Nguyen Thai-Nghe, Lucas Drumond, Tomáš Horváth, Alexandros Nanopoulos, Lars Schmidt-Thieme


Recommender systems are widely used in many areas, especially in e-commerce. Recently, they are also applied in technology enhanced learning such as recommending resources (e.g. papers, books,...) to the learners (students). In this study, we propose using state-of-the-art recommender system techniques for predicting student performance. We introduce and formulate the problem of predicting student performance in the context of recommender systems. We present the matrix factorization method, known as most effective recommendation approaches, to implicitly take into account the latent factors, e.g. “slip” and “guess”, in predicting student performance. Moreover, the knowledge of the learners has been improved over the time, thus, we propose tensor factorization methods to take the temporal effect into account. Experimental results show that the proposed approaches can improve the prediction results.


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Paper Citation

in Harvard Style

Thai-Nghe N., Drumond L., Horváth T., Nanopoulos A. and Schmidt-Thieme L. (2011). MATRIX AND TENSOR FACTORIZATION FOR PREDICTING STUDENT PERFORMANCE . In Proceedings of the 3rd International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-8425-49-2, pages 69-78. DOI: 10.5220/0003328700690078

in Bibtex Style

author={Nguyen Thai-Nghe and Lucas Drumond and Tomáš Horváth and Alexandros Nanopoulos and Lars Schmidt-Thieme},
booktitle={Proceedings of the 3rd International Conference on Computer Supported Education - Volume 1: CSEDU,},

in EndNote Style

JO - Proceedings of the 3rd International Conference on Computer Supported Education - Volume 1: CSEDU,
SN - 978-989-8425-49-2
AU - Thai-Nghe N.
AU - Drumond L.
AU - Horváth T.
AU - Nanopoulos A.
AU - Schmidt-Thieme L.
PY - 2011
SP - 69
EP - 78
DO - 10.5220/0003328700690078