Predicting Student Engagement Using Sequential Ensemble Model

Predicting student engagement can provide timely feedback and help teachers make adjustments to their practices to meet student needs and improve their learning experience. This article proposes a four-step approach using a sequential ensemble model for engagement prediction, discusses the contribution of different features to the model and the influence of video segmentation in the prediction, reports on two in-the-wild datasets-The Emotion Recognition in the Wild Engagement Prediction (EmotiW-EP) dataset published in 2018 as part of a student engagement task and the Dataset for Affective States in E-Environments (DAiSEE), a general purpose dataset also used in the educational context but not limited to it, and, finally, presents a comprehensive and thorough critical analysis, highlighting crucial factors to consider when using AI/computer vision models in educational datasets for learning purposes. Experiments show that our proposed approach outperforms state-of-the-art approaches by obtaining a mean square error of 0.0386 on the DAiSEE dataset and 0.0610 on the EmotiW-EP dataset. We conclude this article with a critical analysis of the reliability of such predictions in learning environments and propose future directions for the effective use of AI/computer vision models in education.