Adaptive Machine Learning-Based Personalized Sustainability Learning for Improving Student Understanding and Behavioral Change

Sustainability education establishes a better understanding for students who study sustainability, yet fails to create observable changes in student conduct. This study presents an adaptive machine learning-based educational framework that predicts student sustainability topic comprehension and provides customized learning resources to enhance academic performance and environmental sustainability practices. A synthetic dataset of 9600 records and 21 attributes was generated to simulate student interaction with eight sustainability topics, including climate change, carbon footprint, recycling, water conservation, renewable energy, sustainable transport, food waste, and green buildings. The dataset contained student demographic information, together with their academic performance indicators, their participation metrics, their quiz results, and their conduct assessment scores, which were collected before and after their educational process. The Random Forest classifier was developed to forecast three different levels of comprehension, which included low comprehension, medium comprehension, and high comprehension. The model achieved an accuracy of 0.999, precision of 0.999, recall of