Smart Burnout Prediction System for Students Using Machine Learning
Keywords:
Academic performance, Artificial intelligence, Educational data mining, Machine learning, Predictive analytics, Stress prediction, Student burnout, Student well-beingAbstract
Student burnout has become one of the most significant challenges in modern education due to increasing academic pressure, examinations, assignments, project deadlines, competitive environments, and extracurricular activities. Continuous exposure to these factors can lead to emotional exhaustion, reduced motivation, decreased concentration, and poor academic performance. If burnout is not identified at an early stage, it may negatively affect students’ mental health, learning outcomes, and overall well-being. Traditional burnout assessment methods mainly rely on surveys, counseling sessions, and manual observation, which are often time-consuming and unable to provide continuous monitoring. This paper proposes a Smart Burnout Prediction System that utilizes machine learning and Predictive Analytics techniques to identify students who are at risk of academic burnout. The proposed system analyzes various academic, behavioral, and lifestyle factors such as attendance percentage, internal assessment scores, assignment completion rate, study hours, sleep duration, stress levels, and participation in academic activities. The collected data is preprocessed and analyzed using machine learning algorithms, including Decision Tree, Random Forest, and Logistic Regression, to classify students into Low, Medium, and High Burnout Risk categories. The system further provides personalized recommendations and preventive measures to help students effectively manage stress, improve time management, maintain a healthy study-life balance, and enhance academic performance. By enabling early detection and intervention, the proposed solution supports educational institutions in monitoring student well-being and implementing proactive support strategies. The integration of artificial intelligence, machine learning, and educational analytics makes the system a reliable and intelligent tool for improving student success, reducing burnout-related issues, and promoting a healthier learning environment. The proposed framework contributes to the development of smart educational systems by combining data-driven decision-making with student-centered support mechanisms.
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