Intelligent course recommendation approach based on modified felder-silverman learning style model

The popularity of E-learning has encouraged learners to benefit from Massive Open Online Courses (MOOC) platforms however information overload is a common challenge in order to select the appropriate courses. Recommendation systems aid learners to select relevant courses and content based on their preferences, interest and learning goals from MOOC. Various factors are considered in course recommendation systems however little research has been done in recommending courses based on learning styles despite its significance laid down by various studies. Most studies have focused on content filtering matching with learners learning style instead from a course. This research study is oriented towards learning style-based course categorization using course content delivery information data from a MOOC platform. The Felder-Silverman learning style model (FSLSM) has been used to develop a course learning style support identification model. The effectiveness of the model developed is analyzed through an experimental study performed on real-time data due to absence of a standard data set for course contents and learning style relationships. The model threshold values are finetuned through the results obtained from the experimental. The application of the course learning style identification model is demonstrated by conducting test case studies consisting of users with different learning styles and recommending courses from a MOOC platform with matching learning style.