Early detection of student burnout using data science: a study of behavioral and psychological indicators
This study examined behavioral and psychological predictors of burnout among high school and university students in Pakistan using survey data and machine-learning models. Shorter sleep and greater mental fatigue—especially fatigue—were associated with higher burnout risk, while a Random Forest model successfully identified students at risk of burnout.
Read More...Identifying anxiety and burnout from students facial expressions and demographics using machine learning
The authors used machine learning to predict the presence of anxiety and burnout in students based on facial expressions and demographic information.
Read More...