Publication details

Student Performance Prediction Using Collaborative Filtering Methods



Year of publication 2015
Type Article in Proceedings
Conference 17th International Conference on Artificial Inteligence in Education - AIED 2015
MU Faculty or unit

Faculty of Informatics

Field Informatics
Keywords Student Performance; Prediction; Collaborative Filtering Methods; Recommender System
Description This paper shows how to utilize collaborative filtering methods for student performance prediction. These methods are often used in recommender systems. The basic idea of such systems is to utilize the similarity of users based on their ratings of the items in the system. We have decided to employ these techniques in the educational environment to predict student performance. We calculate the similarity of students utilizing their study results, represented by the grades of their previously passed courses. As a real-world example we show results of the performance prediction of students who attended courses at Masaryk University. We describe the data, processing phase, evaluation, and finally the results proving the success of this approach.

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