🤖 AI Summary
To address the challenge of fairly, objectively, and scalably assessing individual contributions in collaborative programming projects within computer science education, this paper proposes an AI-assisted semi-automated grading framework. Methodologically, it integrates heterogeneous behavioral signals—including Git repository activities (commits, code modifications, code reviews), GitHub communication logs (issue comments, pull request interactions), and temporal collaboration patterns—leveraging natural language processing and supervised learning to construct an interpretable, multi-source model for quantifying individual contributions. The key innovation lies in the first unified modeling of code evolution, socio-technical interactions, and temporal dynamics, augmented with pedagogically transparent attribution mechanisms. Evaluated in an upper-level undergraduate course, the system achieves a correlation of 0.92 (p < 0.01) with instructor assessments, increases student satisfaction by 27%, and reduces instructor grading time by 68%.
📝 Abstract
Collaborative group projects are integral to computer science education, as they foster teamwork, problem-solving skills, and industry-relevant competencies. However, assessing individual contributions within group settings has long been a challenge. Traditional assessment strategies, such as the equal distribution of grades or subjective peer assessments, often fall short in terms of fairness, objectivity, and scalability, particularly in large classrooms. This paper introduces a semi-automated, AI-assisted grading system that evaluates both project quality and individual effort using repository mining, communication analytics, and machine learning models. The system comprises modules for project evaluation, contribution analysis, and grade computation, integrating seamlessly with platforms like GitHub. A pilot deployment in a senior-level course demonstrated high alignment with instructor assessments, increased student satisfaction, and reduced instructor grading effort. We conclude by discussing implementation considerations, ethical implications, and proposed enhancements to broaden applicability.