IEEE Access (Jan 2019)
On Recommendation of Learning Objects Using Felder-Silverman Learning Style Model
Abstract
The e-learning recommender system in learning institutions is increasingly becoming the preferred mode of delivery, as it enables learning anytime, anywhere. However, delivering personalised course learning objects based on student's preferences is still a challenge. Current mainstream recommendation algorithms, such as the Collaborative Filtering (CF) and Content-Based Filtering (CBF), deal with only two types of entities, namely users and items with their ratings. However, these methods do not pay attention to student's preferences, such as learning styles, which are especially important for the accuracy of course learning objects prediction or recommendation. Moreover, several recommendation techniques experience cold-start and rating sparsity problems. To address the challenge of improving the quality of recommender systems, in this paper a novel recommender algorithm for machine learning is proposed, which combines students actual rating with their learning styles to recommend personalised course learning objects (LOs). Various recommendation techniques are considered in an experimental study investigating the best technique to use in predicting student ratings for e-learning recommender systems. We use the Felder-Silverman Learning Styles Model (FSLSM) to represent both the student learning styles and the learning object profiles. The predicted ratings are compared with the actual student ratings to determine the accuracy of the recommendation techniques, using the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics. This approach has been experimented on 80 students for an online course created in the MOODLE Learning Management System. The results of the experiment show that the best recommendation technique is our proposed hybrid recommendation algorithm that combines the collaborative filtering and the content-based filtering techniques to enhance the accuracy of the predictions, and solves the cold-start and the rating sparsity problems using the FSLSM representations of the student learning styles and the learning object profiles.
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