The Effect of Contextual Information on the Personalization Features of Learning in the Online Learning Recommendation System

Online learning, especially in higher education, is getting more attention in many countries in the past ten years as it provides several advantages to the learners including time and space learning flexibility. Student engagement in online learning has an important factor for the student to complete their study program and achieve their planned learning objective. Unlike in classroom learning, online learning does not provide resources such as instructor to maintain student engagement but Learning Management System (LMS). One approach to increase student engagement is by personalizing learning materials managed automatically by LMS. This paper presents experimental results on using context-sensitive recommender systems embedded in LMS to increase student engagement in online learning by personalizing recommended learning materials to the targeted students. The proposed LMS which adapts to student learning progress, recommendations are personalized by using student contextual information to make the generated recommendation fits the student learning progress. The results of this study indicate that contextual information as an additional feature of the student profile increases the correlation between the actual grade and the prediction grade using user-collaborative filtering.