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.