Early Detection of Academic Burnout Using Student Interaction Logs and Machine Learning

Abstract

Academic burnout is a growing concern in today's digitally driven education system. The prolonged stress of online learning, frequent assessments, and lack of physical interaction has increased the risk of mental fatigue among students. This paper presents a machine learning–based approach to detect early signs of academic burnout using student interaction logs collected from online platforms. The proposed model analyses behavioural data such as login frequency, study time, quiz performance, and emotional feedback to predict burnout risk levels. Early detection allows timely intervention from educators and counsellors, improving student well-being and academic performance.