Student Burnout Detection using MBI-SS

This project explores student burnout using responses from the Maslach Burnout Inventory Student Survey (MBI-SS). We perform exploratory data analysis to visualize and understand patterns in student burnout levels, including gender-based differences and correlations among burnout symptoms.

🔍 Key Concepts

Burnout in students includes:

• Emotional exhaustion (EE)
• Cynicism toward studies (CY)
• Reduced sense of academic efficacy (AE)

The MBI is a reliable psychological instrument for quantifying these symptoms. Scores are aggregated to compute a Total Burnout Score.

📊 Analysis

• Load and visualize collected data
• Analyze distributions of burnout scores
• Compare burnout symptoms across genders
• Correlate individual components of burnout
• Discuss implications and next steps

📁 Files

• psyproject.ipynb: Main Jupyter Notebook
• Burnout Assessment Questionnaire for Univeristy Students(Responses) - Form.csv: Input data file

📈 Future Extensions

We hope to extend our project to include:

• Predict burnout using machine learning
• Clustering for risk profiling
• Longitudinal burnout tracking (if data available)

🤝 Contributors

• @aleena-zahra and @muaz-abdullahh we did this project as a initiative for our psychology class. The data was gotten from FAST isb students who consented to partake in the study