Data-Driven Peer Recommendation and Its Applications in Extracurricular Learning

Abstract

This paper introduces a system designed to enhance extracurricular learning by recommending suitable peer helpers. The system integrates educational big data of knowledge and learner models to optimize peer learning opportunities with learning analytics. Utilizing a graph-based recommendation algorithm, it incorporates three distinct indicators to dynamically match learners with suitable peers based on specific learning contexts and needs. A significant feature of the system is its capability to visualize the knowledge and proficiency levels of potential helpers, thereby empowering learners to make well-informed decisions with less bias. The system's adaptability also permits educators to tailor it to meet diverse educational goals. We are conducting an experiment with this system during a university's paper reading course to test its usability. The system aims to reduce the burden on teachers and minimize the time students spend seeking help outside of class.