ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2020, Vol. 57 ›› Issue (8): 1729-1740.doi: 10.7544/issn1000-1239.2020.20200181

所属专题: 2020数据挖掘与知识发现专题

• 人工智能 • 上一篇    下一篇

基于双路注意力机制的学生成绩预测模型

李梦莹1,王晓东1,阮书岚2,张琨2,刘淇2   

  1. 1(河南师范大学计算机与信息工程学院 河南新乡 453000);2(中国科学技术大学计算机科学与技术学院 合肥 230027) (lmengying@yeah.net)
  • 出版日期: 2020-08-01

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

摘要: 学生成绩的预测与分析旨在实现对学生的个性化指导,提升学生成绩及教师的教学成果.学生成绩受家庭环境、学习条件以及个人表现等多种因素的影响.传统的成绩预测方法往往忽视了不同因素对同一学生成绩的影响程度不同,而且不同学生受同一因素的影响程度也不同,所构建的模型无法实现对学生的个性化分析与指导.因此提出一种基于双路注意力机制的学生成绩预测模型(two-way attention, TWA),该方法不仅有区别地对待了这些因素对成绩的影响程度,而且考虑到了学生的个体差异性.该方法通过两次注意力计算分别得到各属性特征在第1阶段成绩和第2阶段成绩上的注意力得分,并考虑了多种特征融合方式,最后基于融合后的特征对期末成绩进行更好地预测.分别在2个公开数据集上对模型进行了验证,并根据各属性特征在期末成绩上的概率分布对预测结果进行可视化分析.结果显示,所构建模型能够更准确地预测出学生成绩,并且具有良好的可解释性.

关键词: 成绩预测, 注意力机制, 属性特征, 特征融合, 个性化分析

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

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