2026 ASEE Annual Conference & Exposition

One Seminar, Six Universities: A Shared Approach to Launching Learning Assistant Programs Across Computing Courses

Presented at Computers in Education (CoED): AI in Education (5 of 9) -- T408C

This paper describes the design and implementation of the evidence-based Learning Assistant (LA) model across computing courses at six universities representing varied institutional contexts, including small private, rural regional, medium and large public institutions across the eastern and western United States. The initiative engaged 27 computer science students as LAs over four semesters (Fall 2024-Fall 2025) to facilitate active learning in 17 courses enrolling a total of 2,732 students. These courses included Data Structures, Human-Computer Interaction, and other key computing courses identified as critical to student retention and success. The overarching goal was to expand access to student-centered learning and build sustainable institutional capacity for peer-supported instruction.

Because most partner institutions were initiating LA programs for the first time, the project adopted a shared implementation structure: a centralized, online synchronous LA pedagogy seminar coupled with locally adapted faculty-LA collaborations. This approach attempted to reduce barriers typically associated with launching new programs while allowing each institution to refine the model to fit local contexts.

Assessment of implementation draws on faculty interviews (n=12), LA reflections (n=80 from 17 unique LAs) and student surveys (n=958) to examine how faculty and LAs experience and enact the model. Guided by Henderson et al.'s framework for educational change, the analysis explores how individual reflection, shared vision, and emerging institutional structures interact to support sustainable adoption. Preliminary findings reveal strongest evidence within reflective teacher development, where LAs demonstrated a shift from viewing themselves as "helpers" to "facilitators" who apply pedagogical frameworks to computing-specific contexts. The paper shares lessons learned and outlines next steps for evaluating learning outcomes and institutional change processes, contributing to broader efforts to scale evidence-based instructional practices in engineering education.

Authors

Hagit Kornreich-Leshem Florida International University [biography]

Dr. Hagit Kornreich-Leshem is Research Associate Professor and Director of the Learning Assistant (LA) Program at Florida International University's STEM Transformation Institute. She completed a Ph.D. in Chemistry at the Weizmann Institute of Science and postdoctoral research in Chemical Biology at the University of Bonn, Germany. With over 15 years of experience in K-12 and undergraduate education, Hagit prepares LAs to facilitate student learning and supports faculty in transforming their classrooms into collaborative learning environments. Her work is focused on designing effective STEM learning environments based on the LA Model, recently collaborating on multi-institutional LA program implementation in computing courses, the effect of LA practices on student success and persistence in STEM and AI tools for institutional change. She has published in chemistry and STEM education and have trained in Israel, Germany and the US.
Vijayalakshmi Ramasamy http://orcid.org/0000-0001-7848-2247 Georgia Southern University [biography]

Vijayalakshmi (Viji) Ramasamy is an Assistant Professor of Computer Science at Georgia Southern University. Her research interests include AI in education, graph analytics, and computational approaches to broadening participation in computing. She is PI on an NSF CUE-T collaborative award ($2M) focused on active learning strategies in multi-institution online synchronous learning environments. She previously held tenured faculty positions at the University of Wisconsin-Parkside and visiting appointments at Miami University and the University of South Australia. She has more than 83 refereed publications, serves on NSF review panels, and is a faculty advisor for Girls Who Code.
Prof. Sherrene Bogle Cal Poly Humboldt [biography]

Dr. Sherrene Bogle is Professor and Chair of the Department of Computer Science at Cal Poly Humboldt.
Her research resides at the intersection of machine learning, predictive analytics, and educational technology,
including integrating generative AI into STEM education and using topic modeling to analyze student
experiences. Dr. Bogle focuses on improving student outcomes through technological intervention and
expanding computing access for underrepresented groups. A Fulbright Scholar, she earned her Ph.D. in
Computer Science from the University of Georgia, where she specialized in sentiment analysis and market
prediction models. She has several peer reviewed publications in the areas of Machine Learning, Time
Series Predictions, Predictive Analytics, Computer Science Education and E-Learning Technologies.
Dr. Mourya Reddy Narasareddy Gari Rider University [biography]

I am an Associate Professor at Rider University, and my research interests include Software Engineering and Human-Computer Interaction.
Dr. Peter J Clarke http://orcid.org/0000-0003-0892-1907 Florida International University [biography]

Peter J. Clarke received his B.Sc. degree in Computer Science and Mathematics from the University of the West Indies (Cave Hill) in 1987, M.S. degree from SUNY Binghamton University in 1996 and Ph.D. in Computer Science from Clemson University in 2003. His research interests are in the areas of software engineering, software testing, model-driven software development, and computer science education. He is currently an associate professor in the Knight Foundation School of Computing and Information Sciences at Florida International University. He is a member of the ACM (SIGSOFT, SIGCSE, and SIGAPP); IEEE Computer Society; and a member of the Association for Software Testing (AST).

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 21, 2026, and to all visitors after the conference ends on June 24, 2026

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