ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (8): 1729-1740.doi: 10.7544/issn1000-1239.2020.20200181

Special Issue: 2020数据挖掘与知识发现专题

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Student Performance Prediction Model Based on Two-Way Attention Mechanism

Li Mengying1, Wang Xiaodong1, Ruan Shulan2, Zhang Kun2, Liu Qi2   

  1. 1(College of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan 453000);2(College of Computer Science and Technology, University of Science and Technology of China, Hefei 230027)
  • Online:2020-08-01

Abstract: The prediction and analysis of student performance aims to achieve personalized guidance to students, improve students’ performance and teachers’ teaching effectiveness. Student performance is affected by many factors such as family environment, learning conditions and personal performance. The traditional performance prediction methods either treat all the factors equally, or treat all students equally, which cannot achieve personalized analysis and guidance for students. Therefore, we propose a two-way attention (TWA) based students’ performance prediction model, which can assign different weights to different influence factors, and pay more attention to the important ones. Besides, we also take the individual features of students into account. Firstly, we calculate the attention scores of the attributes on the first-stage performance and the second-stage performance. Then we consider a variety of feature fusion approaches. Finally, we made better predictions of student performance based on the integrated features. We conduct extensive experiments on two public education datasets, and visualize the prediction results. The result shows that the proposed model can predict student performance accurately and have good interpretability.

Key words: performance prediction, attention mechanism, attribute characteristics, feature fusion, personalized analysis

CLC Number: