Student’s Attention Monitoring System in Learning Environments using Artificial Intelligence

Educational institutions have rapidly expanded their virtual course offerings and assessment methods. One significant challenge in the realm of online education is the effective assessment of student involvement, engagement, and attentiveness during virtual classes. In this project, a smartphone-based learning monitoring system is
presented. During pandemics, most of the parents are not used to simultaneously deal with their home activities and the monitoring of the home school activities of their children. Therefore, a system allowing a parent, teacher or tutor to assign task and its corresponding execution time to children, could be helpful in this situation. In this work, a mobile application to assign academic tasks to a child, measure execution time, and monitor the child's attention, is proposed. The children are the users of a mobile application, hosted on a smartphone or tablet device that displays an assigned task and keeps track of the time consumed by the child to perform this task. Time measurement is performed using face recognition, so it is possible to infer the attention of the child based on the presence or absence of a face. The app also measures the time that the application was in the foreground, as well as the time that the application was sent to the background, to measure boredom. The parent or teacher assigns a task using a desktop application specifically designed
for this purpose. At the end of the time set by the user, the application sends to the parent or teacher statistics about the execution time of the task and the degree of attention of the child.