Hybrid Course Recommendation System

E-learning has gained enormous momentum and is becoming a prominent way of learning among the student community. A great amount of useful and varied educational resources are available over the internet through Massive Online Open Courses (MOOCs) websites such as Coursera, Udacity, EdX, etc. Though students have many opportunities to explore, it becomes increasingly challenging and time-consuming to search and examine the vast number of courses for suitable content. To overcome this problem, recommendation systems can be used. This paper proposes a course recommendation algorithm based on user’s profile and their similar characteristics to other users. The proposed algorithm combines content- based filtering with collaborative filtering to provide more accurate and targeted results. For subjective testing, a web-based system is developed with 8 courses and 15 users.