Enhancing Facial Expression Recognition System in Online Learning Context Using Efficient Deep Learning Model

Abstract

This paper presents an online educational platform that leverages facial expression recognition technology to monitor students' progress within the classroom. Periodically, a camera captures images of students in the classroom, processes these images, and extracts facial data through detection methods. Subsequently, students' learning statuses are assessed using expression recognition techniques. The developed approach then dynamically refines and enhances teaching strategies using the acquired learning status data. In the course of the experiment, we enhance facial expression recognition accuracy through the utilization of ResNet-50 for effective feature extraction. Additionally, by adjusting the residual down-sampling module, we bolster the correlation among input features, thus mitigating the loss of feature information. Simultaneously, a convolutional attention mechanism module is incorporated to reduce the influence of irrelevant areas within the feature map. The proposed method achieves an accuracy of 87.62% and 88.13 % on the RAF-DB and FER2013 expression datasets, respectively. In comparison with the original ResNet-50 network and the expression recognition outcomes found in existing literature, the suggested approach demonstrates enhanced accuracy and improved detection of students' learning states and expression variations. Consequently, the application of facial expression recognition technology in online learning, along with the optimization of online teaching resources and strategies grounded in th e results of recognition, holds tangible value for augmenting the quality of online learning experiences . We have benchmarked the proposed model against state-of-the-art techniques and conducted evaluations using the FER-2013, CK+, and KDEF datasets. The s ignificance of these results lies in their potential application within educational institutions.

Integrating artificial intelligence to assess emotions in learning environments: a systematic literature review

“…This capability is crucial in education for several reasons. Firstly, it allows educational There are online learning platforms that utilize affective computing principles to accurately identify six fundamental emotions: happiness, disgust, anger, surprise, sadness, and fear (Aly et al, 2023). Recognizing and addressing this range of emotions allows educational strategies to be more contextualized and effective.…”

Revolutionizing online education: Advanced facial expression recognition for real-time student progress tracking via deep learning model

“…It also provides several important pieces of information in communication such as the emotional and mental state of a person and also the intention, attitude, and personality of the communicating person. Several research fields have also tried to replicate this facial expression identification on a computational platform [1]. Simulation of the human face and describing various facial expressions have been subjects of interest in computer graphics and animation.…”

“…2, and can be displayed in a number of different ways. However, there are many more expressions than these basic six, and research continues on the identification of different emotions and how they are displayed [1,4]. The ability for a system to differentiate between these emotions and identify them correctly has obvious implications on human-computer interaction and the development of empathetic response in machines.…”

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“…This capability is crucial in education for several reasons. Firstly, it allows educational There are online learning platforms that utilize affective computing principles to accurately identify six fundamental emotions: happiness, disgust, anger, surprise, sadness, and fear (Aly et al, 2023). Recognizing and addressing this range of emotions allows educational strategies to be more contextualized and effective.…”

“…Additionally, the emotion-based artificial decision-making model has been shown to enhance the performance of educational agents in virtual settings (Yang and Zhen, 2014). Another approach involves the integration of emotional agents in AI-based learning environments to improve learner motivation, self-assessment, and self-motivation by improving the socioemotional climate (Gorga and Schneider, 2009), especially affective computing (Kort et al, 2001;González-Hernández et al, 2018;Ninaus et al, 2019;Shobana and Kumar, 2021;He et al, 2022;Aly et al, 2023;Villegas-Ch et al, 2023).…”