What question did this study set out to answer?

To evaluate the feasibility and accuracy of using facial expression analysis as an affective sensing system to detect student emotional states in online learning environments.

August 19, 2026International Journal of Intelligent Computing and Cybernetics

Towards emotion-aware online learning through facial expression analysis

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Authors

ÖŞÖznur ŞengelIstanbul Kültür University FAFatma Patlar AkbulutIstanbul Kültür University CCCagatay CatalQatar University

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Implication

Experimental study demonstrates accurate classification of student emotional states using lightweight convolutional neural networks, suggesting affective sensing can enhance digital learning.

Key Points

To evaluate the feasibility and accuracy of using facial expression analysis as an affective sensing system to detect student emotional states in online learning environments.
Collected facial video data from N=26 students during online learning sessions.
Mapped self-reported emotion labels into three affective categories: positive, neutral, and negative.
Transformed video streams into standardized face-centered representations and evaluated performance using a lightweight CNN alongside pretrained architectures, including VGG19.
VGG19 achieved the highest classification accuracy among tested architectures at 0.79.
The lightweight CNN achieved a classification accuracy of 0.78 while attaining the lowest loss value.

Cite This Study

Şengel et al. (2026) studied this question.

synapsesocial.com/papers/6a85638803308d306e2d6b5d https://doi.org/10.1108/ijicc-04-2026-0372

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August 19, 2026International Journal of Intelligent Computing and Cybernetics

Towards emotion-aware online learning through facial expression analysis

View Full Paper

Ask AI

Bookmark

Authors

ÖŞÖznur ŞengelIstanbul Kültür University FAFatma Patlar AkbulutIstanbul Kültür University CCCagatay CatalQatar University

Discussion

Member takes

Implication

Experimental study demonstrates accurate classification of student emotional states using lightweight convolutional neural networks, suggesting affective sensing can enhance digital learning.

Key Points

To evaluate the feasibility and accuracy of using facial expression analysis as an affective sensing system to detect student emotional states in online learning environments.
Collected facial video data from N=26 students during online learning sessions.
Mapped self-reported emotion labels into three affective categories: positive, neutral, and negative.
Transformed video streams into standardized face-centered representations and evaluated performance using a lightweight CNN alongside pretrained architectures, including VGG19.
VGG19 achieved the highest classification accuracy among tested architectures at 0.79.
The lightweight CNN achieved a classification accuracy of 0.78 while attaining the lowest loss value.

Cite This Study

Şengel et al. (2026) studied this question.

synapsesocial.com/papers/6a85638803308d306e2d6b5d https://doi.org/10.1108/ijicc-04-2026-0372

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