## 2 Literature Review
This study utilized two-time timescales; one was a 10 s video and the other was a 60 s video, and the study verified (Pearson r = 0.85) that a 10 s video clip was enough to detect the students' engagement level [2]. It is argued that facial expressions are not enough to observe the student's behavioral and emotional patterns during learning. The alternative way is to recognize facial expressions along with physical facial and body actions. In 2020, a hybrid convolutional neural network architecture was proposed that detected the affective state of the student in a classroom environment. Two models were used in the proposed architecture; one model was for detecting the SE of a single student, and the other was for detecting the SE of multiple students in a single frame. This model incorporated the facial emotions, body postures, and hand gestures of students to analyze students' affective states. Inception V3 was used to train the hybrid model. However, this model provides the aggregated score of all the states that are detected in each frame, but the training and testing dataset was generated through the controlled environment in an offline classroom environment [23]. Later, another research work was conducted that detected the engagement level of students using ResNet, temporal convolutional network, and neural Turing machine. These tools used already available datasets such as DAiSEE and EmotiW for the training as well as for the testing. However, up to 65% accuracy is achieved due to the constrained training dataset [20, 21, 22]. Advanced machine learning techniques have been introduced such as recurrent neural networks, RCNN, Encoder-Decoders, and long short-term memory (LSTM)-based attention networks are used for relation extraction time-series classification and Hybridizing LSTM contributing to the best level in some of the latest research, such as efficient federated distillation learning system (EFDLS) [41, 42, 43]. These advanced techniques could help in the future to improve the accuracy of the current study. Besides the work to compute and monitor SE levels for improving learning, post-analysis is also required. For instance, the timestamp of the day in which students usually remain more focused on learning, the impact of gender on SE, etc. The timestamp at which the students feel comfortable learning depends on their chronotype. The morning chronotype person usually sleeps and wakes up early, while the evening chronotype person usually sleeps late and therefore wakes up late in the morning. Several studies have been conducted on chronotype students for different purposes [39, 40, 41]. One of the types of research was conducted on high school students' entrance tests while considering their chronotype gathered through a questionnaire. The results concluded that the morning chronotype students performed better than the evening chronotype students [48]. In another similar work, the relationship between gender and student engagement was studied but at different university levels. These studies presented mixed results and concluded that results were related but varied from university to university [49]. Several other studies were conducted to check whether the chronotype impacts academic achievements or not.