Enabling Sustainable Learning: A Machine Learning Approach for an Eco-friendly Multi-factor Adaptive E-Learning System

Abstract

Adaptive learning seeks to create personalized learning experiences by considering various cognitive and affective factors. However, conventional adaptive models often fall short in meeting diverse learner needs, relying heavily on single factors like learning style. In response, we present a comprehensive framework that integrates an AI-based adaptive learning model, which not only accounts for multiple factors such as prior performance, leisure interests, and learning style but also aligns with principles of green smart education. Leveraging the k-means clustering algorithm, our approach brings together learners with similar leisure interests. Predicting student performance involves a Gradient Boosting Regressor, with demographic data and past performance contributing to a performance metric. Additionally, our system incorporates sustainability practices, optimizing resource usage in computation and data storage to promote eco-friendliness in education. Artificial neural networks predict individual learning styles, and a decision tree algorithm personalizes educational content delivery to align with preferences. Our objective is twofold: to enhance the overall performance of the proposed model and to champion sustainability in education, fostering a greener and more adaptive learning ecosystem.