Automatic Detection of Students’ Engagement During Online Learning: A Bagging Ensemble Deep Learning Approach

Abstract

The COVID-19 pandemic has reshaped education and shifted learning from in-person to online. While this shift offers advantages such as liberating the learning process from time and space constraints and enabling education to occur anywhere and anytime, a challenge lies in detecting student engagement during online learning due to limited interaction. Student engagement, defined as the active involvement of students in the educational journey, is a critical factor influencing the overall learning experience. This research addresses this challenge by proposing a model using bagging (bootstrap aggregating) ensemble learning applied to 1-dimensional convolutional neural networks (1D CNN), 1-dimensional residual networks (1D ResNet), and hybrid ensemble deep learning models. Utilizing the DAiSEE dataset, our findings indicate that the bagging ensemble of the 1D CNN model achieves 93.25% accuracy, surpassing the individual model by 3.25%. The deep learning ensemble bagging attains 93.75%, outperforming the unique 1D ResNet model by 3.5%. Additionally, the hybrid ensemble bagging achieves the highest accuracy of 94.25%, a 1% improvement over the 1D CNN model and a 0.5% increase over the 1D ResNet model.

Keywords

• Bagging
• convolutional neural networks
• deep learning
• engagement detection
• ensemble learning
• online learning