关键词: 成绩预测, 矩阵填充, 自编码器

Abstract:

The traditional methods of predicting students’grades often require manual screening of characteristics or a large amount of prior knowledge and expert knowledge, the Graph-AE model based on deep learning is proposed to predict students’performance, which can automatically extract features without manual intervention and does not require a lot of prior knowledge. Comparing the Graph-AE model with 13 classical recommendation algorithms, the experimental results show that the Graph-AE model is more accurate on the students’ performance data set than the traditional solutions and can better characterize the relevance and difference between students and courses.

Key words: grade prediction, matrix completion, auto-encoder