Knowledge tracing (KT), aiming to mine students' mastery of knowledge by
their exercise records and predict their performance on future test questions,
is a critical task in educational assessment. While researchers achieved
tremendous success with the rapid development of deep learning techniques,
current knowledge tracing tasks fall into the cracks from real-world teaching
scenarios. Relying heavily on extensive student data and solely predicting
numerical performances differs from the settings where teachers assess
students' knowledge state from limited practices and provide explanatory
feedback. To fill this gap, we explore a new task formulation: Explainable
Few-shot Knowledge Tracing. By leveraging the powerful reasoning and generation
abilities of large language models (LLMs), we then propose a cognition-guided
framework that can track the student knowledge from a few student records while
providing natural language explanations. Experimental results from three widely
used datasets show that LLMs can perform comparable or superior to competitive
deep knowledge tracing methods. We also discuss potential directions and call
for future improvements in relevant topics.

基于大型语言模型的认知引导框架可在少量学生记录的情况下追踪学生的知识，并提供自然语言解释。实验结果显示，大型语言模型可与竞争性深度知识追踪方法相当或更优。