With the development of the Internet and intelligent_education systems,the significance of cognitive diagnosis has become increasingly acknowledged.Cognitive diagnosis models(CDMs)aim to characterize learners'cogn...With the development of the Internet and intelligent_education systems,the significance of cognitive diagnosis has become increasingly acknowledged.Cognitive diagnosis models(CDMs)aim to characterize learners'cognitive states based on their responses to a series of exercises.However,conventional CDMs often struggle with less frequently observed learners and items,primarily due to limited prior knowledge.Recent advancements in large language models(LLMs)offer a promising avenue for infusing rich domain information into CDMs.However,integrating LLMs directly into CDMs poses significant challenges.WhileLLMsexcelin semantic comprehension,they are less adept at capturing the finegrained and interactive behaviours central to cognitive diagnosis.Moreover,the inherent difference between LLMs’semantic representations and CDMs'behavioural feature spaces hinders their seamless integration.To address these issues,this research proposes a modelagnostic framework to enhance the knowledge of CDMs through LLMs extensive knowledge.It enhances various CDM architectures by leveraging LLM-derived domain knowledge and the structure of observed learning outcomes taxonomy.It operates in two stages:first,LLM diagnosis,which simultaneously assesses learners via educational techniques to establish a richer and a more comprehensiveknowledge_representation;second,cognitive level alignment,which reconciles the LLM's semantic space with the CDM's behavioural domain through contrastive learning and mask-reconstruction learning.Empirical evaluations on multiple real-world datasets demonstrate that_the proposed framework significantly improvesdiagnosticaccuracyand underscoring the value of integrating LLM-driven semantic knowledge into traditional cognitive diagnosis paradigms.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.62037001 and 62307032)the Zhejiang Province Leading Geese Plan(Grant No.2025C02022).
文摘With the development of the Internet and intelligent_education systems,the significance of cognitive diagnosis has become increasingly acknowledged.Cognitive diagnosis models(CDMs)aim to characterize learners'cognitive states based on their responses to a series of exercises.However,conventional CDMs often struggle with less frequently observed learners and items,primarily due to limited prior knowledge.Recent advancements in large language models(LLMs)offer a promising avenue for infusing rich domain information into CDMs.However,integrating LLMs directly into CDMs poses significant challenges.WhileLLMsexcelin semantic comprehension,they are less adept at capturing the finegrained and interactive behaviours central to cognitive diagnosis.Moreover,the inherent difference between LLMs’semantic representations and CDMs'behavioural feature spaces hinders their seamless integration.To address these issues,this research proposes a modelagnostic framework to enhance the knowledge of CDMs through LLMs extensive knowledge.It enhances various CDM architectures by leveraging LLM-derived domain knowledge and the structure of observed learning outcomes taxonomy.It operates in two stages:first,LLM diagnosis,which simultaneously assesses learners via educational techniques to establish a richer and a more comprehensiveknowledge_representation;second,cognitive level alignment,which reconciles the LLM's semantic space with the CDM's behavioural domain through contrastive learning and mask-reconstruction learning.Empirical evaluations on multiple real-world datasets demonstrate that_the proposed framework significantly improvesdiagnosticaccuracyand underscoring the value of integrating LLM-driven semantic knowledge into traditional cognitive diagnosis paradigms.
