2023 AERA Virtual Research Learning Series

AERA 2023 Virtual Research Learning Series


Professional Development and Training Courses

June 1–September 21

All Courses 1–5 pm ET

Fee Per Course $40 AERA Members / $55 Non-AERA Members

Click here to view the course recordings


All courses register here
Advanced Meta-Analysis

Thursday, June 1, 2023



Terri D. Pigott, Georgia State University

Tasha Beretvas, The University of Texas at Austin

Ryan Williams, American Institutes for Research

Wim Van den Noortgate, KU Leuven


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 course. The activities will include lecture, hands-on exercises, and individual consultation. 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.


Access to and Use of the Trajectories into Early Career Research Data Set: An 8-Year Longitudinal Mixed Methods Data Set of Biological Sciences Ph.D. Students
Thursday, June 15, 2023

David Feldon, Utah State University

Kaylee Litson, Utah State University  

The Trajectories into Early Career Research dataset contains 8 years of surveys (biweekly and annual), interviews, and performance-based data from a national cohort of 336 Ph.D. students who matriculated into U.S. biological sciences programs in Fall, 2014. These deidentified data will be publicly released on the Open Science Framework data repository in 2023. This course will (1) teach participants how to access data and documentation, (2) introduce the instruments, interview protocols, anddata formats, (3) provide instruction and code to prepare data for analysis, and (4) facilitate discussions of participant-identified research questions and analytic techniques.

The course consists of an overview lecture introducing the data set and major study findings to date, live demonstrations and hands-on practice accessing and structuring data. We recommend (but do not require) participants have data analysis software readily available. Participants will leave the course with downloaded, pre-processed data appropriate to their research questions/methods, reference materials to support future data access and analysis, and copies of literature reporting key methods and findings from the data set.


The course is geared toward graduate students and early-mid career scholars—especially those whose access to data was disrupted by the pandemic—with interests in postsecondary education, transitions into STEM careers, adult learning and motivation, research training, and/or longitudinal or mixed methods analytic techniques.


Text Classification for the Pursuit of Truth with Qualitative Evidence: No-Code Machine Learning Via Latent Code Identification
Tuesday, July 11, 2023

Manuel S. Gonzalez Canche, University of Pennsylvania


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.


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 analytic output provides complete transparency of the classification process and aids to recreate the contextualized meanings embedded in the original texts.


Prior to the course participants are encouraged to read these two articles:


González Canché, M. S. (2023). Machine Driven Classification of Open-Ended Responses (MDCOR): An analytic framework and free software application to classify longitudinal and cross-sectional text responses in survey and social media research. Expert Systems with Applications, 215https://doi.org/10.1016/j.eswa.2022.119265


González Canché, M. S. (2023). Latent Code Identification (LACOID): A machine learning-based integrative framework [and open-source software] to classify big textual data, rebuild contextualized/unaltered meanings, and avoid aggregation bias. International Journal of Qualitative Methods, 22. https://doi.org/10.1177/16094069221144940


You can access the articles HERE.

An Introduction to Social Network Analysis and Education Research: Core Concepts and Applications with R
Thursday, August 10, 2023

Shaun B. Kellogg, North Carolina State University

Bodong Chen, University of Pennsylvania

Oleksandra Poquet, Technical University of Munich

Jeanne M. McClure, North Carolina State University


Although social network analysis (SNA) and its educational antecedents date back to the early 1900s, the popularity of social networking sites have raised awareness of and renewed interests in networks and their influence. Moreover, as the use of digital resources continues to expand in education, data collected by these educational technologies and corresponding advances in computing power has also greatly facilitated the application of network analysis in education research. This course is designed to introduce education researchers with little or no background in SNA to social network theory, examples of network analysis in educational contexts, and applied experience analyzing real-world data sets. To support scholars’ conceptual understanding of SNA as both a theoretical perspective and an analytical method, the instructors will provide short presentations and facilitate peer discussion on topics ranging from broad applications of SNA in educational contexts to specific approaches for data collection and storage. This course will also provide scholars with applied experience analyzing network data through code-alongs and interactive case studies that use widely adopted tools (e.g., R, RStudio, and GitHub) and demonstrate common techniques (e.g, network visualization, measurement, and modeling). Collectively, these activities will help scholars both appreciate and experience how SNA can be used to understand and improve student learning and the contexts in which learning occurs. While prior experience with R, RStudio, and GitHub is recommended to complete more advanced activities, it is not required.


Qualitative Meta-Synthesis as a Means to Interrogate Education Research and Achieve Equity
Thursday, September 7, 2023

Nuria Jaumot-Pascual, TERC

Lisette Esmeralda Torres-Gerald, TERC

Christina Silva, TERC
Maria (Mia) Ong, TERC


Qualitative meta-synthesis is a rigorous and innovative approach to analyzing findings from multiple qualitative education research studies that have been determined to meet pre-established criteria (e.g., area of research, methodology used). Instructors from the Institute for Meta-Synthesis will teach theory and techniques for qualitative meta-synthesis, with a main goal of preparing participants to interrogate their topics in education research towards equity. The instructors have successfully completed and published on multiple meta-synthesis projects on equity topics in STEM education; examples and activities will be based on their research data. Despite its potential to help address issues of equity in education and to provide policy guidance at the national level, meta-synthesis is a methodology that is rarely introduced to graduate students. This course will address this knowledge gap for graduate students by teaching participants how to conduct qualitative meta-synthesis research, with a particular emphasis on justice-oriented aims and equitable research practices using examples from STEM education. Furthermore, skills learned for meta-synthesis may be applied to other important research tasks, such as conducting searches for literature reviews. This course is geared towards graduate students and early career scholars. Those interested in participating in this course ideally should have familiarity with literature reviews and qualitative research literature, though that is not required.


Developing Actionable Research Questions and Moving into Study Design
Thursday, September 21, 2023

Leslie Nabors Oláh, University of Pennsylvania

William N. Thomas, American University


This inquiry-based methods course will focus on how researchers can take a problem of practice or topic of interest and transform it into a set of researchable questions. Participants will come to the course with a research question, and we will focus on improving it by interrogating its main concepts and unit(s) of analysis. This course is particularly relevant to practitioner researchers, executive doctoral and master's students, and early-career educational researchers. This will be a creative, collaborative, and constructively critical space of inquiry and support. Taught by two established researchers who are faculty in doctoral programs in education, this seminar will be hands-on and supportively critical. The goal is for every participant to leave with a set of research questions and a plan for next steps in research design. Participants will be asked to submit their draft research questions or topics on a shared document prior to the course. Required material and software include a word-processing program and access to Padlet and Google Docs.



George L. Wimberly, Ph.D.
Virtual Research Learning Series, Director
For more information please contact