Analysis of Student GitHub Repository Activity Patterns in Web Framework Programming Courses using K-Means Clustering - Yori Adi Atma; Andrew Kurniawan Vadreas (TIN: Terapan Informatika Nusantara, 2026)
Instructors in project-based web framework courses frequently lack objective mechanisms to monitor individual student development progress, particularly when engagement occurs asynchronously across multiple weeks and repository activity is not systematically analyzed. This study aims to identify distinct behavioral engagement profiles among students using GitHub repository activity data, and to demonstrate the utility of unsupervised machine learning as a scalable progress monitoring tool for project-based programming courses. The K-Means Clustering algorithm was applied to analyze repository activity patterns of 73 students enrolled in a Web Framework Programming course using Laravel at a vocational higher education institution. Five behavioral features were extracted from each student's GitHub repository, namely total_commit, active_days, avg_commit_per_day, weekend_commit, and last_commit_gap. Following data normalization using StandardScaler, the optimal number of clusters was identified as k=3 using the Elbow Method. The clustering analysis revealed three distinct behavioral profiles: Cluster 0 (51 students, 69.86%) as Passive Learners characterized by low commit activity and a high last_commit_gap indicating deadline-driven development behavior; Cluster 2 (20 students, 27.40%) as Productive Learners demonstrating substantially higher commit intensity and broader repository engagement; and Cluster 1 (2 students, 2.74%) as Highly Consistent Learners exhibiting stable, multi-session repository interaction throughout the project period. As an initial validation of clustering quality, the Silhouette Score of 0.4041 confirms a moderate yet meaningful partition structure within the dataset. The primary contribution of this study lies in demonstrating that mandatory GitHub repository submissions, already required in most project-based programming courses, can be repurposed into an objective, low-cost behavioral monitoring instrument without additional data collection burden. This contributes a replicable, repository-based learning analytics framework that enables instructors to objectively classify student project engagement, supporting early instructional intervention and more process-oriented assessment strategies in software engineering education.