Recommender system for predicting student performance

作     者：Nguyen Thai-Nghe Lucas Drumond Artus Krohn-Grimberghe Lars Schmidt-Thieme 

作者机构：Information Systems and Machine Learning Lab University of Hildesheim 31141 Hildesheim Germany 

出 版 物：《Procedia Computer Science》 

年 卷 期：2010年第1卷第2期

页      面：2811-2819页

主　　题：Recommender systems Matrix factorization Educational data mining Student performance prediction 

摘      要：Recommender systems are widely used in many areas, especially in e-commerce. Recently, they are also applied in e-learning tasks such as recommending resources (e.g. papers, books,..) to the learners (students). In this work, we propose a novel approach which uses recommender system techniques for educational data mining, especially for predicting student performance. To validate this approach, we compare recommender system techniques with traditional regression methods such as logistic/linear regression by using educational data for intelligent tutoring systems. Experimental results show that the proposed approach can improve prediction results.