Combination of hybrid filtering and learning style for learning material recommendation

Abstract

Nowadays, learning styles are increasingly incorporated into adaptive learning systems. Previous studies have proved that learning styles can make learning navigation or presentation adaptive to learners' needs. A challenge in adaptive learning is that to present learning materials relevant to learners based on their learning styles, considering there are a huge number of open accessed learning materials in the web. This paper discusses our research on investigating the effect of learning styles in recommending learning materials. Hybrid filtering, which combines collaborative filtering and content-based filtering, is used in recommender systems by taking into account individual competency learners and the similarity with other learners who have learned the learning materials previously. Commonly, the similarity is taken from correlation among learners, such as rating they have given to learning materials. In our work, we combine rating and learning styles similarities to recommend learning materials. We use Felder-Silverman Learning Styles Model (FSLSM). The experiments conducted is quantitative, in which 44 undergraduate students who have taken Algorithm and Programming Basic course are involved. We make a comparison between MAE scores resulted from recommender when it applies collaborative filtering, learning style similarity filtering, or combined collaborative and learning style similarity filtering. The experiment results indicate the prediction score using rating similarity is the best among the three methods.