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Role of Affect in Interpersonal Attraction

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Related Experiment Video

Updated: Jun 6, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Intelligent emotion and behavior recognition for online learners using deep affective computing.

Priya K1, Pradeep G2, Ravimaran S3

  • 1Research Scholar, Department of AI&DS, Saranathan College of Engineering, Tiruchirappalli, Tamilnadu, India.

Scientific Reports
|June 4, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an affective-computing framework to analyze webcam video for recognizing student emotions and engagement in online classes. The system uses facial analysis and temporal modeling for real-time monitoring, enhancing virtual learning environments.

Keywords:
Affective computingBehavior recognitionEmotion recognitionOnline learningSpatio-temporal modellingStudent engagement

Related Experiment Videos

Last Updated: Jun 6, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Area of Science:

  • Affective computing
  • Computer vision
  • Educational technology

Background:

  • Online learning platforms often lack real-time insights into student emotional states and engagement levels.
  • Effective virtual instruction requires understanding learner affect to adapt teaching strategies.
  • Current methods for assessing student engagement in online settings are often indirect or delayed.

Purpose of the Study:

  • To develop and evaluate a practical affective-computing framework for real-time recognition of student emotions and behavioral states during live virtual sessions.
  • To improve instructors' visibility into student affect and engagement through webcam video analysis.
  • To provide a computationally feasible solution for monitoring learners in online educational contexts.

Main Methods:

  • Facial detection and feature extraction using a convolutional neural network with an attention module.
  • Temporal modeling of extracted facial features with a bidirectional Gated Recurrent Unit (GRU) to predict behavioral states.
  • Evaluation on benchmark datasets (FER2013) for emotion recognition and a specific online learning dataset (EmoDetect) for behavior recognition.

Main Results:

  • Achieved 94.5% accuracy on the FER2013 Private Test for emotion recognition.
  • Reached 86.9% accuracy with a macro-F1 score of 85.4% on a four-class behavior recognition task (engaged, confused, frustrated, disengaged).
  • Demonstrated real-time processing capability at approximately 25 frames per second on a mid-range GPU.

Conclusions:

  • Combining attention-based facial representations with temporal modeling offers effective indicators of learner affect and engagement.
  • The proposed framework provides a practical and feasible solution for real-time monitoring of students in online learning environments.
  • This technology can significantly enhance instructors' ability to support student learning and well-being in virtual classrooms.