Three-dimensional DenseNet self-attention neural network for automatic detection of student’s engagement
Open accessAbstract
Today, due to the widespread outbreak of the deadly coronavirus, popularly known as COVID-19, the traditional classroom education has been shifted to computer-based learning. Students of various cognitive and psychological abilities participate in the learning process. However, most students are hesitant to provide regular and honest feedback on the comprehensiveness of the course, making it difficult for the instructor to ensure that all students are grasping the information at the same rate. The students' understanding of the course and their emotional engagement, as indicated via facial expressions, are intertwined. This paper attempts to present a three-dimensional DenseNet self-attention neural network (DenseAttNet) used to identify and evaluate student participation in modern and traditional educational programs. With the Dataset for Affective States in E-Environments (DAiSEE), the proposed DenseAttNet model outperformed all other existing methods, achieving baseline accuracy of 63.59% for engagement classification and 54.27% for boredom classification, respectively. Besides, DenseAttNet trained on all four multi-labels, namely boredom, engagement, confusion, and frustration has registered an accuracy of 81.17%, 94.85%, 90.96%, and 95.85%, respectively. In addition, we performed a regression experiment on DAiSEE and obtained the lowest Mean Square Error (MSE) value of 0.0347. Finally, the proposed approach achieves a competitive MSE of 0.0877 when validated on the Emotion Recognition in the Wild Engagement Prediction (EmotiW-EP) dataset.
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Adding an attention module to a model to emphasize more important regions of visual information leads to improved performance.
“We added the attention module to assist the model to emphasize on more important regions of the visual information, leading to improved performance [70].”
From Attention-based Multimodal learning framework for Generalized Audio-Visual Deepfake Detection (2023) · cites this paper as [70]
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- Authors
- Mehta, N. K., Prasad, S. S., Saurav, S., Saini, R., & Singh, S.
- Affiliations
- Central Electronics Engineering Research Institute (IN)
- Corresponding author
- Prasad, Shyam Sunder
- Venue
- Applied Intelligence
- Published
- 2022-03-18
- Volume
- 52
- Issue
- 12
- Pages
- 13803-13823
- ISSN
- 0924-669X (ISSN-L)
- 1573-7497
- PMC
- PMC8932470
- OpenAlex
- W4220849944
- Cited by
- 97
- References
- 60
- Citations by year
- 2026: 22 · 2025: 18 · 2024: 28 · 2023: 24 · 2022: 5
- FWCI
- 17.25
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Applied Intelligence (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Central Electronics Engineering Research Institute
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- student engagement detection, facial expression analysis, self-attention neural network, DenseNet, 3D convolutional neural networks, affective computing, boredom detection, emotion recognition, online learning, DAiSEE dataset
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