Digital Computing-Based Course Recommendation Algorithm for Distance Education Platforms

Abstract

A course recommendation algorithm utilizes data about a user's preferences, past behaviour, and possibly other factors like demographics or interests to suggest relevant courses. It employs techniques such as collaborative filtering, content-based filtering, or hybrid approaches to analyse similarities between users or courses and make personalized recommendations. By continuously refining its suggestions based on user feedback and interactions, the algorithm aims to enhance the user's learning experience by presenting courses that align with their interests and goals. This paper explores the integration of course design principles with recommendation systems to enhance personalized learning experiences in distance education platforms. The course design is performed with the integration of collaborative filtering with the edge computing model for the estimation of features in distance education. Collaborative filtering is applied in the education platform through the estimation of features and edge computing is implemented for the processing. With the increasing popularity of online learning, there is a growing need to tailor educational content to meet the diverse needs, preferences, and skill levels of individual learners. Course design plays a crucial role in shaping the structure and delivery of educational materials, while recommendation systems leverage user data to provide personalized course suggestions. By integrating these two components, distance education platforms can create tailored learning pathways that optimize user engagement, retention, and learning outcomes. The analysis is further enriched by showcasing the course recommendations for individual users, highlighting how recommendation systems leverage course design aspects to deliver personalized learning experiences.