Abstract
The engagement of a student is a vital part of an online learning environment, however, because of the spatial separation between instructors and students in an online environment, it is often very difficult to measure the level of engagement in online learning. Higher levels of engagement are often related to a better sense of well-being, and are more related to emotions like happiness. In this paper, two state-of-the-art models that can help measure engagement and emotions are tested, in this case, we investigated suitability of two popular models: XCeption Architecture proposed for the DAiSEE dataset, and the DeepFace Emotion Recognition model. Both models are then applied in a real-life test, using real students in an online class, we measured their emotions using the models and then compared the results to obtain the effectiveness of correctly measuring the engagement and emotions of the students. We interpret the findings from our experimental results based on psychology concepts in the field of engagement.
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Andrés, N., Gonzalez, O., Freddy, T. (2023). Use of the Student Engagement as a Strategy to Optimize Online Education, Applying a Supervised Machine Learning Model Using Facial Recognition. In: Botto-Tobar, M., Zambrano Vizuete, M., Montes León, S., Torres-Carrión, P., Durakovic, B. (eds) Applied Technologies. ICAT 2022. Communications in Computer and Information Science, vol 1755. Springer, Cham. https://doi.org/10.1007/978-3-031-24985-3_21
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