Authors:
Nguyen Thai-Nghe
;
Lucas Drumond
;
Tomáš Horváth
;
Alexandros Nanopoulos
and
Lars Schmidt-Thieme
Affiliation:
University of Hildesheim, Germany
Keyword(s):
Recommender systems, Matrix factorization, Tensor factorization, Student performance.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence and Decision Support Systems
;
Computer-Aided Assessment
;
Computer-Supported Education
;
e-Learning
;
Enterprise Information Systems
;
Information Technologies Supporting Learning
;
Intelligent Tutoring Systems
;
Learning/Teaching Methodologies and Assessment
;
Metrics and Performance Measurement
Abstract:
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.