Enhancing Human–Computer Interaction in Online Education: A Machine Learning Approach to Predicting Student Emotion and Satisfaction

Abstract

Maintaining student engagement is pivotal in the rapidly evolving landscape of online education. The factors that affect and predict students’ satisfaction during online learning are poorly understood. Our study addresses this issue by employing machine learning (ML) models to predict student emotions and satisfaction. ML focuses on small and structured data that do not require the complexity of deep neural networks. We created an ML classification model trained with data from an online survey of seven constructs. We narrowed down the critical features from 55 to 7 and 36 for satisfaction and emotion prediction, respectively, using the backward feature selection (BFS) method. The artificial neural network (ANN) and the random forest (RF) model outperformed other classifiers, showing 81% and 65.3% accuracy for satisfaction and emotion prediction, respectively. Our findings suggest that by predicting and responding to student emotions and satisfaction, we can optimize the online learning experience, providing personalized educational trajectories that align with each student’s unique needs.