Developing a Personalized E-Learning and MOOC Recommender System in IoT-Enabled Smart Education

This paper proposes a personalized e-learning recommender system using machine learning and collaborative filtering techniques to suggest MOOC courses based on student performance, interests, and learning preferences, evaluated on Coursera and Udemy datasets. The system combines explicit ratings and implicit behavioral data to recommend courses in an IoT-enabled smart education environment.

Smart strategies and intelligent technologies are enabling the designing of a smart learning environment that successfully supports the development of personalized learning and adaptive learning. This trend towards integration is in line with the growing prevalence of Internet of Things (IoT)-enabled smart education systems, which can leverage Machine Learning (ML) techniques to provide Personalized Course Recommendations (PCR) to students. Current recommendation techniques rely on either explic

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