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Learning State Assessment in Online Education Based on Multiple Facial Features Detection.

Deguang Li¹, Zhanyou Cui², Fukang Cao¹

¹School of Information Technology, Luoyang Normal University, Luoyang 471934, China.

Computational Intelligence and Neuroscience

|February 8, 2022

Summary

This study introduces an online learning state assessment using blink, yawn, and head pose detection. This approach effectively monitors learner engagement and supports improved online education strategies.

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Area of Science:

Computer Science
Human-Computer Interaction
Educational Technology

Background:

Online training often lacks effective supervision, leading to challenges in assessing learner engagement.
Monitoring learner state is crucial for improving the effectiveness of online educational platforms.
Existing methods may not adequately capture the nuances of learner attention and fatigue.

Purpose of the Study:

To develop and validate an effective online learning state assessment approach.
To combine multiple physiological and behavioral indicators for comprehensive learner state evaluation.
To enhance the real-time monitoring capabilities for online learning environments.

Main Methods:

Utilized blink detection via eye aspect ratio and blink frequency to assess eye fatigue.
Implemented yawn detection using mouth aspect ratio with inner lip features for accurate mouth state analysis.
Employed head pose estimation through 3D face model matching and Euler angle calculation to track head orientation.
Processed all detections using 2D grayscale images for computational efficiency and real-time performance.

Main Results:

The combined approach effectively differentiates various online learner states.
Numerical analysis of blinking, yawning, and head pose changes accurately reflects learning engagement.
Experimental results demonstrate the system's capability to evaluate online learning states.

Conclusions:

The proposed method offers a robust and efficient solution for assessing online learning states.
This technology can provide valuable support for the advancement and personalization of online education.
Integrating behavioral cues like blinking, yawning, and head pose offers a promising direction for intelligent tutoring systems.