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This course provides focused guidance for developing theory by reviewing a qualitatively-derived theoretical statement’s six elements: concepts, propositional logic, parameters and/or variation, causation, generalization and/or transferability, and the improvement of social life. After a review of foundation principles in theory and theorizing, participants will explore skill-building activities in each of the six elements, followed by synthesis and visual modeling exercises, and recommended criteria for evaluating theoretical statements. The course is targeted to graduate students, novices to qualitative research, and early career scholars. Participants should have an introductory knowledge (e.g., a one semester or one quarter course) of research design or qualitative research methods. No pre-course assignments or special materials are needed. A laptop or other device with Internet access is strongly recommended for a few selected activities. Course content is based on Saldaña’s textbook, Developing Theory Through Qualitative Inquiry (Sage Publications, 2025).

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 several evaluation journals. 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. 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 would benefit from having a basic understanding of research methods, but it is not a prerequisite. Participants should bring a laptop or tablet. All necessary materials, including case studies and handouts, will be provided electronically through email before the course begins and via a QR code during the course.

This course provides essential literacy skills for Generative Artificial Intelligence (GAI) systems, combining technical understanding with critical analysis of societal implications. Attendees will learn to: (1) navigate current GAI landscapes, (2) understand how GAI systems work and differ from other computing paradigms, (3) demonstrate effective prompt engineering, (4) design appropriate GAI system evaluation, (5) identify and analyze bias in GAI systems, (6) evaluate GAI long run behavior and appropriate application in research settings, (7) understand the range of systems, applications and media currently in use, and (8) develop personal frameworks for responsible GAI engagement in research & learning contexts.  The course combines interactive lectures, extensive hands-on activities with free web-based tools, and structured group discussions. Critical examination of training bias, algorithmic fairness, and educational equity concerns are woven throughout.  This course targets researchers and practitioners at all career stages. No technical background is required, though familiarity with educational research methods commensurate with a first graduate level course is assumed. Participants must bring a laptop with network access. An email account ending in “.gmail” is essential for some activities. Preparatory materials can be found at bit.ly/AERA_2026_GenAI.

This course will introduce the basics of systematic review and meta-analysis (SRMA) by walking through its steps sequentially. 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 SRMA to the course, with time reserved for one-on-one discussion about it with course instructors throughout the day. The course schedule will emphasize lectures, but also include hands-on application, small-group discussion, and a software tutorial as time permits. The target audience includes those new to SRMA or beginning to conduct one for the first time. Knowledge of basic descriptive statistics is assumed. Participants are required to bring a laptop computer.

This course offers a unique opportunity for participants to learn how to conduct multilevel modeling using data from international large-scale assessments (ILSAs), including TIMSS-2023, PIRLS, and PISA. Participants will learn how to specify two-level models using R as well as about model comparison, centering decisions and their consequences, with resources provided for estimating three-level models. Time will be allotted for hands-on exercises, with instructors available to support and answer participants' questions. Step-by-step demonstrations of each practice item will also be provided along with interactive activities for 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 and basic programming skills for programs similar to R is essential. Participants will be informed prior to the course how to install the R and R Studio software as well as a specific R package to conduct MLM analyses.

In qualitative research, meaning emerges through sustained, context-sensitive engagement with text—be it interviews, observations, reflections, or narratives. As textual datasets grow in size, complexity, and diversity, researchers increasingly face a core challenge: how to maintain interpretive depth while achieving structural clarity across expansive bodies of narrative data. This course uses open access software tools to synthetize qualitative evidence and strengthen data science storytelling. This applied course introduces four methodological frameworks, each accompanied by accessible, no-code software, to address the need for innovative tools that may enable qualitative and mixed methods researchers to tap into the insights that come out of Artificial Intelligence, Machine Learning, and state of the art Data Science and Visualization methods with absolutely no computer language literacy requirements. Following Truly Equal Status Design (TESD), this course will focus on four tools: LACOID, MDCOR, SENA, and GeoStoryTelling. These tools do not seek to replace qualitative insight with quantification. Instead, they enhance and extend it—amplifying interpretive possibilities through transparent, rigorous, and iterative engagement. There are no prerequisite skills as the course and materials have been designed to expand access to data science without any prior data science or statistical programming experience. This course is ideal for graduate students, early career scholars, as well as advanced researchers interested in the use of data science in qualitative research.

In this course, we train attendees to evaluate the quality of their semi-structured or unstructured interviews using a novel process-based mechanism, the Interview Quality Reflection Tool (IQRT). This tool enables interviewers to reflect on how they can adaptively respond in interview settings, beyond the prescribed text of a protocol. Following a brief presentation, this course will use two learning activities to foster experiential immersion coupled with individual and group reflection, which includes a mock-interview experience and reflective analysis. The learning objectives for this course include: 1) develop novel understandings of interview quality by examining how interviewers adapt to the interview situation; 2) apply the IQRT to effectively reflect on how they elicit data in qualitative interviews; and 3) understand how they can use the IQRT to facilitate mentoring relationships in qualitative research. While no prior skills or knowledge are required, this course will be most effective for those who are actively engaged in interview-based research. Participation in the course will be most effective with a laptop. No internet access will be required.

This course introduces educational researchers to mixture modeling, a powerful statistical method for exploring unobserved heterogeneity in diverse populations. Led by experienced methodologists, the course will provide a hands-on introduction to person-centered approaches that go beyond traditional statistical methods. The first portion of the course will cover foundational concepts in mixture modeling and latent class analysis, emphasizing their logic, assumptions, and potential in education research. The second portion will walk participants through a demonstration of model estimation in Mplus using sample data. The final portion will include a discussion of mixture modeling extensions and how to apply mixture models in R. This lecture-style course is designed for graduate students, early-career scholars, and advanced researchers who are familiar with latent variable modeling. Laptops are encouraged for hands-on coding, though Mplus is not required.

This course offers practical tools for minimizing harm and improving research quality by refining the use of sex and gender terminology. Participants will explore the ethical, methodological, and theoretical implications of imprecise language and flawed data collection practices that can misrepresent LGBTQ+ identities, perpetuate exclusion, and lead to unreliable findings. Through breakout discussions, self-reflections, survey design exercises, and writing critiques, supported by a comprehensive slide deck and workbook, this course will guide attendees in distinguishing between sex and gender, identifying common conflations, applying best practices in data collection, and crafting inclusive survey questions and research writing. Through the lens of theoretical frameworks such as queer theory, participants will also examine the impact of language on marginalized communities. Designed for a wide audience, from graduate students to experienced researchers, this course requires no prior experience beyond an open mind.

This course offers an introduction to Quantitative Ethnography (QE) with a focus on Epistemic Network Analysis (ENA), the most prominent approach used in QE analyses. The course explores QE as a framework for supporting education research in the age of Artificial Intelligence (AI). In many learning contexts, we increasingly have access to rich process data. To make meaning of this evidence, our goal is to develop a qualitatively “thick” description of the data and, thus, of learning. However, the more data we have, the more difficult this process becomes: qualitative analysis becomes less feasible, and quantitative analysis becomes less reliable. QE addresses this problem by using statistical and computational techniques to warrant claims about the quality of thick qualitative descriptions/interpretations. The result is a more unified mixed-methods approach that uniquely links the evidence we collect to learning processes and outcomes. 

This course includes a presentation of different coding techniques, including qualitative, AI-supported, and other machine learning methods. The course includes curated readings, videos (with transcripts), interactive lectures, hands-on exercises, and collaborative group activities designed to build participants' capacity to design, implement, and evaluate QE studies that integrate quantitative and qualitative epistemologies. Participants will create EN and interpret ENA models, discuss integrating social justice into QE research, and learn strategies to navigate the challenges of incorporating QE into existing research practices. The target audience for this course is graduate students, early and mid-career researchers, and educators committed to leveraging research for social change. Prerequisite knowledge of general qualitative or quantitative approaches to education research is helpful, but not required. Materials and software, including datasets and instructions, will be provided during the course. Participants will need a laptop with internet access to engage in hands-on activities and collaborative group work.

The goal of this course is to prepare education professionals to responsibly adopt AI within assessment systems that are both technologically advanced and equity-driven. This training offers a practical, hands-on introduction to AI-enhanced assessment systems, focusing on four key areas: understanding and using large language models (LLMs), exploring ethical considerations for using AI-based assessments, using advanced machine learning for cheating detection, and assessing evolving definitions of validity. The course blends presentations, live demonstrations, guided coding activities, and small-group collaboration. By the end of the course, participants will be equipped with tools, strategies, and insights to responsibly apply AI in their own educational contexts, supporting more equitable, valid, and forward-looking assessment practices. Designed for a wide range of educational professionals, including graduate students, early-career scholars, researchers, education administrators, and policymakers, the course is accessible to those with basic knowledge of assessment and general familiarity with AI concepts. No programming experience is required. Support will be provided for varying levels of technical proficiency. All materials, including open-source code notebooks, datasets, slides, and prompts, will be distributed in advance.

This course focuses on Learning Analytics (LA) foundations with a STEM education focus. Learning Analytics (LA), as a computational research methodology, increases the capacity to understand and improve STEM learning and learning environments through the use of new sources of data and powerful analytical approaches. This course will cover methodologies, literature, hands-on application using educational data, and ethical issues as they relate to STEM education. Participants will develop basic proficiency with R and RStudio and apply computational analysis techniques (including data visualization) relevant and appropriate to their STEM education research interests. The first part of the course lays the foundations of LA and R programming basics. The second part uses that base to dive into data visualizations in a STEM education context. Course activities include conceptual overviews, guided code-alongs, blended-learning labs, small group discussions, and individual consultations. The target audience includes those who aim to leverage new data sources and apply computational methods in R Studio following the LA workflow. The level of instruction will be appropriate for those with little or no experience using R, a popular free open- source software program for data science, research, and technical communication.  Knowledge of basic descriptive and exploratory analysis is assumed. Participants are required to bring a laptop computer with R and Rstudio downloaded.

This course provides researchers with a primer on use and analyses of the many free nationally-representative and state-representative PreK-12 teacher and principal survey datasets available through the RAND American Educator Panels (AEP). Some of the many datasets available for analyses include the COVID-19 Survey, the American Instructional Resources Survey, and the American Mathematics Educator Study. These large-scale datasets are publicly available for analysis to examine broad topics and research questions in education research. Course participants will: (1) understand key features of probability-based sampling that ensures nationally representative survey data and the importance of methodological transparency in provision of that data; (2) identify key AEP datasets for addressing their interests and research questions; (3) produce basic descriptive data regarding survey items of interest to them; (4) examine subgroup comparisons for survey items of interest; and (5) consider appropriate data visualizations for displaying results. The course will include a series of short presentations and small group activities. This course is intended for a broad audience, including faculty, researchers, and graduate students interested in using AEP data for their research, as well as policymakers and practitioners interested in learning more about the data. There are no prerequisites for attendance or critical pre-work. Participants should bring a laptop. Those who wish to use a statistical package to explore data should have that package installed on their laptops prior to the workshop.

This course will focus on advanced meta-analysis models and is designed to follow the introduction to systematic review and meta-analysis course given in prior AERA Professional Development training courses. Course objectives are to (a) apply and estimate models that appropriately reflect the multilevel and correlated structure of meta-analysis data, (b) apply and interpret meta-regression models for dependent effect sizes that explore effect size heterogeneity, and (c) apply and interpret the latest methods for exploring the potential impact of publication bias and selection models in a meta-analysis with dependent effect sizes. Course activities will include lectures and hands-on exercises using data from published meta-analyses. 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. The statistical package R will be used to conduct the statistical techniques discussed. Participants who need support with using R will be provided with materials prior to the workshop, and assistance will be available during the workshop. Participants are required to bring a laptop computer and encouraged to bring their own research to the workshop.

This course provides foundational knowledge about missing data analysis. The course content includes missing data assumptions, Markov Chain Monte Carlo (MCMC) estimation, data imputation, incomplete categorical variables, and interaction effects. Participants will learn how to describe key assumptions regarding the reasons for missingness, compare and contract Bayesian and Frequentist statistical paradigms, understand and describe the process for MCMC, and write a script that applies MCMC estimation to a variety of models. The course includes a mixture of lecture and demonstrations. The 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. 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.

This interactive course provides a foundational introduction to autoethnography, a powerful qualitative research method in which researchers study their own lives to produce scholarly insights for their fields. Participants will learn autoethnography’s core definition, explore its four major types (Evocative, Analytic, Critical, Performance/Arts-Based), and discuss when and when not to use it. The course will delve into crucial ethical considerations and quality criteria, ensuring a robust and responsible approach to autoethnographic inquiry. Through hands-on activities, participants will gain practical strategies for identifying compelling topics, formulating central research questions, generating diverse forms of 'data' (like memories and sensory experiences), transforming this data into meaningful analysis, and crafting engaging narratives using expressive writing techniques. No pre-course assignments are required.

Through engaging in this course, experienced, developing, and emerging qualitative researchers will acquire and build their analytic tools for use in their narrative research projects. The course begins by identifying philosophic bases for narrative research. Next, participants will select four of the eight 45-minute workshops offered including 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 parallel and serial interpretation. During the course, researchers will learn the theoretical underpinnings of the tool, have hands-on experiences using it, and receive guidance in developing skill through interaction with experienced, published researchers who engage in narrative research using the tool they are teaching. Developing versatility and strength in analytic skills will enable researchers to produce more nuanced and trustworthy findings.