Instructors: Lorri Many Rivers Johnson Santamaría, Mixteco Indígena Community Organizing Project (MICOP); (course director) Cristina Corrine Santamaria Graff, Indiana University - Purdue University at Indianapolis

The Emotion Coding Technique (Lustick, 2021) is a systematic method of qualitative analysis that captures emotions as they arise and helps us process them as information about us, our participants, and our research objectives. In this course, we will review the emotion coding technique, which applies a set of reflexive questions to a chunk of data (Lustick, 2021). We will then talk about some of the complexities of naming and reflecting on emotions during data analysis. We will share our own best practices and hear some additional strategies from the instructor, including an emotion wheel to choose from. Lastly, we will shift into independent work time to practice and reflect on the technique. The course is open to all qualitative and mixed methods researchers, with graduate students and early career researchers in mind. Please have basic qualitative and mixed methods training, including an understanding of positionality and reflexivity. You are advised, though not required, to have available original qualitative data, such as an interview transcript, with which to practice the technique.

This course provides faculty, students, and other researchers with a primer on use and analyses of the many free teacher and principal survey datasets available through the RAND American Educator Panels (AEP). Participants will: (1) understand key features of probability-based sampling that ensures nationally representative survey 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.

This course will focus on preparing education researchers to use state-of-the-art tools in artificial intelligence (AI) and machine learning in educational contexts. The course will cover topics such as applications of AI in education for prediction and classification, model evaluation via performance metrics, human feedback in AI model development, randomized control trials, and cost-benefit analysis. There will also be a strong emphasis on data ethics and responsible AI throughout the session. The course will be intended for those who have at least some programming and statistics background. The goal for the course is to introduce core tools and concepts in artificial intelligence and machine learning and familiarize participants with potential use cases via examples in educational settings. Required material include an installation of Jupyter notebooks or a google colab account.

The purpose of this course is to introduce the definition, identification, estimation, and sensitivity analysis for causal moderated mediation effects under the potential outcomes framework. Participants will also learn how to use a user-friendly R package to conduct the analysis and visualize results. The method introduction and the package implementation will be illustrated with a re-analysis of the National Evaluation of Welfare-to-Work Strategies (NEWWS) Riverside data.

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.

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.

This course is designed to introduce education researchers with little or no background in social network analysis (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.

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, 215. https://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

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, and data 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.