# Measuring Student Engagement through Behavioral and Emotional Features Using Deep-Learning Models
## 1 Introduction  
Student engagement in the classroom environment refers to a student's involvement, interest, and participation during a lecture. Student engagement may be further categorized in terms of cognitive, emotional, and behavioral contexts [1]. More precisely, cognitive engagement relates to the mental efforts that students make in the learning process in class. Emotional engagement refers to students' emotional responses toward learning, whereas behavioral engagement is inferred from the student's actions such as paying attention, completing tasks, and following instructions. Since behavioral and emotional engagements are interrelated, and behavioral engagement influences cognitive engagement positively, behavioral engagement should be precisely assessed to improve the learning experience of students [2]. To improve learning and the subsequent academic progress, students' engagement in the classroom environment is necessary. However, students may lose interest in the content delivered during a lecture due to a lack of relevance, a lack of interaction, a lack of variety, the teaching style, distractions, personal reasons, the lack of a challenge, and complex oroverloaded information. Such issues are mostly observed during lectures in a traditional offline classroom environment (OCE) [3]. However, measuring students' engagement in comparatively large classes (more than 30 students) becomes difficult [4]. Moreover, the limited teaching resources, the limited time, the difficulty in observing non-verbal cues, and the limited training of an instructor may also be other challenges in measuring student engagement. Thus, alternative methods may be incorporated for the measurement of student engagement in an OCE. Thus, a computer-vision-based solution may be suggested as a more feasible solution for measuring student engagement. More precisely, non-verbal clues such as affective states, including being focused, feeling sleepy, yawning, and a skewed head pose (right or left), may be incorporated to monitor student engagement. When the students are found to be engaged, the level of focus through facial expressions may be used to measure the level of student engagement. It has been statistically proven in the prevalent work that students' facial expressions may reveal their behavioral engagement [5]. Most researchers have incorporated computer vision for student engagement monitoring in e-learning environments [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18]. However, these proposals monitor engagement in a controlled environment with few students [19; 20; 21]. Moreover, e-learning or an online learning environment differs from an OCE. Some works have also been proposed for student engagement monitoring in an OCE or in similar settings [2; 22; 23; 24; 25; 26; 27]. However, these proposals utilize dedicated physical devices or sensors [6; 22; 24], incorporate only behavioral features [7; 23; 25], test on limited data [8; 27], are prone to efficiency issues [26], or use only emotional features [2]. Moreover, some of these proposals utilize a single pretrained transfer-learning (TL) model [8; 25; 28] or train a machine-learning or convolutional neural network model [2; 6; 8; 22; 23; 24; 26; 27]. These limitations show that more pretrained and self-trained models may be incorporated for measuring student engagement in order to compare the performance of the underlying models. Moreover, behavioral and emotional features may be simultaneously incorporated for measuring student engagement. Furthermore, the level of engagement may be revealed instead of only classifying the affective states. The proposed work utilizes pretrained and self-trained models along with behavioral and emotional features to measure student engagement in an OCE. More precisely, along with other contributions, the underlying work aims to find the answers to the following research questions: Can TL be effectively used for measuring student engagement in an OCE? Which TL model surpasses the others for measuring student engagement in an OCE?