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

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. Furthermore, the existing recommendation techniques are based on either explicit or implicit feedback and fail to capture the changes in learners' preferences while integrating implicit or explicit feedback. To this end, this paper proposes a new model for personalized learning and PCR that is enabled by a smart E-Learning (EL) platform. The model aims to gather data on students' academic performance, interests, and learning preferences and utilize this data to recommend the courses that will be most beneficial to each student. The proposed approach makes suggestions based on the learner's interactions with the system and the cosine similarity in related contents by combining explicit (user ratings) and implicit (views and behavior) methodologies. The suggested method makes use of ML algorithms and an EL Recommender System (RecSys) based on Collaborative Filtering (CF).This includes Random Forest Regressor (RFR), Decision Tree Regressor (DTR), K-Nearest Neighbors (KNN), Singular Value Decomposition (SVD), eXtreme Gradient Boosting Regressor (XGBR), and Linear Regression (LR). The proposed solution is benchmarked against existing approaches on both predictive accuracy and running time. Experimental results are conducted based on two benchmark datasets (Coursera and Udemy). The proposed model outperforms existing top-K recommendations techniques in terms of accuracy metrics such as precision@k, Mean Average Precision (MAP)@k, recall@k, Normalized Discounted Cumulative Gain (NDCG)@k, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) for PCR. From the experiments, it can be shown that SVD can perform well in terms of higher accuracy and MAP and NDCG and lower MAE, RMSE, and MSE values when contrasted to other proposed algorithms because it is better suited to capture complex student-course interactions. The proposed solutions are promising on two different datasets and can be applied to various RecSys domains.