2026 ASEE Annual Conference & Exposition

Peer Instruction for Undergraduate Data Science Education in India– A Collaborative Pilot Study

Presented at Computers in Education (CoED): Learning, Engagement & Inclusion (3 of 9) -- M508A

In this pilot study, we describe the adoption of Peer Instruction (PI) in an undergraduate Data Science classroom, at the School of Computing at a large University in southern India. During PI, students actively participate in their learning through small group peer discussions in real time while they answer conceptual questions posed by the instructor. This research work is one of the results of a week-long hands-on workshop conducted by American engineering educators introducing Peer Instruction (PI), an active teaching-learning pedagogy, to the academic context in India. In this international collaborative work, we studied the effectiveness of PI as a pedagogy for teaching the Fundamentals of Data Science in an in-person undergraduate Data Science and Business Systems classroom in a large University in southern India. Several studies on PI in engineering courses exist, but very few studies focus on PI in the topic of Data Science Business Systems in general and/or Fundamentals of Data Science in particular. The main research question in this pilot study focuses on comparing correctness gains for students learning Fundamentals of Data Science through Peer instruction and lecture-based instruction. Traditional lecture-based teaching was conducted in the academic year 2023-2024. PI-based teaching was conducted in the academic year 2024-2025. Ten PI questions were developed for Fundamentals of Data Science. Pre and post-course surveys were conducted through Google Forms. This paper analyses 48 students' responses to determine if PI has helped students learn and recognize the fundamentals of data science more effectively than lectures. The results show significant correctness gains for students with PI over lectures. Average answer correctness for students improved from 2.38 to 9.29 out of a possible maximum of 10. Statistical significance of student scores were compared using ANOVA tests. We found a statistically significant correlation between using PI and improved student scores, than through traditional lectures. PI as an approach seemed to have helped students obtain clarity in their understanding, and seemed to have leveraged the social aspect of learning, as students often seemed to find explanations from peers more relatable and easier to understand than those from instructors.

Authors

Geetha Jenifel M [biography]

First Author: M. Geetha Jenifel, Ph.D, Assistant Professor,
Department of Data Science and Business Systems,
SRM Institute of Science and Technology
jenifel.it@gmail.com, Orcid: 0000-0002-0961-3722
Second Author: Bhuvana Gopal, Ph.D, Assistant Professor of Practice
School of Computing
University of Nebraska–Lincoln
bhuvana.gopal@unl.edu, Orcid: 0000-0002-1796-2789.
Third Author: Byrav Ramamurthy, Professor, School of Computing
University of Nebraska–Lincoln
Dr. Bhuvana Gopal http://orcid.org/0000-0002-1796-2789 University of Nebraska - Lincoln [biography]

Dr. Bhuvaneswari (Bhuvana) Gopal is an Assistant Professor of Practice in the School of Computing at the University of Nebraska-Lincoln where she teaches Software Engineering, Software Security in Practice, the industry internship course, and leads the Learning Assistant Program that serves several computing courses at the School of Computing. Dr. Gopal is a College of Engineering Teaching Fellow at UNL. Dr. Gopal has extensive experience in the software industry, where she spent 14 years in various roles, including Software Architect and Lead software engineer positions before switching to full time academia. She is also a Microsoft certified professional, with an MCPD certification. Her research work is focused on evidence-based, active learning pedagogies to improve software engineering education. She has published papers specifically on Peer Instruction and Process Oriented Guided Inquiry-based Learning-like pedagogical approaches in the undergraduate software engineering classroom, both in person and virtual. She holds a Bachelors degree in Physics from the University of Madras, Chennai, India. She holds two Masters degrees - one in Computer Science from the University of Nebraska-Lincoln, and one in Medical Physics from Anna University, Chennai, India. She holds a Ph.D. in Computer Science from the University of Nebraska-Lincoln.
Byrav Ramamurthy http://orcid.org/0000-0002-4104-7513 University of Nebraska - Lincoln [biography]

Byrav Ramamurthy is currently a Professor in the Department of UNL School of Computing at the University of Nebraska-Lincoln (UNL). He is the author of the book "Design of Optical WDM Networks - LAN, MAN and WAN Architectures" and a co-author of the book "Secure Group Communications over Data Networks" published by Kluwer Academic Publishers/Springer in 2000 and 2004 respectively. He served as the Chair of the IEEE Communication Society's Optical Networking Technical Committee (ONTC) during 2009-2011. He served as the IEEE INFOCOM 2011 TPC Co-Chair. He is currently the Editor-in-Chief for the Springer Photonic Network Communications (PNET) journal.

His research areas include optical and wireless networks, peer-to-peer networks for multimedia streaming, network security and telecommunications. His research work is supported by the U.S. National Science Foundation, U.S. Department of Energy, U.S. Department of Agriculture, NASA, AT&T Corporation, Agilent Tech., Ciena, HP and OPNET Inc.

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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