Predicting Student Adaptability to Online Education Using Machine Learning

In the wake of global shifts towards digital platforms, online education has become a cornerstone of modern learning environments. Understanding student adaptability in these settings is crucial for developing effective educational strategies and ensuring successful learning outcomes. While the transition to online education offers numerous benefits, it also poses significant challenges, particularly in terms of student engagement and adaptability. Identifying factors that influence adaptability can help educators tailor interventions to assist students who may struggle with online learning modalities. This study utilized a dataset from Kaggle, consisting of 1,205 students with features encompassing demographic information, technological access, and personal educational environments. A Random Forest classifier was employed within a One-vs-Rest strategy to predict three levels of student adaptability to online education. The model's performance was evaluated using accuracy, precision, recall, and F1-score metrics. The Random Forest model achieved an accuracy of 88.3%. It showed high precision and recall for the ‘High’ and ‘Moderate’ adaptability classes but lower performance in predicting ‘Low’ adaptability. The analysis also revealed that class duration, financial condition, and age were among the most significant predictors of adaptability. The findings underscore the potential of machine learning in identifying key factors affecting student adaptability, which can inform the design of personalized learning experiences and interventions in online education. These insights are pivotal for educational institutions aiming to enhance student engagement and reduce dropout rates in digital learning environments.