Student Modeling is an important part of an Intel- ligent Tutoring System. The student model tracks information of individual student (e.g., time spent on problems, hints requested, correct answers, etc). One of the important tasks in student modeling is predicting student performance, where the system can provide the students early feedbacks to help them improving their study results.

In this work, we propose using multi-relational factorization approach, which has been successfully applied in recommender systems area, for student modeling in the Intelligent Tutoring Systems. Experiments on large real world data sets show that the proposed approach can improve the prediction results and could be used for student modeling.