Comparative analysis of matrix factorization and graph convolutional networks in student

Abstract

Abstract. For the educational worker, predicting students grades and having a deep understanding of students levels is quite important to improve their teaching methods. Fortunately, there have been several research to predict students grades in applying different models, such as Matrix Factorization (MF) and Graph Convolutional Networks (GCNs). This essay is talking about comparing two different models, MF and GCNs, which are going to exhibit the difference between them. By comparing their performance in predictive accuracy, interpretability, and computational efficiency, people can identify their strengths and areas for improvement. In this essay, the advantages and disadvantages of the two models will be listed and their performance will be compared. Therefore, in this context, this essay will introduce two models first, then show their performance in different experiences from past research and compare their results. As a result, MF shows a better performance in handling large-scale sparse datasets and providing meaningful interpretations, whereas GCNs are good at capturing complex dependencies and integrating multiple data sources.