2024 ASEE Annual Conference & Exposition

Analyzing Individual Contribution in Team-based Software Engineering Projects

Presented at Software Engineering Division (SWED) Technical Session #3

While collaboration improves student learning outcomes, it is necessary to understand how students collaborate to help instructors make informed decisions on designing effective team-based learning activities. In this paper, we analyze the collaboration practices of 45 teams on team-based programming assignments in an undergraduate software engineering course. We used objective metrics based on each team's commit history and branching activity in Git. Also, we used subjective metrics based on the effort reported by each team. The results demonstrate that teams have an unequal work distribution, which leads to a potentially unbalanced learning environment. Based on the results, we propose that instructors design team-based learning activities to enable equal contributions from all team members.

Authors

Joydeep Mitra Northeastern University [biography]

Joydeep Mitra is an assistant teaching professor in the Khoury College of Computer Sciences at Northeastern University, based in Boston.

Mitra’s research interests center around mobile app security and privacy. He has been awarded two Android security rewards by Google for discovering vulnerabilities in the Android platform. Further, he is passionate about the opportunity to train the next generation of computing professionals. He gets immense joy from teaching students how their favorite programming languages work under the hood, as well as developing new teaching methods and evaluating existing ones to understand what engages students. He was previously awarded a Faculty Fellowship by Stony Brook University to study the effects of the Process Oriented Guided Inquiry Learning methodology in a large classroom.

Mitra has published in the Journal of Empirical Software Engineering and the Technical Symposium of Computer Science Education (SIGCSE TS), and has presented at both the International Conference on Predictive Models and Data Analytics in Software Engineering and the International Workshop on Advances in Mobile App Analysis. Additionally, he has served as a journal reviewer for SIGCSE TS and Transactions in Software Engineering and Methodology.
Amir Kirsh

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