Enhancing Student Performance Prediction on Learnersourced Questions with SGNN-LLM Synergy
This work addresses the problem of personalizing learning experiences in scalable education for students and educators, though it appears incremental as it builds on existing graph-based and LLM techniques.
The paper tackled the challenge of predicting student performance on learnersourced questions by introducing a method that combines Signed Graph Neural Networks (SGNNs) and Large Language Model (LLM) embeddings, resulting in improved predictive accuracy and robustness across five real-world datasets.
Learnersourcing offers great potential for scalable education through student content creation. However, predicting student performance on learnersourced questions, which is essential for personalizing the learning experience, is challenging due to the inherent noise in student-generated data. Moreover, while conventional graph-based methods can capture the complex network of student and question interactions, they often fall short under cold start conditions where limited student engagement with questions yields sparse data. To address both challenges, we introduce an innovative strategy that synergizes the potential of integrating Signed Graph Neural Networks (SGNNs) and Large Language Model (LLM) embeddings. Our methodology employs a signed bipartite graph to comprehensively model student answers, complemented by a contrastive learning framework that enhances noise resilience. Furthermore, LLM's contribution lies in generating foundational question embeddings, proving especially advantageous in addressing cold start scenarios characterized by limited graph data. Validation across five real-world datasets sourced from the PeerWise platform underscores our approach's effectiveness. Our method outperforms baselines, showcasing enhanced predictive accuracy and robustness.