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Facial emotion recognition based real-time learner engagement detection system in online learning context using deep

Swadha Gupta1, Parteek Kumar1, Raj Kumar Tekchandani1

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Summary

This study introduces a deep learning method to monitor student engagement in online learning by analyzing facial emotions. ResNet-50 achieved over 92% accuracy in classifying engaged and disengaged states, enhancing virtual education.

Keywords:
Deep learningEmotion detectionEngagement detectionFacial expressionsOnline learnerOnline learningReal-time engagement detection

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Area of Science:

  • Artificial Intelligence
  • Educational Technology
  • Computer Vision

Background:

  • The COVID-19 pandemic necessitated a rapid shift to online learning, highlighting challenges in maintaining student engagement.
  • Traditional classroom engagement metrics are difficult to translate to virtual environments.
  • Effective student engagement is crucial for successful online learning outcomes.

Purpose of the Study:

  • To propose and evaluate a deep learning-based system for real-time detection of online learner engagement.
  • To analyze facial emotions for classifying engagement states as 'Engaged' or 'Disengaged'.
  • To compare the performance of different deep learning models for this task.

Main Methods:

  • A deep learning approach utilizing facial emotion recognition to assess student engagement.
  • Analysis of facial expressions to classify emotions during online learning sessions.
  • Implementation and comparison of Inception-V3, VGG19, and ResNet-50 models.
  • Validation using benchmarked datasets (FER-2013, CK+, RAF-DB) and a custom dataset.

Main Results:

  • ResNet-50 demonstrated the highest accuracy at 92.32% in classifying facial emotions for engagement detection.
  • Inception-V3 and VGG19 achieved accuracies of 89.11% and 90.14%, respectively.
  • The system effectively predicts 'Engaged' and 'Disengaged' states based on facial emotion recognition.

Conclusions:

  • Deep learning models, particularly ResNet-50, are effective for real-time online learner engagement detection.
  • Facial emotion analysis provides a viable method for assessing engagement in virtual learning environments.
  • This technology can help educators adapt and improve online teaching strategies to foster better student participation.