Neural Cognitive Modeling Based on The Importance of Knowledge point for Student Performance Prediction
Yan Cheng, Meng Li, Haomai Chen, Yingying Cai, Huan Sun, Gang Wu, Zhuang Cai, Guanghe Zhang · 2021
At present, cognitive diagnosis has become one of the important research issues of current intelligent education. Existing cognitive diagnosis models generally use a manually designed function to mine the students' practice process. These functions are usually liner and not enough to capture the complex relationship between students and exercises. In this paper, based on the Neural Cognitive Diagnosis (NeuralCD) framework, we propose an importance of knowledge point-based neural cognitive diagnosis model (IK-NeuralCD). It introduces the importance factor of knowledge point and uses the frequency of knowledge points examined to express the importance of knowledge point, which improves the degree of fitting of the complex relationship between students and exercises, thereby improving the diagnosis and prediction effect. Based on comparison between IK-NeuralCD and the existing typical model on the real dataset, this paper proves the effectiveness of the IK-NeuralCD model.