PD26-10 Introduction to Quantitative Ethnography and Epistemic Network Analysis in the Age of AI

Session Type: Professional Development Course

Abstract

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.

Sub Unit

• Professional Development and Training Committee

Instructors

• Adaurennaya C. Onyewuenyi, The College of New Jersey

• Zachari Swiecki

• Andrew Ruis, University of Wisconsin