E-Learning Recommendation System and Classification Techniques - A Survey

Abstract

eLearning has brought about significant transformations in the way students receive education where systems recommend essential elements. "E-learning task recommenders" refer to recommendations of learning activities to students based on their completed assignments. Collaboration and content-based filters are two examples of the many techniques and algorithms that may be used to create recommendation systems. The goal of this research project is to present a comprehensive overview of recommender systems, covering challenges and strategies used. For reliable results, this research proposes to combine content-based and collaborative filters. The review examines several approaches to recommendation system creation, with a primary focus on techniques for course recommendation systems. Online course recommendation systems aim to improve learning experiences by helping students locate courses that align with their interests, hone their skills, and expand their knowledge in a targeted and efficient manner. This study gives valuable information on online course recommenders that may tremendously aid in defining future advances for researchers, instructors, and practitioners working in the field of online education.