MAML-KT: Addressing Cold Start Problem in Knowledge Tracing for New Students via Few-Shot Model-Agnostic Meta Learning

Researchers introduce MAML-KT, a meta-learning approach that addresses the cold start problem in knowledge tracing systems when predicting performance of new students with limited interaction data. The model uses few-shot learning to rapidly adapt to unseen students, achieving higher early accuracy than existing knowledge tracing models across multiple datasets.