A Review on Different Approaches for Assessing Student Attentiveness in Classroom using Behavioural Elements

Analyzing one's participation and attention may be useful in a variety of contexts, like work situations such as driving a car, defusing a bomb, and many learning environments. Increasing the student's involvement and participation in the classroom has been proven to improve learning results. Attention is core for effective learning, yet analyzing attention is a tricky task. People have been working on attention analysis for decades, and as a result, current learning systems contain methods for monitoring and reporting on students' attention states. Facial features and eye movements are some of the important behavioural features to access attentiveness. Approaches such as EEG signals, gaze detection, head and body posture detection are used in this context as they provide rich information about a person's behavior and thoughts. It also gives essential information for interpreting their nonverbal, cues. These are referred to be “honest signals” since they are unconscious patterns that reveal the focus of our attention. They give vital indications concerning teaching methods and students' responses to various conscious and unconscious teaching tactics inside the classroom. Examining verbal and nonverbal conduct in the classroom can give valuable input to the instructor. This paper will go through various approaches available for analyzing student attentiveness for effective learning in the classroom. Integrating different technical approaches with Machine learning and Deep learning models accuracy up to 90% can be observed in different research with minimum error.