Building an E-learning Recommender System Using Vector Space Model and Good Learners Average Rating

Abstract

An enormous amount of learning materials in e-learning has led to the difficulty on locating suitable learning materials for a particular learning topic, creating the need for content recommendation tools within learning context. In this paper, we aim to address this need by proposing a novel framework for an e-learning recommender system. Our proposed framework works on the idea of recommending learning materials based on the similarity of content items (using Vector Space Model) and good learnerspsila average rating strategy. This paper presents the overall architecture of the proposed system and its potential implementation via a prototype design.