摘要
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 teachers assess students’knowledge state from limited practices and provide explanatory feedback.To fill this gap,this study explores a new task formulation,namely,explainable few-shot KT.By leveraging the powerful reasoning and generation abilities of large language models(LLMs),this study then proposes a cognition-guided framework that can track students’knowledge from a few students’records while providing natural language explanations.Experimental results from three widely used datasets show that LLMs can perform comparable or superior to competitive deep KT methods.Finally,this study discusses potential directions and calls for future improvements.
基金
supported by the National Natural Science Foundation of China(Grant No.62377002)
the SMP-Zhipu.AI Large Model Cross-Disciplinary Fund(Grant No.20240211).
