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
Abstract Cognitive diagnosis is an intelligent assessment technique of mining learners' cognitive state based on learning data. Concepts in learning tasks are regarded as equally important by most cognitive diagnosis deep model. Without the consideration of the interaction between concepts, diagnosis accuracy is affected and interpretability is insufficient. To solve the problems, a concept interaction-based cognitive diagnosis deep model is proposed to realize the unified representation of students' cognitive state and concept weights. In the meanwhile, an algorithm of ideal response calculation based on the Choquet integral is implemented. Finally, a deep neural network based on fuzzy measures is proposed to predict learners' response performance. Experiments show that the proposed model holds advantages in prediction results and the explanation at the concept interaction level provided for prediction results.
Fund:National Natural Science Foundation of China(No.62207031,U20A20229)
Corresponding Authors:
HUANG Song, Ph.D., professor. His research interests include software testing and data mining.
About author:: ZHANG Suojuan, Ph.D., associate 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.SHEN Shuanghong, Ph.D. candidate. His research interests include data mining, student modeling and knowledge tracing.ZHENG Yu, master, teaching assistant. Her research interests include information retrieval and natural language processing.
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2023, Vol. 36 
