Abstract
Understanding student engagement and burnout has long been a central concern in educational research. While prior studies have extensively examined levels and correlates of them, far less is known about the timing at which students transition into amotivation (i.e., burnout) state from motivated state. In present study, based on Cox proportional hazards modeling and using large-scale data from the Programme for International Student Assessment (PISA), we develop a computational simulation model to infer individual- and group-level distributions of academic burnout onset across study weeks. Results demonstrate systematic variation in the timing of academic burnout onset as a function of weekly study time, with longer study hours associated with an earlier increase in burnout risk. To facilitate practical application, we further implement an interactive ShinyApp that allows users to estimate academic burnout onset periods at the individual, classroom, and school levels. This study contributes a novel methodological framework for temporally locating motivational breakdown and offers a scalable tool for translating large-scale assessment data into actionable educational insights.