Retrieval-Augmented Contrastive Learning for Knowledge Tracing

Knowledge Tracing (KT) models aim to predict student performance from interaction histories in order to support personalised learning. However, many learners generate only limited interaction data, making reliable knowledge-state estimation difficult. Recent contrastive KT methods attempt to address this data sparsity through self-supervised representation learning from augmented versions of individual learner sequences, operating within an intra-learner paradigm, where contrastive signals are derived solely from variations of a single learner's trajectory. We advance prior work by proposing RACL (Retrieval-Augmented Contrastive Learning), a knowledge tracing framework that introduces an inter-learner contrastive paradigm, leveraging the observation that students with similar skill profiles often exhibit comparable learning trajectories. Cross-learner structure therefore provides naturally occurring positive and negative examples that are more pedagogically meaningful than synthetic augmentations. Experiments on four benchmarks demonstrate that RACL achieves +1.2% average AUC improvement over state-of-the-art methods, with 97% performance retention at 20% training data, indicating improved robustness under sparse-learning conditions.