## 1 Introduction  
Facial expressions are a fundamental aspect of human communication that convey emotions, intentions, and reactions. In educational environments, the ability to accurately perceive and respond to students' emotional states can significantly impact the students' learning experience. Traditional facial expression recognition systems have limitations in handling complex expressions and varying lighting conditions. Research on these environments has received widespread attention in multiple disciplines, including computer science, psychology, architecture, and education [1]. Usually, the learning flow in these environments depends on the learner's mental responses based on solving tests and answering exam questions, enabling the next level of the learning process to be reached. However, conventional methods do not consider the emotional behavior of the learner during the learning process. Emotional behavior is an important factor in the quality of the learning process and the success of the desired learning outcomes. A dashboard can support emotion detection during online learning [2]. The learner's interactions with the learning environment can be classified into two categories. The first consists of mental responses to test questions, and the second consists of emotional responses through facial expressions. Therefore, integrating both types of responses is critical for developing more adaptive and intelligent computer-based learning environments [3]. Due to the importance of considering the learner's emotions during the learning process, other research efforts have focused on modelling these emotions by interpreting facial expressions using machine learning algorithms [4; 5; 6; 7; 8; 9]. Furthermore, researchers [10; 11] have tried to create models for studying the human mind, including analyzing various behaviors such as what kind of person someone is or their likes and dislikes. For example, in terms of applications, the benefit of experimenting on an FER dataset that can be adopted in the education sector is the ability to assess student satisfaction with the quality of learning when they are learning online using tools such as Zoom and Google Meets. However, facial expression recognition (FER) is a significant area of research with applications in the education field. Understanding students' emotional states and engagement levels can greatly enhance the environmental learning process. Research has explored the application of facial expression recognition to applied techniques such as those based on machine learning and deep learning [12]. Deep learning performance mostly depends on the image resolution [13; 14; 15], and preprocessing data during feature extraction and feature selection are very important for FER classification [16]. However, when a person is portraying a neutral expression, their internal feelings or hidden messages can be reflected on their face, and observers can perceive those feelings. The six most universal emotions are sadness, fear, happiness, disgust, surprise, and angry [17]. However, facial expressions and the emotional interpretations of those expressions are not the same thing. The emotional interpretations of expressions are inferred by a person's perception of the internal state of emotion. The current research is still examining the complex dataset FER2013 [18], which poses challenges for single machine learning models and deep learning approaches. Existing efforts encounter bottlenecks in terms of the accuracy, which are drawbacks when addressing FER2013 due to the following reasons:  
1. The existing approaches concentrate on feature processing within deep learning to effectively handle between five and seven multiple class labels. 2. The existing approaches rely on a single deep learning method, which is not amenable to resolving imbalanced data issues, particularly evident in a class of seven classes like the disgust class label. Multi-class labels in the FER2013 dataset are numerous and are benchmark class labels for challenging FER datasets.