Using factorization machines for student modeling

Predicting student performance (PSP), one of the task in Student Modeling, has been taken into account by educational data mining community recently. Previous works show that good results can be achieved by casting the PSP to rating prediction task in recommender systems, where students, tasks and performance scores are mapped to users, items and ratings respectively, and thus, matrix factorization - one of the most prominent approaches for rating prediction task - is an appropriate choice. In this work, we propose using Factorization Machines which combine the advantages of Support Vector Machines with factorization models for the problem of PSP. Experiments on two large data sets show that this approach can improve the prediction results over the standard matrix factorization.