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Related Experiment Video

Updated: Jan 10, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.2K

A comprehensive deep learning framework for real time emotion detection in online learning using hybrid models.

Mohammed Aly1, Nouf Saeed Alotaibi2

  • 1Department of Artificial Intelligence, Faculty of Artificial Intelligence, Egyptian Russian University, Badr City, 11829, Egypt. mohammed-alysalem@eru.edu.eg.

Scientific Reports
|November 25, 2025
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Labeling Emotion01:20

Labeling Emotion

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Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
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This study presents an advanced Facial Emotion Recognition (FER) system using deep learning for real-time online learner engagement detection. The novel approach achieves high accuracy, enhancing educational monitoring.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Facial Emotion Recognition (FER) is crucial for understanding user engagement in online learning environments.
  • Existing FER systems often lack the accuracy and real-time processing capabilities required for dynamic educational settings.

Purpose of the Study:

  • To develop and evaluate an advanced deep learning-based FER system for real-time monitoring of online learner engagement.
  • To identify the most effective predictive classification model for engagement detection using facial emotions.

Main Methods:

  • Integration of ResNet-50, Convolutional Block Attention Module (CBAM), 3D Convolutional Neural Networks (3D CNN), and Ant Colony and Genetic Algorithm-based Target Optimization (AGTO).
  • System evaluation on multiple FER datasets (FER2013, CK+, KDEF, proprietary dataset) for real-time engagement detection.
Keywords:
3D CNNAGTOCBAMDeep learningEmotion detectionFER

Related Experiment Videos

Last Updated: Jan 10, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.2K

Main Results:

  • Achieved high accuracies: 95.57% (FER2013), 97.29% (CK+), 98.35% (KDEF), and 98.09% (proprietary dataset).
  • Demonstrated significant improvements over existing FER approaches.
  • Real-time learning scenarios achieved a facial emotion classification accuracy of 97.3%.

Conclusions:

  • The proposed FER system enhances emotion recognition accuracy, refines feature relevance, and captures temporal dynamics.
  • The integrated model offers robustness and adaptability for online learning environments.
  • This approach enables precise interpretation of student emotions and engagement levels in real-time.