Comparison of collaborative-filtering techniques for small-scale student performance prediction task

Abstract

Collaborative-filtering (CF) techniques were successfully used for student performance prediction, however the research was provided mainly on large and very sparse matrix representing (student, task, performance score) triples. This work investigates the usability of CF techniques in student performance prediction for small universities or courses with only a few of students. We compared several CF techniques on a real-world dataset collected at our university which is very small and not so sparse. The experiments show that in such cases the predictive accuracy of these models is not so good and we need to utilize more information about students or tasks.