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A multimodal facial cues based engagement detection system in e-learning context using deep learning approach.

Swadha Gupta1, Parteek Kumar1, Rajkumar Tekchandani1

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Summary

This study introduces a novel system to detect student engagement during e-learning by analyzing facial expressions, eye blinks, and head movements. The deep learning-based approach achieved 92.58% accuracy, offering real-time feedback for enhanced virtual education.

Keywords:
Deep learningEmotion detectionEngagement detectionEye-blinkingFacial expressionsHead-movementOnline learningReal-time

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Area of Science:

  • Education Technology
  • Computer Science
  • Artificial Intelligence

Background:

  • The COVID-19 crisis necessitated a rapid shift to virtual learning environments.
  • Monitoring student engagement and providing feedback in e-learning is challenging due to the lack of physical observation.
  • Existing e-learning platforms often lack effective mechanisms for real-time engagement assessment.

Purpose of the Study:

  • To develop and evaluate a novel system for real-time detection of student engagement during e-learning sessions.
  • To address the limitations of virtual education by providing immediate feedback based on student behavior.
  • To enhance the effectiveness of online learning through accurate engagement monitoring.

Main Methods:

  • A multimodal approach analyzing facial expressions, eye blink count, and head movements from live video streams.
  • Implementation using deep learning models: VGG-19 and ResNet-50 for facial emotion recognition.
  • Utilizing facial landmark detection for eye blink and head movement analysis, combined to calculate an Engagement Index (EI).

Main Results:

  • The proposed system accurately predicts student engagement states (engaged or disengaged) based on the calculated EI.
  • The multimodal system achieved a high accuracy of 92.58% in experimental evaluations.
  • The developed engagement detection approach significantly outperforms existing methods in real-time performance.

Conclusions:

  • Facial cues-based multimodal systems can accurately determine student engagement in real-time e-learning environments.
  • The proposed system offers a viable solution for improving student feedback and interaction in virtual classrooms.
  • This research contributes to the advancement of adaptive e-learning systems through intelligent engagement monitoring.