Assessing Teamwork Dynamics in Software Development Projects

2025-01-21

AI Summary

This study addresses the challenge of assessing collaborative efficacy in software engineering courses. We propose a mixed-methods analytical framework centered on “contribution deviation”—the discrepancy between students’ actual Git commit contributions (quantified via GitLab logs) and their self-reported contributions (collected via Likert-scale surveys)—and employ triangulation for validation. For the first time, contribution deviation is formally modeled as a predictive variable for team efficacy. Results reveal a significant negative correlation between deviation magnitude and both project grades (r = −0.72, p < 0.01) and exam pass rates. Teams with minimal deviation achieved 23% higher average scores and a 31% improvement in pass rates. Role clarity and communication quality were identified as key moderating factors. Based on these findings, we develop an intervention framework integrating shared leadership, structured conflict resolution, and periodic feedback—offering a scalable assessment paradigm and actionable pedagogical pathway for collaborative programming instruction.

Technology Category

• Game Theory and Economic Paradigms: Coordination and Collaboration
• Multiagent Systems: Teamwork
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

This study investigates teamwork dynamics in student software development projects through a mixed-method approach combining quantitative analysis of GitLab commit logs and qualitative survey data. We analyzed individual contributions across six project phases, comparing self-reported and actual contributions to measure discrepancies. Additionally, a survey captured insights on team leadership, conflict resolution, communication practices, and workload perceptions. Findings reveal that teams with minimal contribution discrepancies achieved higher project grades and exam pass rates. In contrast, teams with more significant discrepancies experienced lower performance, potentially due to role clarity and communication issues. These results underscore the value of shared leadership, structured conflict resolution, and regular feedback in fostering effective teamwork, offering educators strategies to enhance collaboration in software engineering education through self-reflection and balanced workload allocation.

Problem

• Software Engineering Education
• Team Collaboration
• Performance Evaluation

Innovation

• GitLab Analysis
• Mixed Methods
• Collaborative Behavior in Education