Factorization Techniques for Predicting Student Performance

Thai-Nghe, Nguyen; Drumond, Lucas; Horváth, Tomáš; Krohn-Grimberghe, Artus; Nanopoulos, Alexandros; Schmidt-Thieme, Lars

Abstract

Recommender systems are widely used in many areas, especially in e-commerce. Recently, they are also applied in e-learning for recommending learning objects (e.g. papers) to students. This chapter introduces state-of-the-art recommender system techniques which can be used not only for recommending objects like tasks/exercises to the students but also for predicting student performance. We formulate the problem of predicting student performance as a recommender system problem and present matrix factorization methods, which are currently known as the most effective recommendation approaches, to implicitly take into account the prevailing latent factors (e.g. “slip” and “guess”) for predicting student performance. As a learner’s knowledge improves over time, too, we propose tensor factorization methods to take the temporal effect into account. Finally, some experimental results and discussions are provided to validate the proposed approach.

Citation

Thai-Nghe, N., Drumond, L., Horváth, T., Krohn-Grimberghe, A., Nanopoulos, A., & Schmidt-Thieme, L. (2012). Factorization Techniques for Predicting Student Performance. In O. C. Santos, & J. G. Boticario (Eds.), Educational Recommender Systems and Technologies: Practices and Challenges (129-153). IGI Global. https://doi.org/10.4018/978-1-61350-489-5.ch006

• Publication Date: 2012
• Deposit Date: Mar 27, 2024
• Publisher: IGI Global
• Pages: 129-153
• Book Title: Educational Recommender Systems and Technologies: Practices and Challenges
• Chapter Number: 6
• ISBN: 9781613504895
• DOI: https://doi.org/10.4018/978-1-61350-489-5.ch006
• Public URL: http://researchrepository.napier.ac.uk/Output/3577358
• Related Public URLs: https://www.ismll.uni-hildesheim.de/pub/pdfs/Nguyen_et_al_ERSAT_2011.pdf