Structured Peer-to-Peer Skill Development with Faculty Oversight

Abstract

Effective skill sharing among college students enhances learning opportunities and fosters collaborative growth. However, existing platforms often lack personalized matching and verification mechanisms, limiting their effectiveness and trustworthiness. This research presents a novel peer-to-peer skill exchange platform designed to facilitate efficient matching between students and to connect them with college faculty for skill verification. Leveraging a hybrid recommendation system combining content-based and collaborative filtering, the platform intelligently pairs users based on skills, interests, and past interactions. Faculty allocation is managed through a rule-based matching algorithm that assigns domain experts for verification, ensuring quality and credibility. The system’s performance was evaluated through user engagement metrics, satisfaction surveys, and verification accuracy. To build user confidence and transparency, the platform incorporates explainable features that clarify match recommendations and verification assignments. The entire framework is deployed as a web application, offering an intuitive interface for skill exchange, real-time matching, and faculty verification. The proposed solution demonstrates how integrating intelligent matching algorithms and expert validation can create a trustworthy, scalable, and user-centric environment for peer learning in college settings.