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AERA has announced a robust program of professional development courses for the 2025 AERA Annual Meeting in Denver. The courses cover salient topics in education research design; quantitative, qualitative, and mixed research methods; meta-analysis; and other data analysis techniques. The courses begin on Tuesday, April 22, one day prior to the start of the Annual Meeting. Led by an expert faculty of researchers and scholars, these courses are designed at various levels to reach graduate students, early career scholars, faculty, and other education researchers working in public, non-profit, and private sector settings.
Professional development courses can be added at registration under "Optional Events." Early registration is encouraged as space is limited. Questions about the courses should be directed to profdevel@aera.net.
Courses can be added to existing Annual Meeting registrations in the My AERA section of the AERA website.
Click the link below to jump to each course. Courses are listed in chronological order.
Full-Day Courses
Half-Day Courses
Credible and useful evaluations require selecting an appropriate evaluation model or approach. Research has shown that choosing an approach can be challenging for various reasons. This course covers essential concepts for evaluating educational programs and focuses on the characteristics that distinguish evaluation approaches. Participants will learn about multiple evaluation approaches using the Garden of Evaluation Approaches, an empirically based framework published in the American Journal of Evaluation. This framework describes the roles of values, valuing, social justice, context, use, engagement, and power dynamics in evaluation. The course combines interactive lectures, hands-on exercises, and case-based applications to ensure a dynamic and engaging learning experience, preparing you for real-world evaluation scenarios. This course is targeted to graduate students, early career scholars and practitioners, and experienced evaluators and researchers interested in updating their evaluation knowledge and skills. Participants should come with a basic understanding of research methods, but it is not a prerequisite. By the end of the course, participants will be able to (a) explain the purpose of evaluation approaches, (b) evaluate the strengths and opportunities associated with different evaluation approaches and their applicability in diverse educational contexts, and (c) apply multiple approaches in practice. Participants should bring a laptop or tablet. All necessary materials, including case studies and handouts, will be provided.
This course will introduce the basics of systematic review and meta-analysis. Topics covered include developing a research question, searching the literature, evaluating and coding studies, conducting a meta-analysis, and interpreting results for various stakeholders. Participants are encouraged to bring an idea for a systematic review to the course, with time reserved for discussion about it with course instructors. Course activities will include a lecture, hands-on exercises, a small group discussion, and individual consultation. The target audience includes both those new to systematic review and meta-analysis as well as those currently conducting either type of project. Knowledge of basic descriptive statistics is assumed. Participants are required to bring a laptop computer.
Labeling or classifying textual data is an expensive and consequential challenge for Mixed Methods and Qualitative researchers. The rigor and consistency behind the construction of these labels may ultimately shape research findings and conclusions. A methodological conundrum to address this challenge is the need for human reasoning for classification that leads to deeper and more nuanced understandings, but at the same time manual human classification comes with the well-documented increase in classification inconsistencies and errors, particularly when dealing with vast amounts of texts and teams of coders. With a development grant of 2022 SAGE Concept Grant, this course offers an analytic framework designed to leverage the power of machine learning to classify textual data while also leveraging the importance of human reasoning in this classification process. This framework was designed to mirror as close as possible the line-by-line coding employed in manual code identification, but relying instead on latent Dirichlet allocation, text mining, MCMC, Gibbs sampling and advanced data retrieval and visualization. A set of output provides complete transparency of the classification process and aids to recreate the contextualized meanings embedded in the original texts. In the pursuit of truly expanding access to data science, advanced visualization tools, and machine learning to non-programmers, this analytic framework has been packaged in an open-access software application and is the second product of the analytic movement "Democratizing Data Science."
Data from international large-scale assessments (ILSAs) reflect the nested structure of education systems and are therefore well suited to multilevel modeling (MLM). However, because these data come from complex cluster samples, there are methodological aspects that researchers need to understand when using MLM, including sampling weights and multiple achievement values for accurate parameter and standard error estimation. Using the most recently released ILSA data from the Trends in International Mathematics and Science Study (TIMSS) 2023, this course will teach participants how to conduct MLM, with an emphasis on the assessment design features of ILSAs (e.g., TIMSS, PIRLS, and PISA) and implications for MLM analysis. Participants will learn how to specify two-level models using R and will explore model comparison, centering decisions and their consequences, and the resources available for doing three-level models. Time will be allotted for hands-on exercises, and instructors will be available to mentor participants and answer their questions. Step-by-step demonstrations of each practice item will also be provided. Interactive activities will provide research networking opportunities throughout the course. Participants should have a solid understanding of ordinary least squares (OLS) regression and a basic understanding of MLM. While prior knowledge of ILSAs—and prior experience using their databases—is not required, prior experience using R (e.g., installing packages, running functions, fitting regression models) is required. To fully participate in the hands-on exercises, participants will be informed prior to the workshop how to install R (https://cran.r-project.org/) and R Studio (https://posit.co/download/rstudio-desktop/).
This course provides researchers with information about recently released data from two cohorts of the National Center for Education Statistic’s (NCES) high school longitudinal studies program: the High School Longitudinal Study of 2009 (HSLS:09) and the High School and Beyond Longitudinal Study of 2022 (HS&B:22). Data from the HSLS:09 and HS&B:22 allow researchers to examine the relationships between a wide range of family, school, classroom, and individual characteristics and students’ entry into high school and transitions into postsecondary education and the workforce. In this training, participants will become familiar with the design, content, and research utility of HSLS:09 and HS&B:22; learn how to access public-use data files and apply for restricted-use files; understand the need for using sample weights and adjusting variance estimates to conduct accurate analyses; and become familiar with NCES resources related to HSLS:09 and HS&B:22. The training will focus on the released data from HSLS:09, the 9th-grade through the recently released postsecondary education administrative records collection (PEAR) collections, and the HS&B:22 base year collection. Participants will also be given an opportunity to ask questions about how HSLS:09 and HS&B:22 data can be best used to address their own research interests. This course will be a mix of lecture and hands-on time for participants to review questionnaires and online codebooks and ask questions related to their research topics. Therefore, it is recommended that each participant brings a laptop that has their preferred statistical package installed. There are no prerequisite skills or knowledge needed for this course; it is appropriate for researchers of all levels.
This course will introduce advanced methods in meta-analysis. Topics covered include models for handling multiple effect sizes per study (dependent effect sizes) and exploring heterogeneity, the use of meta-analysis structural equation modeling (MASEM), and an introduction to single-case experimental design meta-analysis. The statistical package R will be used to conduct the statistical techniques discussed. Participants are encouraged to bring their own research in progress to the workshop. The activities will include lectures, hands-on exercises, and individual consultation. This course is designed to follow the introduction to systematic review and meta-analysis course given in prior AERA Professional Development training sessions. The target audience are those researchers with systematic review and meta-analysis experience, but who are interested in learning advanced methods for meta-analysis. Knowledge of basic descriptive statistics, systematic review, and basic meta-analysis is assumed. Students are required to bring a laptop computer. Course objectives include: Apply and estimate models that appropriately reflect the multilevel and correlated structure of meta-analysis data; Apply and interpret meta-regression models for dependent effect sizes that explore effect size heterogeneity; Demonstrate the use of meta-analysis structural equation modeling for estimating path models; Demonstrate the use of single-case meta-analysis models to synthesize single-case experimental design studies.
This hands-on, creative course explores the intersection of game-based learning and educational research, particularly in relation to the 2025 AERA Annual Meeting theme of Research, Remedy, and Repair: Toward Just Education Renewal. This course explores games as a research instrument to gain deeper insights into current learning trends and the application of critical pedagogies. The course objectives provide participants with the skills to design problem-solving educational games as practical research tools and to encourage an understanding of how game-based learning can be incorporated into educational research to tackle complex issues. The target audience for this course includes graduate students, early career scholars, and advanced researchers interested in educational research methods and game design. The course format will include a brief lecture introducing game-based learning pedagogies, and their importance in educational research and a problem-solving card game developed by the researchers. Next, each small group will participate in a hands-on demonstration round, during which part of each group will play a game, and the other part will observe gameplay to collect data. Next, small groups will develop and test game prototypes in relation to existing or new research questions and theoretical frameworks. The prerequisite skills or knowledge include a basic understanding of educational research methodologies and social-emotional learning frameworks. Familiarity with game design concepts and pedagogy is helpful but optional. The required materials will include game design papers and digital prototype kits provided by the course faculty for each participant. Potential assignments include creating customized games and proposing initial game-based research designs.
This course provides foundational knowledge about missing data analyses. The course content includes missing data assumptions, Markov Chain Monte Carlo (MCMC) estimation, missing data imputation, incomplete categorical variables, and incomplete interaction effects. The course includes a mix of lectures and software demonstrations. Attendees will be provided with the following materials: lecture slides built around analysis examples from a real educational data set; free statistical analysis software, Blimp, developed by the instructors; a 100+ page white paper that provides details about a range of missing data topics; and a 250+ page annotated software tutorial guide that provides step-by-step instructions for 20 common statistical analyses. Because missing data are ubiquitous in educational research settings, the course content would appeal to virtually any educational discipline that relies on quantitative methods. The target audience includes graduate students, professors, and research professionals who use, but do not specialize in, quantitative methods. To maximize accessibility, the only prerequisite is a working knowledge of statistical concepts from a typical first-year graduate statistics sequence, in particular multiple regression. The course instructors co-develop the free software application Blimp, available at www.appliedmissingdata.com/blimp.
Stefinee E. Pinnegar, Brigham Young University Trudy M. Cardinal, University of Alberta M. Shaun Murphy, University of Saskatchewan Janice Huber, University of Alberta Svanborg Rannveig Jónsdóttir, University of Iceland Deborah L. Tidwell, University of Northern Iowa Jason Pearson, The Church of Jesus Christ of Latter-day Saints Cathy A. Coulter, University of Alaska - Anchorage Eliza A. Pinnegar, Independent Researcher Elaine Chan, University of Nebraska - Lincoln Vicki Ross, Northern Arizona University Celina Marie Lay, Brigham Young University Eunhee Park, Texas A&M University Michaelann Kelley, Mount St. Joseph University Gayle A. Curtis, Texas A&M University Cheryl J. Craig, Texas A&M University
The purpose of this course is to help experienced, developing, and emerging qualitative researchers to build skills in using analytic tools within narrative research projects. The course begins by identifying philosophic bases for narrative research. The workshop directors have expertise in the tools taught and have designed sessions and developed handouts that will enable researchers to use a wider range of analytic tools in narrative research Participants can select four of the nine, 45-minute workshops offered: autobiographical narrative research, tools for multicultural studies, memory work, analysis with visuals, narrative beginnings, literary analysis, use of narrative vignettes with large data sets, narrative music analysis, and serial interpretation. Developing versatility and strength in analytic skills will enable researchers to produce nuanced and trustworthy findings. During the course, researchers will learn the theoretical underpinnings of the tool, have hands-on experience using it and have guidance in developing skill through interaction with experienced, published researchers who engage in narrative research.
The purpose of this course is to train researchers and evaluators how to plan efficient and effective cluster and multisite randomized studies that probe hypotheses concerning main effects, mediation, and moderation. We focus on the conceptual logic and mechanics of multilevel studies and train participants in how to plan cluster and multisite randomized studies with adequate power to detect multilevel mediation, moderation, and main effects. We introduce participants to the free PowerUp! software programs designed to estimate the statistical power to detect mediation, moderation, and main effects across a wide range of designs. The course will combine lecture with hands-on practice with the free software programs. The target audience includes researchers and evaluators interested in planning and conducting multilevel studies that investigate mediation, moderation, or main effects. Participants should bring a laptop to the session.
This course explores the integration of critical perspectives within mixed methods research, emphasizing the role of Critical Social Theory (CST) such as Queer Theory, Critical Race Theory, and Feminist Theory, among others. CST calls for an analysis of social structures, power dynamics, and identity politics, making it applicable across disciplines like education, race studies, and sociology (Leonardo, 2004). Designed for a half-day session, participants will examine the foundational designs of mixed methods research (MMR), including explanatory sequential, exploratory sequential, convergent parallel, and multiphase designs while critically analyzing their potential to challenge power dynamics and promote equity in the implementation of research design and praxis. The course features curated reading and video materials, interactive lectures, hands-on exercises, and collaboration group activities designed to build participants' capacity to design, implement, and evaluate MMR studies that integrate CST. Participants will create and visualize mixed methods designs, engage in meaningful discussions about integrating social justice into research, and learn strategies to navigate the challenges of embedding critical perspectives in MMR. The target audience for this course is graduate students, early and mid-career researchers, and educators committed to leveraging research for social change and seeking to incorporate criticality into their MMR work.
This course provides new and experienced scholars with an opportunity to be in community with fellow research team, lab, collective, and collaborative facilitators and leaders while learning about liberatory and emancipatory frameworks to sustain their work. Attendees will engage critical, community-based participatory action research theory to reexamine their axiological and epistemological perspectives and reimagine their practices and processes for recruiting and caring for members, running meetings, and communicating about their research. Through hands-on activities, dialogic exercises, and reflexive time, attendees will leave with co-constructed resources designed to elevate the liberatory impact of their work.
The objectives of this course include the following:
Whether early career scholars who desire to start a research team or seasoned researchers who have led teams for years, this course is for all who are interested in renewing their research team’s work and for those who seek to cultivate environments that are equitable, just, emancipatory, and liberatory (i.e., actively address and resist oppression in all of its forms within and beyond the context of postsecondary education). Attendees will receive a list of pre-course readings and reflective prompts to complete before the course. Attendees do not need any additional supplies, materials, or software to engage with the course.
In this course, we delve into the transformative potential of applying critical theories to the analysis of large-scale datasets in educational research. As educational institutions and policymakers increasingly rely on big data to inform policies and practices, it is crucial to scrutinize how these data are analyzed, interpreted, and utilized to identify effective remedies, sustainable repairs, and systemic renewals addressing thorny issues in K-12 and higher education. This session will explore how critical theories—such as critical race theory, disability theory, intersectionality, sense of belonging (Strayhorn, 2019), socialization, among others—can provide a robust framework for analyzing educational data, operationalizing key variables, and guiding analytic procedures. By employing these lenses, researchers can uncover hidden biases, challenge dominant narratives (i.e., counter storytelling), and advocate for more equitable educational practices. The course has several major objectives. Participants will learn specific strategies for integrating critical theories into their data analysis processes. Through practical examples and real-life case studies, we will demonstrate how to identify and address structural inequalities, systemic barriers (e.g., poverty), and proxies for other social circumstances using large-scale data published by the US Department of Education, Social Science Research Consortium, and other grant-funded agencies across the globe. This approach not only aids in remedying existing issues but also contributes to the repair and renewal of educational systems. Join us for an engaging and thought-provoking discussion on harnessing the power of critical theories to drive meaningful change in educational research and practice. Enter ready to learn and engage; leave ready to act and analyze!