Determine the Level of Concentration of Students in Real Time from their Facial Expressions

Bouhlal Meriem,Kaiss Wijdane,Mohamed Amine Naji,Habib Benlahmar,Elfilali Sanaa

doi:10.14569/ijacsa.2022.0130119

Bouhlal Meriem, Kaiss Wijdane + Show 3 more

Open Access

https://doi.org/10.14569/ijacsa.2022.0130119

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Abstract

In teaching environments, student facial expressions are a clue to the traditional classroom teacher in gauging students' level of concentration in the course. With the rapid development of information technology, e-learning will take off because students can learn anytime, anywhere and anytime they feel comfortable. And this gives the possibility of self-learning. Analyzing student concentration can help improve the learning process. When the student is working alone on a computer in an e-learning environment, this task is particularly challenging to accomplish. Due to the distance between the teacher and the students, face-to-face communication is not possible in an e-learning environment. It is proposed in this article to use transfer learning and data augmentation techniques to determine the concentration level of learners from their facial expressions in real time. We found that expressed emotions correlate with students' concentration, and we designed three distinct levels of concentration (highly concentrated, nominally concentrated, and not at all concentrated).

Highlights

In recent years, E-learning is very popular because this type of learning system uses modern educational technologies to implement an ideal learning environment by integrating information technology into the program.E-learning is gaining prominence in universities, colleges, and industries by examining its advantages over traditional approaches, as students can access all the data they need for their research
We experimented with transfer learning models on the dataset we combined (JAFEE + FER2013+Cohn-Kanade Database (CK)+) for facial expression recognition to determine the concentration level of students during educational tasks and estimated their effectiveness
The results show that VGG16 performs better than other proposed models, while the results show that the VGG19 model could achieve decent accuracy in facial expression recognition

Summary

Introduction

E-learning is gaining prominence in universities, colleges, and industries by examining its advantages over traditional approaches, as students can access all the data they need for their research. Through webinars, they can access information they might not otherwise be able to access in person due to finances, distances, or time constraints. Depending on the level of understanding of the student, they can study at their own pace, which may increase their satisfaction with the course and reduce their stress levels. While the online environment separates teachers from students and students from students, there is a lack of face-to-face communication to understand students' emotions and cognitive state

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