Early Detection of School Disengagement Using MyBuddy Application

School disengagement is one of the pressing topics for educational equity in many countries worldwide, which will lead to school dropout. Dropping out of school has adverse consequences, including negative effects on employment, lifetime earnings, and physical health. Several attempts have been made to solve engagement issues, for example, the development of an intelligent tutoring system (ITS), which is useful to know when a student has disengaged from a task and might benefit from a particular intervention. However, predicting disengagement on a trial-by-trial basis is a challenging problem, particularly in complex cognitive domains. This paper emphasizes the MyBuddy mobile application developed as a smart classifier to identify the level of school disengagement risk among at-risk students in secondary schools. The working engine of MyBuddy is translated from a computational model that comprises fourteen predictors of four main entities: student, family, school, and surroundings. This application employs advanced mathematical models to analyze the data and generate risk scores indicating the dropout likelihood. This empowers counselors to proactively intervene and provide targeted support to the students who require it the most. MyBuddy offers a centralized platform to access and analyze aggregated data from multiple schools. This functionality allows them to smartly identify patterns, trends, and systemic challenges contributing to dropout rates. With comprehensive data-driven insights, district and state officers can design effective interventions and allocate resources strategically to mitigate dropout risks. Utilizing MyBuddy to revolutionize dropout prevention and foster a more inclusive and equitable education system in Kedah, Malaysia, aligns with the fourth Sustainable Development Goal, which aims to provide quality education for everyone.