Multitask EfficientNet affective computing for student engagement detection

Title

Multitask EfficientNet affective computing for student engagement detection

Creator

Thiruthuvanathan M.M.; Krishnan B.

Description

In the realm of education, feedback emerges as a pivotal component, serving to foster engagement and interaction while also facilitating the refinement of teaching methods to capture and maintain student attention. Traditional classroom assessment methods often struggle to accurately gauge the degree of comprehension among students during lectures, relying on manual comment collection that inherently carries the risk of inaccuracies. In response to this challenge, a novel system has been proposed, harnessing the power of Facial Emotion Recognition (FER) technology to capture student feedback. Within this framework, students are given a unique avenue to convey their emotions and reactions, employing facial expressions and gestures as the means to communicate. This innovative approach enables the analysis of students emotional responses and thereby provides invaluable insights into their comprehension levels, as well as the overall quality and engagement experienced during lectures. The approach takes shape through the utilization of Computer Vision techniques, with a particular focus on an unobtrusive methodology for assessing students overall engagement. Overcoming limitations of traditional assessment, our approach integrates compound scaling, employing the proposed Multitask EfficientNetB0 model recognized for its proved accuracy in emotion recognition (95.7%) and behavior analysis (96.3%) across diverse datasets (DAiSEE, iSED, iSAFFE). The behavioral classification system categorizes students into Engaged and Disengaged classes within a multi-class framework, providing nuanced insights into comprehension and Student engagement. Assessment metrics, including ROC Curves, Precision, Recall, and F1-Score, ensure a thorough evaluation. Our systems adaptability is demonstrated across varied educational environments, showcasing real-world efficacy in classrooms, laboratories, and seminar halls. The inclusion of MTCNN enhances face detection capabilities, facilitating robust analysis in dynamic scenarios. Expanding its applicability, the model has been put to the test in a range of educational settings, including classrooms, laboratory environments, and seminar halls, offering dual-capability analysis of both emotions and behavior. This comprehensive approach yields nuanced insights into student engagement and interaction, and its performance has been validated through real-world deployment within classrooms and seminars The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

Source

Multimedia Tools and Applications

Date

2024-01-01

Publisher

Springer

Subject

Affective computing; Behavioral analysis; Deep learning; Emotional analysis; Engagement analysis; Face detection; Multi task-EfficientNEtB0; Multi-modal student analysis

Coverage

Thiruthuvanathan M.M., Computer Science and Engineering, CHRIST University, Kanmanike, Karnataka, Bengaluru, 560029, India; Krishnan B., Computer Science and Engineering, CHRIST University, Kanmanike, Karnataka, Bengaluru, 560029, India

Rights

Restricted Access

Relation

ISSN: 13807501; CODEN: MTAPF

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1007/s11042-024-19815-3

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198561288&doi=10.1007%2fs11042-024-19815-3&partnerID=40&md5=2e31e86f48ce81ce6aab8217e2211713