Prediction of Students' Grade by Combining Educational Knowledge Graph and Collaborative Filtering

Abstract

Traditional collaborative filtering-based grade prediction methods overly rely on students' historical grades and overlook the content correlation between courses, resulting in lower accuracy in predicting student grades. This paper proposes a grade prediction method that combines the educational domain knowledge graph with collaborative filtering, gathering course semantic information and constructing a course knowledge graph as auxiliary information for grade prediction. Through experimentation, it has been demonstrated that the integration of the educational knowledge graph and collaborative filtering in the grade prediction method uncovers more semantic relationships between courses, thereby improving the accuracy of predicting grades for related courses. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics show a decrease when compared to collaborative filtering and K-means algorithms. The method in this paper allows for more personalized learning and recommendation in the knowledge-rich field of education with semantic richness.