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Impact of Mental Health on Academic Performance: A Study of University Students in Malaysia
Public Health & Epidemiology
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About
Contains survey data examining the correlation between mental health and academic performance among university students in Malaysia, specifically targeting students from the International Islamic University Malaysia (IIUM). The dataset focuses on how various mental health factors impact students' CGPA, providing insights valuable for educators, policymakers, and mental health professionals interested in supporting student well-being and academic achievement.
Dataset Features:
- Student ID: Unique identifier for each respondent.
- Age: Age of the student (in years).
- Gender: Gender of the student.
- CGPA: Cumulative Grade Point Average of the student.
- Mental Health Factors: Includes variables like anxiety, stress levels, sleep quality, and social support.
- Study Hours: Average hours spent studying per day.
- Physical Activity: Frequency of physical activity or exercise.
- Sleep Duration: Average hours of sleep per night.
- Support Systems: Access to social and familial support.
Usage:
This dataset can be used for:
- Mental health research: To explore the impact of mental health factors on academic performance among university students.
- Educational strategy: To guide university policies supporting student well-being and resilience.
- Predictive modeling: To create models that predict academic performance based on mental health and lifestyle factors.
Coverage:
The dataset includes anonymized data from students at IIUM in Malaysia, with a range of mental health and academic performance indicators.
License:
CC0 (Public Domain)
Who can use it:
- Researchers: For studies on student mental health and academic outcomes.
- Educational institutions: To develop supportive academic environments.
- Mental health professionals: To better understand student mental health needs in academic settings.
How to use it:
- Correlation analysis: Study the relationships between mental health, study habits, and academic performance.
- Data exploration: Examine trends in mental health factors across demographics within the student population.
- Predictive analytics: Build models to predict students' CGPA based on mental health and lifestyle variables.
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