# Deep Analysis of Student Body Activities to Detect Engagement State in E-Learning Sessions
## 1 Introduction  
The importance of e-learning systems has increased considerably in the post-pandemic world. E-learning systems provide a continuous, flexible, and affordable educational process. However, students using e-learning systems are more easily distracted, get bored, and are more likely to drop out from online courses [1]. To provide a better learning experience, engagement level indicators/detectors can be added to e-learning platforms. The automatic detection and recognition of students' engagement states will help instructors adapt and diversity content and activities. Furthermore, the adaptation of e-learning systems can be automated based on learner engagement with content (changing learning and assessment strategies), especially for negative states. It is important to be able to measure the engagement of students accurately in order to assess the effectiveness of the learning process. The evolution of e-learning systems has motivated this study to investigate a new method to detect student engagement during e-learning sessions. The reported results will assist in developing intelligent interactive systems (intelligent tutoring systems). These systems can provide feedback and improvements according to the automatically measured performance of the student [2]. Measuring student engagement is part of affective computing. Affective computing is an interdisciplinary field concerned with developing models and devices that have the ability to recognize, interpret, and simulate human affects [3]. It helps in the development of more intelligent systems that are aware of users' affections. Therefore, it can improve the interaction between students and the e-learning systems and ensure that the students stay engaged and motivated.  
This paper contributes to advancing science and technology in the online education area in at least two ways: (1) new knowledge from learner behavior (engagement level), which is an influencing factor of decision making in the customized education experience through interactive e-learning systems; and (2) new modality (body activities) and technique (deep 3D CNN model), which allow a shift from static learning materials to more dynamic interactive and adaptive content.  
There are different techniques and approaches to measuring student engagement levels: data mining, using digital data, and students' GPAs or test marks [4]; vision-based methods, such as those described in [5; 6], where the researchers proposed a binary model with two levels--engaged or disengaged; and another proposed model with three levels--high, medium, and low [7]. This work follows the newly proposed affective model [8]. It has been developed for detecting student engagement levels in the learning environment. The model is composed of the following five levels: strong engagement, high engagement, medium engagement, low engagement, and disengagement. The model also defines two implicit categories for the proposed five levels: Positive engagement and negative engagement. The scope of this study is to generate deep learning models to detect student engagement levels by analyzing spatiotemporal features from real videos recorded in uncontrolled e-learning environments. The proposed models focus on discovering knowledge from the learner's body actions. In our study, we included undergraduate students from King Abdulaziz University. The data were collected during the COVID-19 lockdown.