Implementing active learning in massive classes (300–500 students) presents a logistical paradox: high-impact collaborative projects often struggle with logistical hurdle, leading to student frustration. This study presents a multi-semester SoTL research project using Semantic Network Analysis (SNA) to iteratively refine a "Digital Workbook" project in Principles of Macroeconomics. The research utilized student surveys from Spring 2024 and Spring 2025, analyzed via MAXQDA and UCINet. By applying the Girvan-Newman algorithm and betweenness centrality measures, we mapped the "semantic landscape" of student experiences. The 2024 analysis identified confusing logistics and teammate conflict as central nodes of struggle. In response, specific "Design Moves" were implemented in Spring 2025: mandated team-building activities (e.g., social outings), formal group contracts, and sequential task-scaffolding in Canvas. Results from the Spring 2025 network (Q=0.539) demonstrate a significant shift in the student experience. "Building relationships" (n=149) and "Improved learning" (n=122) emerged as primary success clusters, suggesting that design moves mitigated the typical frustrations of large-scale group work. This paper demonstrates how network analysis allows instructors to move beyond anecdotal feedback to make surgical, data-driven design changes. It offers a scalable framework for maintaining the pedagogical benefits of constructivism - creativity, peer review, and real-world application - within the large enrollment classes.

Authors:
Natalia Pakhotina, Texas A&M University, United States
Robert Jacob Robbins, Texas A&M University, United States
Mariafernanda Chavez Martinez, Texas A&M University, United States

About the Presenter(s)
Dr Natalia Pakhotina is a University Associate Professor/Senior Lecturer at Texas A&M University in United States

Connect on Linkedin
https://www.linkedin.com/in/nataliya-pakhotina

See this presentation on the full schedule – Saturday Schedule

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