Using Emotion Analysis, Eye tracking, and Head Movement to Monitor Student Engagement among ESL Students with Facial Recognition Algorithm (Mediapipe)

The objective of the study is to monitor learning engagement of English as a Second Language (ESL) students using techniques such as Emotion analysis, eye tracking, and head movement, combined with facial recognition algorithms (Mediapipe). By analyzing students' emotional states, eye focus, and head movements, we aim to provide an objective way to assess students' levels of engagement and interest. This study will use Mediapipe algorithm to capture students' facial expressions and movements and correlate them with students' learning performance. By gaining insight into students' emotional responses and allocation of attention, we will be able to better understand their learning experience and engagement. The results obtained from this study will allow educators as well as researchers to understand the use of ESL learning environments and teaching methods.