Utilizing the Constrained K-Means Algorithm and Pre-Class GitHub Contribution Statistics for Forming Student Teams
- UNCW Author/Contributor (non-UNCW co-authors, if there are any, appear on document)
- Yang Song (Creator)
- Institution
- The University of North Carolina Wilmington (UNCW )
- Web Site: http://library.uncw.edu/
Abstract: In modern software engineering education, team formation is crucial for mimicking real-world collaborative scenarios and boosting project-based learning outcomes. This paper introduces a simple, innovative, and universally adaptable method for forming student teams within a software engineering class. We utilize publicly available pre-class GitHub metrics as our input variables (e.g., number of commits, pull requests, code size, etc.). For team formation, the constrained k-means algorithm is employed. This algorithm embracesdomain-specific constraints, ensuring the resulting teams not only resonate with the inherent data clusters but also meet educational requirements. Preliminary results suggest that our methodology yields teams with a harmonious blend of skills, experiences, and collaborative potentials, thereby setting the stage for enhanced project success and enriched learning experiences. Quantitative analyses show that teams formed via our approach outperform both randomly assembled teams and student self-selected teams concerning project grades. Moreover, teams created using our method also display a reduced standard deviation in grades, suggesting a more consistent performance across the board.
Utilizing the Constrained K-Means Algorithm and Pre-Class GitHub Contribution Statistics for Forming Student Teams
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Created on 8/16/2024
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Additional Information
- Publication
- Cui, J.; Zhou, F.; Liu, C.; Jia, Q.; Song, Yang ; & Gehringer, E. (2024, July). Utilizing the constrained K-Means algorithm and pre-class GitHub contribution statistics for forming student teams. ITiCSE 2024: Proceedings of the 2024 on Innovation and Technology in Computer Science Education (vol. 1), (pp. 569-575). https://doi.org/10.1145/3649217.3653634
- Language: English
- Date: 2024
- Keywords
- Software Engineering Education, Teamwork and Collaboration, Qualitative Study, Statistical Study, GitHub