Explainable Few-Shot Knowledge Tracing

This paper proposes explainable few-shot knowledge tracing using large language models (LLMs) to predict student performance on future exercises from limited practice records while providing natural language explanations, addressing limitations of traditional deep learning knowledge tracing methods that require extensive data and lack interpretability.

Knowledge tracing (KT), aiming at mining students’ mastery of knowledge by their exercise records and predicting their performance on future test questions, is a critical task in educational assessment. While researchers achieve tremendous success with the rapid development of deep learning techniques, current KT tasks fall into the cracks from real-world teaching scenarios. Relying on extensive student data heavily and predicting numerical performances solely differ from the settings where teac

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