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| Modeling Method of Knowledge Relevance Based on Fuzzy Measures |
| ZHANG Suojuan1,2, HUANG Song1, YU Xiaohan1, CHEN Enhong2 |
1. College of Command and Control Engineering, Army Engineering University of PLA, Nanjing 210007;
2. Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027 |
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Abstract The relevance between knowledge in the instructional scenarios draws much attention. The existing research usually focuses on modeling the relationship between two knowledge points. However, the complex relevance in knowledge sets is ignored, which results in the deviation of the research results. Aiming at this problem, the fuzzy measure is introduced to quantify the knowledge set, and then a modeling method of knowledge relevance based on fuzzy measures is proposed. Firstly, three different knowledge relationships are analyzed grounded on the cognitive theory, and the knowledge relevance is modeled with fuzzy measures. Then, the practicability of the modeling method is demonstrated by the practical scenario. Secondly, based on fuzzy measure modeling, the importance and interaction of knowledge are discussed from the perspective of knowledge relevance. Finally, the application of knowledge relevance in cognitive diagnosis is studied. The influence of knowledge relevance on cognitive diagnosis is demonstrated through the experiments on real-world datasets. The results show that the proposed method predicts precisely with better interpretability.
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Received: 22 September 2021
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| Fund:Supported by National Key Research and Development Program of China(No.2018YFB1403400), National Natural Science Foundation of China(No.U20A20229) |
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Corresponding Authors:
HUANG Song, Ph.D., professor. His research interests include software testing and data mining.
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| About author:: ZHANG Suojuan, Ph.D. candidate, asso-ciate professor. Her research interests include educational data mining and cognitive diagnosis.YU Xiaohan, Ph.D., associate professor. His research interests include artificial intelligence.CHEN Enhong, Ph.D., professor. His research interests include machine learning, data mining, education big data analysis and personalized recommendation system. |
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2022, Vol. 35 
