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Assessing student engagement from facial behavior in on-line learning.

Paolo Buono¹, Berardina De Carolis¹, Francesca D'Errico²

¹Department of Computer Science, University of Bari 'Aldo Moro', Via Orabona 4, Bari, 70125 Italy.

Multimedia Tools and Applications

|October 31, 2022

Summary

This study used facial behavior analysis and Long Short-Term Memory (LSTM) networks to predict student engagement in online learning. While overall engagement prediction showed weak correlation, emotional engagement showed stronger links with facial movements and gaze.

Keywords:

Computer vision Engagement measurement Face behavior LSTM

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Area of Science:

Computer Science
Educational Technology
Affective Computing

Background:

Automatic monitoring of learner engagement in distance education is crucial for personalized support.
Facial behavior, including action units, gaze, and head poses, offers potential indicators of engagement.
Existing methods require validation in real-world learning contexts.

Purpose of the Study:

To investigate the assessment of student engagement levels using facial behavior.
To propose and evaluate a Long Short-Term Memory (LSTM) network model for engagement prediction.
To compare model-assessed engagement with students' self-perceived engagement.

Main Methods:

Utilized the EmotiW 2019 challenge dataset for model training.
Conducted an experiment with students in an online lecture setting.
Collected video data of student facial behavior and post-session engagement questionnaires.

Main Results:

Global engagement prediction from facial behavior showed a weak correlation with subjective self-evaluation.
Emotional engagement prediction demonstrated a stronger correlation with facial behavior.
Facial action units and head pose positively correlated with emotional engagement, while gaze showed an inverse correlation.

Conclusions:

Facial behavior analysis, particularly focusing on emotional dimensions, can offer insights into student engagement.
LSTM networks show promise for automated engagement assessment in online learning environments.
Further research is needed to refine models and improve the correlation between objective and subjective engagement measures.