Authors:
Asma Ayari
1
;
2
;
Mariem Chaabouni
2
and
Henda Ben Ghezala
2
Affiliations:
1
Esprit School of Engineering, Tunis, Tunisia
;
2
Univ. Manouba, ENSI, RIADI LR99ES26, Campus Manouba, 2010, Tunisia
Keyword(s):
Technology-Enhanced Learning, Education, e-Learning, Learner’s Engagement, Engagement Detection, Artificial Intelligence, Deep Learning, CNN, VGG-16.
Abstract:
The rise of online learning modalities, including fully online, hybrid, hy-flex, blended, synchronous, and asynchronous formats, has transformed educational landscapes. However, assessing learners' engagement in these environments, where direct teacher-student interaction is limited, poses a significant challenge for educators. In this context, Artificial Intelligence (AI) emerges as a transformative force within education, leveraging advanced algorithms and data analysis to personalize learning experiences and enhance teaching methodologies. The detection of engagement in educational settings is critical for evaluating the effectiveness of instructional strategies and fostering student participation. This study presents an approach to assess the learner’s engagement through detecting the facial emotions and classifying the level of engagement. This approach proposes a deep learning model specifically designed for automatic engagement detection in educational environments, employing
a Convolutional Neural Network (CNN) approach. We introduce an optimized CNN model tailored for recognizing learner engagement through facial expressions in distance learning contexts. By integrating the foundational elements of traditional CNN architectures with the widely acclaimed VGG-16 model, our approach harnesses their strengths to achieve exceptional performance. Rigorous training, testing, and evaluation on an augmented dataset demonstrate the efficacy of our model, which significantly surpasses existing methodologies in engagement recognition tasks. Notably, our approach achieves an accuracy of 94.10% with a loss rate of 10.39%, underscoring its potential to enhance the assessment of learner engagement in online education.
(More)