Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (11): 160-170.DOI: 10.3778/j.issn.1002-8331.2010-0276

• Pattern Recognition and Artificial Intelligence • Previous Articles     Next Articles

Student Performance Prediction Based on Reading Cognitive Diagnosis

JIANG Peichao, WANG Chuan, HU Fuzhen, LI Qi, WANG Xiaodong   

  1. 1.College of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan 453007, China
    2.Department of Education, Henan Normal University, Xinxiang, Henan 453007, China
  • Online:2022-06-01 Published:2022-06-01



  1. 1.河南师范大学 计算机与信息工程学院,河南 新乡 453007 
    2.河南师范大学 教育学部,河南 新乡 453007

Abstract: Performance prediction for students(i.e., scores on exercises) is a significant research direction in the domain of online education. However, the prediction accuracy of traditional cognitive diagnosis is insufficient, and collaborative filtering is difficult to guarantee the interpretability of prediction results. Besides, most of the current methods make use of students’response logs on exercises when making predictions. Therefore, they cannot predict the performance of students who have no response logs. Actually, students usually read some auxiliary text learning materials before responding to the exercises. Reading cognitive diagnosis means that the contents of learning materials read by students can frequently reflect students’ knowledge state(i.e., proficiency for knowledge points), which is beneficial to predict their performance. To this end, this paper proposes a knowledge state modeling method based on reading cognitive diagnosis. First, the students’ potential cognitive level for learning materials is quantified by using the contents that students have read learning materials. Second, the difficulty of learning materials is quantified by combining an educational assumption. Third, according to the above two quantitative results, the students’ actual mastery for learning materials is computed through the item response theory. Based on this, the students’ knowledge state is modeled and their performance can be predicted. Consequently, experiments on a real-world dataset prove that the proposed method can guarantee the accuracy and interpretability of the prediction results. Meanwhile, it can also predict the performance of students who have no response logs.

Key words: online education, student performance, learning material, reading cognitive diagnosis, knowledge state

摘要: 面向学生的表现预测(试题得分)是在线教育领域重要的研究课题。但传统认知诊断的预测准确性较低,协同过滤方法难以保证预测结果的可解释性。此外,由于目前方法预测时大多利用了学生的试题作答记录,因而不能预测无作答记录的学生在试题上的表现。学生作答试题之前,通常会阅读一些辅助性文本类学习材料。阅读认知诊断即学生阅读学习材料的内容往往可以反映出学生的知识状态(知识点掌握情况),从而有助于预测学生表现。为此,提出一种基于阅读认知诊断的知识状态建模方法。利用学生阅读学习材料的内容,量化其对学习材料的潜在认知程度。结合教育学假设,量化学习材料的难度。利用上述两个量化结果,根据项目反应理论,计算出学生对学习材料的实际掌握程度,据此建模学生的知识状态并预测其在试题上的表现。在实际数据集上进行实验,实验结果表明所提方法可以保证预测结果的准确性与可解释性,也可以预测出无作答记录的学生表现。

关键词: 在线教育, 学生表现, 学习材料, 阅读认知诊断, 知识状态