Enhancing Math Learning in an LMS Using AI-Driven Question Recommendations
This work addresses math learning for students in LMS environments, but it is incremental as it applies existing methods to a specific domain.
This paper tackled the problem of enhancing math learning in a Learning Management System by using AI-driven recommendations for similar math questions, finding that Self-Organizing Maps yielded higher user satisfaction compared to cosine similarity and Gaussian Mixture Models.
This paper presents an AI-driven approach to enhance math learning in a modern Learning Management System (LMS) by recommending similar math questions. Deep embeddings for math questions are generated using Meta's Llama-3.2-11B-Vision-Instruct model, and three recommendation methods-cosine similarity, Self-Organizing Maps (SOM), and Gaussian Mixture Models (GMM)-are applied to identify similar questions. User interaction data, including session durations, response times, and correctness, are used to evaluate the methods. Our findings suggest that while cosine similarity produces nearly identical question matches, SOM yields higher user satisfaction whereas GMM generally underperforms, indicating that introducing variety to a certain degree may enhance engagement and thereby potential learning outcomes until variety is no longer balanced reasonably, which our data about the implementations of all three methods demonstrate.