration process, wherein all members of the coding team coded a single paper and discussed resulting codes over conference calls to reach consensus, allowed for further refinement in defining each coding category. During this process, the codebook was refined to reflect emergent themes as well as collapsed themes due to commonalities. This process was conducted sequentially with a new paper every 2 weeks over a 6-month period until coders consistently reached consensus. The resulting coding guide has 24 items. It is noteworthy that the coding team comprised 13 geographically dispersed members (all located in the United States) who represented a variety of science-related fields. Challenges to establishing intercoder agreement were similar to those reported by others ( Kowalski et al. , 2018 ). The process of reducing and clarifying the coding guide as a team was essential for assuring intercoder agreement.

Coders received papers in batches of 10. Helpful coding tools included a detailed coding dictionary that thoroughly defined each coding term and the agreed-upon criteria for placing a paper into one or more particular categories, as well as an electronic coding form (Google Form) that collected coding data into a single spreadsheet. The Google Form permitted coders to view later and make edits to their data as deemed necessary based on group discussions. These procedures facilitated a systematic coding process by multiple coders and gave the group the opportunity to review the data together in an electronic format. As a continued check for intercoder reliability, 16 papers were independently coded by two to five coders, and results were compared. Any discrepancies were addressed by the whole group to reach consensus. See the Supplemental Material for coding guide/dictionary and Google Form survey.

After the coding guide was finalized, the number of relevant papers was reduced to 307 for full analysis. After the 307 papers were coded, descriptive statistics allowed basic comparison by percentages and frequencies, cross-tabulation, and multiple-response using SPSS v. 21. We conducted comparisons within and across type of program (CURE, URE, TRE). A Pearson’s chi-squared test was used for the statistical tests reported.

We present notable findings across program types and study type, including participant number, identification of participants from underrepresented groups, duration and intensity of research experience, study methodology, outcomes, and author-identified theoretical framework. Additionally, we present more specific results for data-driven studies.

Program and Study Type

In the final sample of 307 papers, 113 focused on CUREs (published 2014–2017), 85 on UREs (published 2014–2017), and 61 on TRE programs (published 2007–2017); 21 focused on a combination of more than one program type; and 27 were considered “other” due to an inability to determine a clear category.

To focus our analysis on recent literature within the same time period and facilitate comparisons across program types, we removed the 39 TRE papers published from 2007–2013 (citations for these papers are included in the Supplemental Material for reference, and several that are particularly relevant are cited and summarized in the Discussion ).

Among the remaining 268 papers from 2014 to 2017, 177 were coded for study type as empirical studies or program evaluation, while the remaining papers were program description only, theoretical, review, or other. To meet the call from Linn et al. (2015) for more empirical studies, and because the distinction between empirical and program evaluation was not always clear, we grouped the two categories that included analysis of data (empirical and program evaluation), terming the study type for these 177 papers as “data driven.”

In the remainder of the Results section, we present our analysis of these 177 papers (data-driven papers published from 2014 to 2017), because they had the most potential to provide descriptions as well as evidence-based claims that may be translatable to other programs in the field (see the Supplemental Material for a reference list of these 177 papers).

Of the 177 data-driven papers published during the 2014–2017 time period, more studies involving CUREs ( n = 72; 41%) and UREs ( n = 65; 37%) were published, compared with TRE studies ( n = 22; 12%). The classification of “other” ( n = 9; 5%) includes studies that did not fall into the CURE–URE–TRE coding scheme, whereas the “combination” classification ( n = 9; 5%) includes publications that studied a mixture of two or more types of research experiences. Interestingly, of the 268 total papers from 2014 to 2017, TREs had a significantly greater percentage of data-driven papers (85%) than CUREs (64%) or UREs (77%; p = 0.027).

The majority of undergraduate and teacher research programs described had a disciplinary focus in biology (58%). Other fields include physical science (31%), earth and space science (11%), engineering (20%), mathematics (8%), computer science (7%), interdisciplinary STEM fields (17%), other (6%), and not stated (6%). The predominance of biological research was also the case for each of the three program types: CUREs (64%), UREs (57%), and TREs (45%). Note that multiple codes were permitted for disciplinary focus.

Study Participants

The number of study participants reported in each paper was determined overall and compared across program types ( Table 1 ). The overall data were clustered across categories of less than 20 ( n = 35), 20–100 ( n = 71), 100–200 ( n = 18), 200–1000 ( n = 31), more than 1000 ( n = 13), and unknown ( n = 9). The number of participants in each cluster was then broken down across different program types ( Table 1 ). The majority of TRE programs involved fewer than 20 participants and the majority of CURE and URE programs involved between 20 and 100 participants. There were 14 CURE and 15 URE studies that involved between 200 and 1000 participants. Of the studies that collected data from more than 1000 participants, most were CUREs ( n = 7). The only TRE study of more than 1000 participants was also one of the largest studies among this data set ( n = 10,468; Ragusa and Juarez, 2017 ). Papers reporting on TRE experiences primarily studied in-service teachers ( n = 20); few studied preservice teachers ( n = 0) or both ( n = 2).

a In all tables, n = number of papers in the group.

Note that these data were obtained by coding for total participant number reported. In most cases, the total number reported referred to the participant number in the particular research experience program and was the same as the number of study participants, but in some cases the total participant number referred to the total study participants in several different programs combined or programs from consecutive years or to a subset of program participants.

Identification of Participants from Underrepresented Groups

A number of prior studies pointed to the importance of considering how participants from underrepresented groups experience CURE, URE, and TRE settings, as experiences and outcomes for underrepresented undergraduate students and teachers may differ from those of other program participants ( Jones et al. , 2010 ; Junge et al. , 2010 ; Ovink and Veazey, 2011 ; Schwartz, 2012 ; Slovacek et al. , 2012 ; Stevens and Hoskins, 2014 ; Linn et al. , 2015 ). Thus, our analysis specifically sought mention of, and disaggregated outcomes for, participants from underrepresented groups, which was coded by author identification of study and program populations (underrepresented groups include women, persons of color, and persons with disabilities in science and engineering). Of the 177 studies, 114 (64%) failed to mention the involvement of teachers or undergraduate students from underrepresented groups ( Table 2 ). While 47 studies (26%) identified the number of participants from underrepresented groups in the program and study, they did not report findings specific to these participants. Only 17 (10%) reported an intentional focus on participants from underrepresented groups. This general trend repeated across all categories of research experiences. Specifically, the majority of TRE studies did not mention underrepresented groups, and there were no TRE studies that focused explicitly on participants from underrepresented groups.

Duration and Intensity of Research Experience

Because others have identified variable outcomes related to the duration and level of intensity of the research experience ( Sadler et al. , 2010 ; Estrada et al. , 2011 ; Shaffer et al. , 2014; Corwin et al. , 2015 ; Linn et al. , 2015 ), we sought information on research duration and intensity. In our analysis scheme, we define low intensity as less than 10 hours of participation per week and high intensity as more than 10 hours of participation per week. Previously, Linn et al. (2015) reported that most CUREs involved lower-division students (first- and second-year undergraduates) and lasted one semester or less. Most UREs involve a full range of students and variable lengths of participation ( Corwin et al. , 2015 ). In our analysis, we found similar trends over the past few years across CURE, URE, and TRE papers. Overall, only 7% of the 177 data-driven studies described research experiences longer than 1 year ( n = 12), and most CUREs and UREs lasted for one term (3 to 4 months; n = 31, 43%, and n = 24, 37%, respectively). While 15% ( n = 10) of UREs and 29% ( n = 21) of CUREs lasted for less than one term, most TREs in our study fell into this category ( n = 14, 64%), which may result from teachers’ participation during the summer (ranging from 1 week to 3 months). The percentage of TREs in this category was a statistically higher percentage than for UREs or CUREs ( p = 0.0001). Across all studies, a noteworthy proportion did not state the duration ( n = 32, 18%) or intensity of the research experience ( n = 58, 33%). Most CURE studies reported low-intensity research experiences ( n = 50, 69%), but most URE and TRE studies reported intensity either as high ( n = 25, 38%, and n = 11, 50%, respectively) or failed to state the intensity of the research experience ( n = 29, 45%, and n = 8, 36% respectively; p = 0.0001).

Study Methodology

Prior studies highlighted the need to examine the impacts of research experience programs through a variety of data sources. For example, Sadler et al. (2010) found that most programs for undergraduates and programs for teachers relied solely on participant self-reports of learning outcomes. Similarly, Linn et al. ’s (2015) review found that more than half of the studies they examined relied solely on self-report surveys or interviews. From our analysis, we suggest that this call has begun to be answered, with 57% ( n = 101) of the 177 data-driven reports containing measures other than, or in addition to, self-report, a significant increase across CUREs, UREs, and TREs ( p = 0.047). The most substantial proportion were TRE studies, which more frequently used measures beyond self-report data ( n = 16, 73%), compared with CURE ( n = 42, 58%) or URE ( n = 30, 46%) studies ( p = 0.047).

Sadler et al. (2010) state, “The most pressing issue that we have raised is the need for greater methodological diversity” (p. 253). Accordingly, we coded the sample for methodology and data sources, both quantitative and qualitative, as well as for research and evaluation purposes. Results ( Table 3 ) show that many studies used a variety of data sources and approaches. While many studies continue to use quantitative self-report surveys (such as Likert-scale items), they use additional measures simultaneously. The use of qualitative self-report surveys (such as open-ended questions) to measure participant perspective is high among TRE programs ( n = 14, 64%) compared with URE and CURE programs ( n = 40, 56%, and n = 28, 43%, respectively), but not to a statistically significant degree ( p = 0.164). Among measures other than surveys, 23% ( n = 41) used institutional data, and 24% used a content or practice assessment ( n = 42). Methods other than surveys used across CURE, URE, and TRE studies varied; CURE studies most frequently used content/practice assessments ( n = 27, 38%), URE studies most frequently used institutional data ( n = 21, 32%), and TRE studies most frequently used interviews/focus groups ( n = 12, 55%). Also note that many studies used a variety of methods, as the proportions add up to far beyond 100% for each program type and across specific data sources.

a The 177 data-driven papers were independently coded for self-report only vs. more than self-report, and type of data collected (survey, interviews, content/practice assessment, other). A paper could receive multiple codes for type of data collected.

b Self-report survey, Quant, quantitative data (e.g., multiple choice, checkboxes) collected via survey; Qual, qualitative data (e.g., open-ended responses, essay questions) collected via survey.

c Interview includes focus groups.

A final coding scheme for methodology included categorizing different study designs with respect to when data were collected ( Table 4 ). While most studies used a pre/post measure ( n = 54, 31%), a number were of more complex design, such as multiple data points throughout the program ( n = 22, 12%), quasi-experimental ( n = 19, 11%), and longitudinal ( n = 7, 4%). A proportion of the TRE studies ( n = 8, 36%) used either multiple data points over time or a trajectory/retrospective study design, for example, as a method of classroom follow-up after the research experience.

a One data point included pre or post only; trajectory included retrospective and/or multiple data points over time; quasi-experimental included comparison of 2+ sample conditions; longitudinal included tracking individuals over time.

Measured Outcomes

The three previous reviews presented well-organized lists of reported measured outcomes. Authors included similar reported outcomes in the coding scheme used here ( Table 5 ). Across all programs, the most frequently reported outcomes measured were science practices ( n = 67, 38%), laboratory skills ( n = 61, 35%), disciplinary content knowledge ( n = 60, 34%), and confidence ( n = 55, 31%). These reports indicate positive gains with respect to the measured outcomes. Among the TRE studies, these four outcomes were relatively sparse, with “impacts on classroom practice” being the most targeted outcome ( n = 13, 60%). Surprisingly, only 23% ( n = 5) of TRE studies focus on K–12 student outcomes, which is a stated long-term goal for most TRE programs (see Discussion ).

a Performance includes course grades and/or grade point average. Perceptions refers to teachers and teaching. NOS, nature of science.

b Pertains to STEM careers.

c Pertains to science content discipline knowledge.

d Indicates the most frequent category in the group.

Author-Identified Theoretical Framework

Linn et al. (2015) proposed the application of the knowledge integration framework for research on undergraduate research experiences, which provided a guide for their study and enabled them to identify gaps and challenges in the field. They suggested that reports of research experiences need to more frequently identify the theoretical foundation that framed their studies as well as programs. Of note in the data-driven studies in the current review, only 37% ( n = 66) explicitly identified a framework (theoretical, conceptual, or learning theory) that informed their approach ( Table 6 ). Author-identified theoretical frameworks (i.e., the structure that supports the theoretical basis of a research study; see also Sutton and Staw, 1995 ) included cognitive apprenticeship; situated learning theory; communities of practice, identity, and agency; and social cognitive career theory. Interestingly, a much higher proportion of TRE studies ( n = 13, 59%) describe a framework compared with CURE ( n = 23, 32%) and URE ( n = 25, 38%) studies, but not to a statistically significant degree ( p = 0.072).

DISCUSSION, IMPLICATIONS, AND DIRECTIONS FOR FUTURE RESEARCH

This study provides updates to calls sounded by Sadler et al. (2010) , Corwin et al. (2015) , Linn et al. (2015) , and NASEM (2017) . We focus on our key research questions sequentially in the following sections: 1) How has the research literature about science research experiences for teachers and undergraduates changed over the past decade? 2) What is the involvement of, and impact on, participants from underrepresented groups in science research experiences? 3) How are science research experiences impacting teachers and science teaching? 4) How are studies of research experience programs being conducted and what type of data sources are in use? Finally, we address the need for synergistic approaches between science research experts and science education experts when designing and conducting science research experiences. Our findings reveal trends as well as limitations in the recent research literature in several areas. We address critical points for these findings by discussing exemplary studies and outline recommendations for future action.

How Has the Research Literature about Science Research Experiences Changed over the Past Decade?

Sadler et al. (2010) examined 53 papers written between 1961 and 2008, 22 of which were publications about apprenticeship research experiences for undergraduates, 20 about secondary education students, and 11 about research programs for teachers. In comparison, our analysis of 177 data-driven papers written between 2014 and 2017, found that 72 studies focused on CUREs, 65 focused on UREs, 22 studies focused on TREs, and 18 studies focused on a combination of these groups or were grouped as “other.” Thus, there is a substantial increase overall in the number of studies involving science research experiences, especially for undergraduate research. While the overall growth is contributing to our understanding of the nature and impact of such research experiences, we note that the decreased proportion of studies (i.e., from 21 to 12%) focused specifically on science teacher development raises questions about the direction of the field. Involving as many learners as possible in research experiences is encouraging. However, as teacher educators, we need to continue improving how we prepare teachers to engage learners in authentic science practices. Given the increase in CUREs and UREs, there is an opportunity for enhancing teacher preparation programs. Purposefully including courses with embedded research experiences and other undergraduate research opportunities within teacher preparation could provide essential contextual and disciplinary knowledge related to scientific practices and the scientific community. Facilitating the transfer of science research experiences to inquiry-based science pedagogy could be targeted as part of CURE and URE programs, as well as during pedagogy and practicum courses for future teachers who participate in CURE or URE programs. In these ways, undergraduate research experiences could more purposefully address the needs of future teachers.

What Is the Involvement of, and Impact on, Participants from Underrepresented Groups in Science Research Experiences?

Science research experiences have been shown in earlier studies to positively impact the achievement and STEM interests of participants from underrepresented groups ( Nagda et al. , 1998 ; Davis, 1999 ; Sadler et al. , 2010 ; NASEM, 2017 ). Nagda and colleagues (1998) reported positive impacts on retention of undergraduates in science majors, particularly among African American students. Linn et al. (2015) concluded that “studies that analyze benefits for subgroups of students could also help those designing research experiences address the unique interests and aspirations of individuals and groups” and that “findings about successful experiences need replication and extension, particularly for students from non-dominant cultures” (2015, p. 5). Despite these findings and the intentions of funding agencies to support programs that address the needs of people of color and other underserved populations in STEM fields, we found that the majority of studies about CUREs, UREs, and TREs published between 2014 and 2017 fail to report demographic data that would identify the proportion of participants from underrepresented populations (64%), or when this information is reported, fail to disaggregate outcomes specifically for underrepresented students and/or teachers (26%). While many of these reports may mention increasing underrepresented populations in STEM as rationale for their program, most do not purposely target these groups or report relevant outcomes. Of the 17 papers (10%) that clearly targeted underrepresented groups and included disaggregated data for these populations, 15 were URE studies. The underrepresentation of such papers in the CURE literature reviewed here may reflect the perspective that CURE curricula are intended to be designed for all students. However, in a 2018 CURE study about the well-known Freshman Research Initiative, Corwin et al. (2018) call for more research on CUREs that report on outcomes for underrepresented groups, in order to understand how these research experiences may differ in their impact across groups.

Nine papers from our review of recent literature stood out as not only involving data analysis for underrepresented groups, but presented findings that we consider particularly relevant ( Miranda and Damico, 2015 ; Robnett et al. , 2015 ; Shapiro et al. , 2015 ; Griffeth et al. , 2016 ; Haeger and Fresquez, 2016 ; Remich et al. , 2016 ; Carpi et al. , 2017 ; Katz et al. , 2017 ; Ghee et al. , 2018 ). A particularly noteworthy example is from Carpi et al. (2017) , who suggest the value of an extended duration of mentorship during URE participation (1–3 years), especially for participants from underrepresented groups, for increasing graduation rates and the number of students earning advanced degrees. In addition, Haeger and Fresquez (2016) explore research duration and the impact of socioemotional and culturally relevant mentoring, reporting increased grade point averages and level of independence in research. Ghee et al. (2018) focuses on professional development activities for students at minority-serving institutions, closely tracking the success of these students through career pathway measures. Finally, a comparison study by Katz et al. (2017) convincingly demonstrates STEM persistence for female students of color in particular.

Very recent work beyond the inclusion criteria for our current study offers additional insights into impacts of science research experiences on underrepresented minority (URM) participants. A longitudinal study by Hernandez et al. (2018) that focused primarily on URMs (94%), women (74%), and biology majors (72%) found that the duration and intensity level of a research experience impacted URM persistence in STEM, stating that undergraduate research was impactful only if students engaged in research for at least 10 hours/week for two or more semesters. Of the 72 CURE papers in our review of the literature, only 11 papers (15%) report a duration of more than one semester and only six papers (8%) report high-intensity level (10+ hours/week); no papers fall into both categories. Additional studies focused on the impacts of undergraduate research on URM persistence in STEM suggest that CUREs alone may not be able to offer the amount and depth of mentoring needed to support underrepresented students to stay in STEM fields, but may be useful as a stepping stone to a high-impact URE ( Fuchs et al. , 2016 ; Tootle et al. , 2019 ).

We join others in reiterating the call for more studies that 1) purposefully target a diverse participant sample and 2) rigorously collect, analyze, and report data that reflect outcomes that may be unique for women and participants of color. Such studies will lead to increasingly better understanding of new approaches to undergraduate and teacher course and programs that result in enhanced representation in STEM fields. If we are to make strides toward equity and diversity in STEM fields, researchers and program developers alike need to intentionally develop programs that not only attract and facilitate diverse participation, but also consider specific attention toward data collection, analysis, and reporting that reflects this goal.

How Are Science Research Experiences Impacting Teachers and Science Teaching?

A primary goal of research experiences for teachers is “to equip teachers with an understanding of and a capability to conduct scientific research that will transfer to their science classrooms” ( Sadler et al. , 2010 , p. 242). The literature also shows the importance of program elements that provide specific and supported opportunities for teachers to translate their research experiences into classroom instruction ( Schwartz and Crawford, 2004 ; Sadler et al. , 2010 ). The current review demonstrates that outcomes that would indicate a transfer of science research knowledge to the science classroom (especially K–12 student learning outcomes) are not being measured sufficiently. Also, there has been insufficient attention to the outcomes that may provide insight into the success of these experiences, such as teacher identity or perceptions of the teaching profession.

While it is understandable that CURE and URE programs prioritize other goals (e.g., lab skills, science practices, attitudes), we emphasize to researchers and program developers the importance of acknowledging that teachers (preservice or in-service) are researchers with similar immediate outcomes to those students who participate in CUREs or UREs, but different professional interests, career aspirations, and long-term outcomes. To measure results pertaining specifically to the K–12 classroom, research questions focused on assessment in this area must be developed and recognized as valid ways to measure the success of a thoughtfully planned and implemented research experience for teachers.

Many studies that focus on TREs are of particular note, including papers published between 2007 and 2013 that were not included in previous reviews. We list several here with their alignment toward specific aspects of the CARET model ( Figure 1 ). In particular, we specifically highlight TRE studies associated with communities of practice ( Feldman et al. , 2009 ; Alkaher and Dolan, 2014 ; McLaughlin and MacFadden, 2014 ; Salto et al. , 2014 ; Peters-Burton et al. , 2015 ; Luera and Murray, 2016 ; Southerland et al. , 2016 ); PLCs ( Enderle et al. , 2014 ; Miranda and Damico, 2015 ; Southerland et al. , 2016 ); translation to practice ( Pop et al. , 2010 ; Herrington et al. , 2011 ; McLaughlin and MacFadden, 2014 ; Amolins et al. , 2015 ; Godin et al. , 2015 ; Miranda and Damico, 2015 ; Blanchard and Sampson, 2017 ; Hardré et al. , 2017 ; Ragusa and Juarez, 2017 ); and K–12 assessments ( Silverstein et al. , 2009 ; Yelamarthi et al. , 2013 ).

Particularly noteworthy for both undergraduate and teacher research is a study by Hanauer et al. (2017) that found measures of project ownership, scientific community values, science identity, and science networking reflected persistence in science. Some of these components can be addressed by using an approach such as that of Feldman et al. (2009) , wherein a research group model is applied to the research apprenticeship, assigning specific roles to students, such as novice researcher, proficient technician, or knowledge producer. These align with a community of practice component. Indeed, reasons teachers give for participating in a research experience or selecting particular programs, can influence their outcomes ( Enderle et al. , 2014 ). Enderle and colleagues (2014) found that those teachers looking to reform their instruction chose and showed considerable impacts from research experience programs explicitly designed for pedagogical development. Likewise, Southerland et al. (2016) found that providing personal relevance and social engagement in the research context increased investment and that participating in a research experience shapes science teaching beliefs, which in turn influences practice. Similarly, Miranda and Damico (2015) found that a summer TRE followed by an academic year–long PLC can help teachers to shift their beliefs surrounding pedagogical approaches; however, documentation of classroom practices that evidence this shift is limited. Such studies provide in-depth insight into the learning outcomes and impacts of research experiences that can serve as a model for future studies, particularly for teacher researchers.

How Are Studies of Research Experience Programs Being Conducted and What Type of Data Sources Are in Use?

While the approach of using self-reported outcomes has been valuable for understanding undergraduate or teacher researcher perspectives for many years, as well as being an efficient means of engaging in evaluation of particular programs, our findings suggest a current trend toward additional measures that hold promise for the development of a more in-depth understanding of CURE, URE, and TRE outcomes. First, researchers have developed many innovative instruments and approaches in recent years from which other studies could benefit. These instruments also inspire the development of new ways for measuring outcomes of research experiences. Papers of note in this area include Feldman et al. , 2009 ; Grove et al. , 2009 ; Kerlin, 2013 ; Yelamarthi et al. , 2013 ; Miranda and Damico, 2015 ; Zhou et al. , 2015 ; Felzien, 2016 ; Hanauer et al. , 2017 ; and Harsh et al. , 2017 .

Second, a variety of novel methodologies or study designs were used in many recent papers, including those that did comparative or longitudinal studies, used multiple instruments, or performed analysis to determine a causal relationship. These papers include Silverstein et al. , 2009 ; Bahbah et al. , 2013 ; Saka, 2013 ; Enderle et al ., 2014 ; Yaffe et al. , 2014 ; Peters-­Burton et al. , 2015 ; Robnett et al. , 2015 ; Shapiro et al. , 2015 ; Thompson et al. , 2015 ; Luera and Murray, 2016 ; Staub et al. , 2016 ; Stephens et al. , 2016 ; and Mader et al. , 2017 . In particular, future studies with designs that can shed light on correlational and causal relationships between factors involved in TREs, the PLC supporting the translation to practice, and longer-term teacher and student outcomes have great potential to advance the field ( Figure 1 , CARET model).

A study conducted by Grove et al. (2009) serves as an example of how a variety of qualitative tools, including pre/post interviews, can be effectively used to test outcomes of general target goals such as inquiry, nature of science, experimental design, process skills, and communicating about science. Kerlin’s (2013) study used observations, email solicitation of teacher ideas, and field notes in an innovative way, identifying critical factors toward incorporation of inquiry learning, including collaboration of STEM and education faculty with teacher participants and sustained support in the classroom. Noteworthy again, Hanauer et al. ’s (2017) comprehensive report on the SEA-PHAGES program demonstrates an exciting array of outcomes with their use of the Psychometric Persistence in the Sciences survey, including project ownership (with content and emotion categories), self-efficacy, science identity, scientific community values, and networking, each measuring a psychological component that correlates strongly with a student’s intention to continue in science. Finally, Klein-Gardner et al. (2012) studied a program in which teachers used a challenge-based curriculum (Legacy Cycle), that incorporated real-world contexts and interdisciplinary approaches to expose K–12 students to engineering, leading to a high level of engagement and participation among students in the classroom.

One of the most extensive studies among the collection was a 7-year longitudinal study involving 10,468 participants ( Ragusa and Juarez, 2017 ). This work examined the impact of two TRE programs, reporting on the combined results of both programs on in-service teachers and their students, using an observational rubric to examine teaching performance and a Science Teaching Efficacy Beliefs Instrument ( Enochs and Riggs, 1990 ) and K–12 student science reading and concept inventory assessments. Findings support the hypothesis that a teacher intervention results in student outcome gains. While this longitudinal study has a uniquely large sample, such research is essential for connecting research experience models, pedagogical impacts, and student impacts.

Enderle et al. (2014) is one of the few studies that used a mixed-methods approach to compare impacts related to two distinct summer research models for teachers: one pedagogically focused and the other science research focused. Over a 5-year period, they measured science teachers’ self-efficacy, pedagogical discontentment, teachers’ beliefs about teaching and learning, and contextual beliefs about teaching science. Results indicated both types of research experience programs positively impacted participants’ beliefs, with the teachers from the pedagogically focused program showing greater gains compared with the science research–focused group. In addition, teachers in the pedagogically focused program were successful in reforming their instructional practice; whereas the science research–focused teachers were not. This study shows the importance of multiple data sources, collected over multiple years of program implementation, for identifying consistent and overarching trends concerning program impacts on participants’ beliefs and potential for translation to classroom practice.

The recent NASEM report (2017) provides detailed recommendations for methodology that would be useful for surfacing causal relationships among components of UREs (chap. 7, pp. 163–180); most of these recommendations also apply to research on TREs.

Suggestions for Increasing Synergistic Approaches between Science Research Experts and Science Education Experts When Designing and Conducting Science Research Experiences

Among the publications examined in this literature review, the occurrence of productive research efforts through collaborations between STEM-disciplinary faculty/industry researchers and education faculty is rare; however, we identified several relevant efforts. Boesdorfer and Asprey (2017) demonstrated that research experiences can be science education research experiences and found a distinct difference in the documented outcomes between science research experiences and science education research experiences. A recent National Academies report ( NASEM, 2017 ) concluded that URE designers were not taking full advantage of the education literature in the design, implementation, and evaluation of UREs. The report recommended that those with science education expertise should conduct studies in collaboration with URE program directors to address how program components impact outcomes. We echo this call and encourage future researchers to form authentic collaborative research partnerships among content area experts and pedagogical experts. We also note that such collaborations could be significant for further understanding effective communities of practice and PLCs outlined in the CARET model.

CONCLUSIONS

This review advances our collective understanding of the current state regarding science research experiences for teachers and undergraduates. Researchers have made advances in studies of CURE and URE programs. These types of programs include future teachers, and we hope to raise awareness within these programs that these undergraduates may benefit further from pedagogical reflection, as many choose a career in teaching in K–12 schools or in higher education. Reports of TRE programs, while decreasing in frequency, are providing some glimpse into instructional impacts, especially within the past 4 years. We call for more studies with such focus in order to correlate program features with instructional outcomes.

Moreover, we found that, despite funding agencies’ explicit goals for increasing representation in STEM fields, few recent studies describe how they select participants or provide additional insights into outcomes of research experiences for people of color and underserved groups. Intentional selection, design, and support are needed for STEM research experience programs in general, and science teachers in particular. Finally, we have highlighted exemplary studies that used methodological designs and multiple data sources that lend confidence to the research findings and effectiveness of the programs. Again, we recommend continued advances in research approaches that provide insights into teacher development, instructional reform, and student impacts.

We offer the CARET model as a conceptual framework that can inform program development and future studies on teacher STEM research experiences. The model provided an initial perspective that guided our coding scheme. More work is needed to test components of this preliminary model and potentially expand it to incorporate finer-grained features. More specific participation and collection of data from underrepresented populations and development of practical, valid, and reliable instruments for measuring K–12 teaching practices and student learning outcomes will contribute to a more accurate view of the success of the TRE programs. Finally, we encourage authentic collaboration between content experts and pedagogical experts to forward these goals.

ACKNOWLEDGMENTS

This work was made possible through funding for the CARET group to attend several meetings and conferences provided through a 100Kin10 collaboration grant, funding from the APLU Network of STEM Education Centers (NSEC), in-kind support from the MJ Murdock Charitable Trust, and several awards from the NSF, including the following programs: Robert Noyce Teacher Scholarship, Research Experiences for Teachers, Graduate Research Fellowship Program, and Improving Undergraduate Science Education. This material is based upon work supported by the NSF under Grant Nos. (1524832, 1340042, 1540826, 1542471, 1106400, and 1712001). We thank these organizations for their generous support. We also extend a special thank you for the guidance and contribution provided by Bruce Johnson of the University of Arizona.

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Submitted: 4 April 2019 Revised: 5 August 2019 Accepted: 11 September 2019

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Experimental Research

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Experiments are part of the scientific method that helps to decide the fate of two or more competing hypotheses or explanations on a phenomenon. The term ‘experiment’ arises from Latin, Experiri, which means, ‘to try’. The knowledge accrues from experiments differs from other types of knowledge in that it is always shaped upon observation or experience. In other words, experiments generate empirical knowledge. In fact, the emphasis on experimentation in the sixteenth and seventeenth centuries for establishing causal relationships for various phenomena happening in nature heralded the resurgence of modern science from its roots in ancient philosophy spearheaded by great Greek philosophers such as Aristotle.

The strongest arguments prove nothing so long as the conclusions are not verified by experience. Experimental science is the queen of sciences and the goal of all speculation . Roger Bacon (1214–1294)

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Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

• When time is an important factor in establishing a relationship between the cause and effect.
• When there is an invariable or never-changing behavior between the cause and effect.
• Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

• There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
• A variable that can be manipulated by the researcher
• Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

• Researchers have firm control over variables to obtain results.
• The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
• The results are specific.
• Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
• Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
• Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

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