With the rapid development of online education,cognitive diagnosis has become a key task in intelligent education,particularly for student ability assessments and resource recommendations.However,existing cognitive di...With the rapid development of online education,cognitive diagnosis has become a key task in intelligent education,particularly for student ability assessments and resource recommendations.However,existing cognitive diagnosis models face the diagnostic system cold-start problem,whereby there are no response logs in new domains,making accurate student diagnosis challenging.This research defines this task as zero-shot cross-domain cognitive diagnosis(ZCCD),which aims to diagnose students'cognitive abilities in the target domain using only the response logs from the source domain without prior interaction data.To address this,a novel paradigm,large language model(LLM)-guidedcognitive state transfer(LCST)is proposed,which leverages the powerful capabilities of LLMs to bridge the gap between the source and target domains.By modelling cognitive states as natural language tasks,LLMs act as intermediaries to transfer students'cognitive states across domains.The research uses advanced LLMs to analyze the relationships between knowledge concepts and diagnose students’mastery of the target domain.The experimental results on realworld datasets shows that the LCST significantly improves cognitive diagnostic performance,which highlights the potential of LLMs as educational experts in this context.This approach provides a promising direction for solving the ZCCD challenge and advancing the application of LLMs in intelligent education.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.62107001,U21A20512,62302010,and 62202006)the Chinese Postdoctoral Science Foundation(Grant No.2023M740015)+1 种基金the Postdoctoral Fellowship Program(Grade B)of the China Postdoctoral Science Foundation(Grant No.GZB20240002)the Anhui Province Key Laboratory of Intelligent Computing and Applications,China(Grant No.AFZNJS2024KF01).
文摘With the rapid development of online education,cognitive diagnosis has become a key task in intelligent education,particularly for student ability assessments and resource recommendations.However,existing cognitive diagnosis models face the diagnostic system cold-start problem,whereby there are no response logs in new domains,making accurate student diagnosis challenging.This research defines this task as zero-shot cross-domain cognitive diagnosis(ZCCD),which aims to diagnose students'cognitive abilities in the target domain using only the response logs from the source domain without prior interaction data.To address this,a novel paradigm,large language model(LLM)-guidedcognitive state transfer(LCST)is proposed,which leverages the powerful capabilities of LLMs to bridge the gap between the source and target domains.By modelling cognitive states as natural language tasks,LLMs act as intermediaries to transfer students'cognitive states across domains.The research uses advanced LLMs to analyze the relationships between knowledge concepts and diagnose students’mastery of the target domain.The experimental results on realworld datasets shows that the LCST significantly improves cognitive diagnostic performance,which highlights the potential of LLMs as educational experts in this context.This approach provides a promising direction for solving the ZCCD challenge and advancing the application of LLMs in intelligent education.
