基于特征嵌入的学生知识点熟练度预测

Prediction of Student’s Knowledge Proficiency Based on Feature Embedding

学生知识点熟练度是教师为学生制定学习计划的重要依据.为解决认知诊断中无法概率化学生知识点熟练度的问题,提出了将知识点作为特征嵌入的预测方法.该方法分别对学生和试题建立知识点向量,并且构造卷积神经网络进行监督学习,根据学生的答题情况不断调整他们的知识点熟练度.实验结果与现有的方法进行对比,验证了该方法的准确率的确有所提升.

The proficiency of students’ knowledge points is an important basis for teachers to make learning plans. To tackle the problem that the students’ proficiency for knowledge points cannot be described in a probabilistic way in cognitive diagnosis, this study proposes a prediction method of embedding knowledge points as features. This method establishes knowledge point vectors for students and test questions respectively and constructs a convolutional neural network for supervised learning to adjust students’ proficiency for knowledge points according to their answering records.Compared with existing related methods, the proposed method greatly improves the accuracy.