Cited by 97Emotion and Mood RecognitionOnline Learning and AnalyticsIntelligent Tutoring Systems and Adaptive Learning

Three-dimensional DenseNet self-attention neural network for automatic detection of student’s engagement

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Mehta, Naval Kishore, Prasad, Shyam Sunder, Saurav, Sumeet, Saini, Ravi, Singh, Sanjay·2022·Applied Intelligence

Abstract

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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  1. [63]Zhang H, Goodfellow I, Metaxas D, Odena A (2019) Self-attention generative adversarial networks. In: International conference on machine learning. PMLR, pp 7354–7363
  2. [65]Drummond C, Holte R C, et al. (2003) C4. 5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling. In: Workshop on learning from imbalanced datasets II, vol 11. Citeseer, pp 1–8

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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
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
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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