Skill Tracker: An Optimization-Based Approach for Automated Student Team Formation in Projects

Abstract

This paper presents Skill Tracker, an automated platform designed to bridge the gap between student talent and project requirements in Tier-3 engineering colleges. Traditional team formation methods are often times a manual, time-consuming and primarily focuses on academic performance, thus overlooking non-technical skills and lacking efficient team-balancing mechanisms. To address this issue, Skill Tracker is developed using the MERN Stack as a centralized platform that collects and processes structured student data. The system parses resume to identify both technical expertise (e.g., React, Java) and creative or interpersonal skills (e.g., dancing, leadership). In addition, it incorporates a Fitness Score Algorithm to evaluate compatibility and optimize team formation based on skill diversity, preferences, and availability. The implementation of Skill Tracker reduces the manual effort required by faculty and project coordinators while improving the efficiency and fairness of team allocation. Teams formed using this system demonstrate better balance in skill sets, leading to improved collaboration and overall project performance. The primary contribution of this work is the introduction of a scalable, data-driven approach to team formation that emphasizes holistic skill evaluation. Skill Tracker enhances both administrative efficiency and effective utilization of student potential in academic environments.