G-Learn: A Graph Machine Learning Content Recommendation System for Virtual Learning Environments

Recommender Systems in Virtual Learning Environments (VLEs) provide personalized suggestions to users based on preferences, interaction history, and behavior. They enhance learning by offering personalized content, increasing engagement, and improving teaching effectiveness. Challenges in VLEs include the cold start problem, data sparsity, and limited coverage. To address these, we propose G-Learn, a recommendation system operating in both supervised and unsupervised models. It utilizes graph machine learning, keyword mining, and similarity techniques to recommend educational materials tailored to each student's performance. We demonstrate G-Learn's effectiveness in a real scenario using data from Homero, a VLE for computer science education developed for the brazilian federal government. Validation shows an average f1-score of 0.64 in unsupervised model and 0.95 in supervised model.