  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.
