Automated Classification of Student’s Emotion Through Facial Expressions Using Transfer Learning


Emotions play a critical role in learning. Having a good understanding of student emotions during class is important for students and teachers to improve their teaching and learning experiences. For instance, analyzing students’ emotions during learning can provide teachers with feedback regarding student engagement, enabling teachers to make pedagogical decisions to enhance student learning. This information may also provide students with valuable feedback for improved emotion regulation in learning contexts. In practice, it is not easy for teachers to monitor all students while teaching. In this paper, we propose an automated framework for emotional classification through students’ facial expression and recognizing academic affective states, including amusement, anger, boredom, confusion, engagement, interest, relief, sadness, and surprise. The methodology includes dataset construction, pre-processing, and deep convolutional neural network (CNN) framework based on VGG-19 (pre-trained and configured) as a feature extractor and multi-layer perceptron (MLP) as a classier. To evaluate the performance, we created a dataset of the aforementioned facial expressions from three publicly available datasets that link academic emotions: DAiSEE, Raf-DB, and EmotioNet, as well as classroom videos from the internet. The configured VGG-19 CNN system yields a mean classification accuracy, sensitivity, and specificity of 82.73% ± 2.26, 82.55% ± 2.14, and 97.67% ± 0.45, respectively when estimated by 5-fold cross validation. The result shows that the proposed framework can effectively classify student emotions in class and may provide a useful tool to assist teachers understand the emotional climate in their class, thus enabling them to make more informed pedagogical decisions to improve student learning experiences.

Author Information
Rajamanickam Yuvaraj, Nanyang Technological University, Singapore
Ratnavel Rajalakshmi, Vellore Institute of Technology, India
Venkata Dhanvanth, Vellore Institute of Technology, India
Jack Fogarty, Nanyang Technological University, Singapore

Paper Information
Conference: ECE2023
Stream: Design

This paper is part of the ECE2023 Conference Proceedings (View)
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To cite this article:
Yuvaraj R., Rajalakshmi R., Dhanvanth V., & Fogarty J. (2023) Automated Classification of Student’s Emotion Through Facial Expressions Using Transfer Learning ISSN: 2188-1162 The European Conference on Education 2023: Official Conference Proceedings
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Posted by James Alexander Gordon