Recommender System for e-Learning based on Personal Learning Style

Online shopping has become an important part of lifestyle nowadays. Despite their many practical advantages, the users of online shopping systems can be overwhelmed with the abundant information about the goods they want to buy. While some users start their search with a preference for certain items or manufacturers, others may find it difficult to narrow down the range of options being offered. The recommender system can assist the users to filter the information and show the most relevant items to the users. Despite being very popular in ecommerce area, research on recommender systems for education is still underexplored. Similar to the users of ecommerce system, some students may also feel overwhelmed by the available choices of material contents offered by the elearning system in which, it does not always suit to their learning style. This is important as some experts in educational psychology suggest that students need to learn by following their personal learning style. We propose an implementation design of e-learning recommender system based on a logic approach, APARELL (Active Pairwise Relation Learner), which has been implemented for used car sales domain. There is an opportunity to apply the same procedure for e-learning system to help the student to choose the best material according to their preferences. We also propose an ontology of material content based on the different learning styles. In this paper, we show that there is a big potential to implement a personalised recommender system in e-learning based on the students learning style.