Student-wellbeing-analysis

Overview

The analysis utilizes data from 1,100 students, assessed through a Likert scale ranging from 0 to 5. In this scale, higher values represent more severe or stronger conditions. Key areas of focus include:

Key Areas of Focus:

• Psychological Factors: Anxiety levels, self-esteem, depression
• Physiological Factors: Sleep quality, frequency of headaches
• Environmental Factors: Living conditions, noise levels, unmet basic needs
• Academic Factors: Academic performance, study load
• Social Factors: Incidence of bullying, participation in extracurricular activities

Power BI Dashboard Breakdown

The Power BI report is structured into three main pages:

1. Page 1: Includes correlations between critical factors such as:

   • Anxiety and academic performance
   • Depression and sleep quality
   • Bullying and mental health history

2. Page 2:

   • Presents metrics with DAX measures, offering a clear view of the data collected.

3. Page 3:

   • Features a Correlation Heatmap that visually represents the relationships between various variables.
   • Notable correlations include:
     • Poor sleep correlating strongly with depression (0.71).
     • Anxiety negatively impacting academic performance (-0.65).
     • Insights on how bullying is associated with mental health issues.

DAX Measures

• Academic negative effect = COUNTROWS(FILTER(StressLevelDataset,StressLevelDataset[academic_performance]<3&&StressLevelDataset[study_load]>3&&StressLevelDataset[teacher_student_relationship]<3&&StressLevelDataset[future_career_concerns]<2))
• Physiological negative effect = COUNTROWS(FILTER(StressLevelDataset,StressLevelDataset[headache]>3&&StressLevelDataset[blood_pressure]>2&&StressLevelDataset[sleep_quality]<3&&StressLevelDataset[breathing_problem]>3))
• Environmental negative effect = COUNTROWS(FILTER(StressLevelDataset,StressLevelDataset[noise_level]>3&&StressLevelDataset[living_conditions]<3&&StressLevelDataset[safety]<2&&StressLevelDataset[basic_needs]<3))
• Psycological negative effect = COUNTROWS(FILTER(StressLevelDataset,StressLevelDataset[anxiety_level]>10&&StressLevelDataset[self_esteem]<15&&StressLevelDataset[mental_health_history]=1&&StressLevelDataset[depression]>15))
• Social negative effect = COUNTROWS(FILTER(StressLevelDataset,StressLevelDataset[social_support]<1&&StressLevelDataset[peer_pressure]>3&&StressLevelDataset[extracurricular_activities]<3&&StressLevelDataset[bullying]>2))
• Below avg academics = COUNTROWS(FILTER(StressLevelDataset,StressLevelDataset[academic_performance]<3))
• extracurricular activity = COUNTROWS(FILTER(StressLevelDataset,StressLevelDataset[extracurricular_activities]>=2))
• Frequent headaches = COUNTROWS(FILTER(StressLevelDataset,StressLevelDataset[headache]>=5))
• High noise = COUNTROWS(FILTER(StressLevelDataset,StressLevelDataset[noise_level]>=2))
• Percentage of bullying = DIVIDE(COUNTROWS(FILTER(StressLevelDataset,StressLevelDataset[bullying]>=3)),COUNTROWS(StressLevelDataset))*100
• Percentage students with depression = DIVIDE(COUNTROWS(FILTER(StressLevelDataset,StressLevelDataset[depression]>10)),COUNTROWS(StressLevelDataset))*100
• Poor sleep = COUNTROWS(FILTER(StressLevelDataset,StressLevelDataset[sleep_quality]<=2))
• Students with low self esteem = COUNTROWS(FILTER(StressLevelDataset,StressLevelDataset[self_esteem]<AVERAGE(StressLevelDataset[self_esteem])))
• unmet basic needs = COUNTROWS(FILTER(StressLevelDataset,StressLevelDataset[basic_needs]<=2))

Python code for Correlation Heatmap

import seaborn as sns
import matplotlib.pyplot as plt
sns.heatmap(dataset.corr(), cmap='Purples', annot=True)
plt.show()

This analysis is for educational purposes only