Purpose-Recently,the number of online learners and learning resources has increased dramatically,and the knowledge network generated in the e-learning platform is getting vaster and more complex than ever.Analyzing le...Purpose-Recently,the number of online learners and learning resources has increased dramatically,and the knowledge network generated in the e-learning platform is getting vaster and more complex than ever.Analyzing learners’potential preferences by aggregating high-level semantic information from this network and accurately modeling their cognitive states is crucial for identifying similar learners.Combining similar learners’learning records helps recommend suitable exercises to improve the effectiveness of exercise recommendations.This article tackles the challenging problem of how to aggregate high-level semantic information in a huge graph and accurately model learners’cognitive states.Design/methodology/approach-Firstly,this approach constructs e-learning environments’knowledge graphs by integrating the difficulty of exercises and characteristics of answering behaviors,and the knowledge graph attention network(KGAT)is used to train the graph embedding model of the knowledge graph.Secondly,a score reevaluation method is designed based on the coefficient of completion quality to help accurately model learners’cognitive states.Then,the learners’actual cognitive states,obtained by the cognitive diagnosis model(CDM),are innovatively incorporated into graph matching for acquiring similar subgraphs.Finally,the personalized recommendation results are ranked according to learners’interaction probability on similar exercises.Findings-First,the proposed method has superior exercise recommendation performance.Experiments demonstrate that,compared to the existing approach,the proposed approach has an increase rate of 3.21%,3.32%,3.27%and 0.38%in precision,recall,F1 score and HR@10,respectively,in the large-scale graph data scenario.Second,aggregating high-level semantic information from the knowledge network helps explore learners’potential preferences.Finally,the fine-grained scoring mechanism based on learners’exercise completion quality can better reflect the actual mastery levels of learners,which improves the accuracy of modeling their cognitive states.Originality/value-First,an approach to personalized exercise recommendation is proposed via knowledge enhancement and fuzzy cognitive fusion.The experiments demonstrate the effectiveness and feasibility of this approach in a scenario with large-scale graph data.Second,this approach provides a flexible and adaptable framework.In it,the CDM can be replaced to explore for better accuracy of cognitive evaluation.Third,KGAT is employed to embed the knowledge graph in e-learning environments for aggregating high-level semantic information from the graph.Finally,a score reevaluation method is designed to analyze learners’learning behavior for accurately modeling their cognitive states.展开更多
文摘Purpose-Recently,the number of online learners and learning resources has increased dramatically,and the knowledge network generated in the e-learning platform is getting vaster and more complex than ever.Analyzing learners’potential preferences by aggregating high-level semantic information from this network and accurately modeling their cognitive states is crucial for identifying similar learners.Combining similar learners’learning records helps recommend suitable exercises to improve the effectiveness of exercise recommendations.This article tackles the challenging problem of how to aggregate high-level semantic information in a huge graph and accurately model learners’cognitive states.Design/methodology/approach-Firstly,this approach constructs e-learning environments’knowledge graphs by integrating the difficulty of exercises and characteristics of answering behaviors,and the knowledge graph attention network(KGAT)is used to train the graph embedding model of the knowledge graph.Secondly,a score reevaluation method is designed based on the coefficient of completion quality to help accurately model learners’cognitive states.Then,the learners’actual cognitive states,obtained by the cognitive diagnosis model(CDM),are innovatively incorporated into graph matching for acquiring similar subgraphs.Finally,the personalized recommendation results are ranked according to learners’interaction probability on similar exercises.Findings-First,the proposed method has superior exercise recommendation performance.Experiments demonstrate that,compared to the existing approach,the proposed approach has an increase rate of 3.21%,3.32%,3.27%and 0.38%in precision,recall,F1 score and HR@10,respectively,in the large-scale graph data scenario.Second,aggregating high-level semantic information from the knowledge network helps explore learners’potential preferences.Finally,the fine-grained scoring mechanism based on learners’exercise completion quality can better reflect the actual mastery levels of learners,which improves the accuracy of modeling their cognitive states.Originality/value-First,an approach to personalized exercise recommendation is proposed via knowledge enhancement and fuzzy cognitive fusion.The experiments demonstrate the effectiveness and feasibility of this approach in a scenario with large-scale graph data.Second,this approach provides a flexible and adaptable framework.In it,the CDM can be replaced to explore for better accuracy of cognitive evaluation.Third,KGAT is employed to embed the knowledge graph in e-learning environments for aggregating high-level semantic information from the graph.Finally,a score reevaluation method is designed to analyze learners’learning behavior for accurately modeling their cognitive states.
