eliel2003/student-burnout-analysis2026

🔥 Predicting Academic Burnout: A Multivariate Analysis of Student Stressors

Exploring how financial pressure, family expectations, and social support shape burnout in university students.

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📋 Abstract

Academic burnout is an increasingly recognized phenomenon with far-reaching consequences for student wellbeing and performance. This study investigates the relationship between external environmental stressors — specifically financial stress and family expectations — and academic burnout levels among university students, while examining the moderating role of social support.

The analysis draws from a large-scale synthetic dataset of 1,000,000 student records (20 features each). To ensure computational efficiency while maintaining statistical robustness, a stratified random sample of n = 2,000 records was extracted for this exploratory data analysis (EDA). The findings provide empirically grounded insights into the interplay of stressors and protective factors in academic environments.

🔬 Hypotheses

This study is guided by two primary hypotheses derived from the stress-buffering model in psychosocial research:

#	Hypothesis	Direction
H1	Higher levels of financial stress and family expectations are positively correlated with increased academic burnout.	Stressor → Burnout ↑
H2	Social support acts as a moderating buffer; the negative impact of environmental stressors on burnout will be significantly weaker for students with high social support.	Support → Burnout ↓

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📦 The Dataset

Property	Value
Source	Kaggle – Student Mental Health and Burnout Dataset
Total Records	1,000,000
Features	20
Sample Used	2,000 (random, random_state=42)
Target Variable	academic_burnout_level (composite of Stress, Anxiety & Depression scores)

⚠️ Dataset Note: Based on structural characteristics — zero missing values across 1M records, perfectly uniform feature distributions, and absence of real-world noise — this dataset is assessed to be synthetically generated. While this enables clean, reproducible analysis, findings should be interpreted with caution and may not directly generalize to real-world student populations.

Feature Categories

Category	Features
Demographics	age, gender, academic_year
Lifestyle	study_hours_per_day, sleep_hours, physical_activity, screen_time
Psychological	stress_level, anxiety_score, depression_score, exam_pressure
Environmental	financial_stress, family_expectation, social_support
Academic	academic_performance
Target	academic_burnout_level

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⚙️ Methodology

1. Data Loading & Sampling

The full dataset was downloaded via kagglehub and a reproducible random sample of 2,000 rows was extracted. The target variable was standardized from burnout_score to academic_burnout_level for semantic clarity.

2. Data Cleaning

A systematic quality assessment was performed:

• Missing values: None detected across all 20 features (confirmed via df.isnull().sum()).
• Duplicate rows: Zero duplicates found (confirmed via df.duplicated().sum()).
• Data types: All features confirmed to be in expected formats; no parsing or type conversion required.

3. Outlier Detection & Decision

Box plots were generated for the four key research variables. While extreme values were observed — particularly in academic_burnout_level (high end) and family_expectation (low end) — all outliers were retained.

Justification: These values represent legitimate extreme experiences within the student population. Removing high-burnout cases would systematically bias the analysis against the very phenomenon under study.

4. Feature Engineering

To enable group-level comparisons and multivariate visualization, three categorical bin variables were engineered:

📊 Key Visualizations & Insights

Figure 1 — Outlier Detection: Box Plots

The box plots reveal that academic_burnout_level exhibits the most pronounced outliers, with a tail of students experiencing extreme burnout. family_expectation shows a minority cluster near zero, suggesting a subset of students reporting minimal family pressure. The interquartile ranges for all four variables are well-contained, indicating that the distribution is not pathologically skewed for the majority of the sample.

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Figure 2 — Feature Distributions: Histograms

The distribution of financial_stress is approximately bell-shaped with a slight right skew, indicating that moderate financial pressure is most common while a minority of students experience severe financial hardship. social_support mirrors this pattern inversely, with most students reporting moderate support levels. academic_burnout_level follows a roughly normal distribution, centered around a mid-range value, confirming that the target variable captures meaningful variation across the sample.

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Figure 3 — Relationship Analysis: Scatter Plots

The scatter plot of financial_stress vs. academic_burnout_level reveals a positive, though diffuse, linear trend — as financial stress increases, burnout tends to rise, consistent with H1. The plot of social_support vs. burnout demonstrates the opposite pattern, with higher support associated with reduced burnout, providing initial visual support for H2. The high dispersion in both plots underscores that burnout is a multi-determined outcome not fully explained by any single variable.

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Figure 4 — Research Questions: Bar Chart Panel

Q1 — Does financial stress increase burnout?

Mean burnout levels rise monotonically from 1.12 (Low stress) to 2.66 (High stress), more than doubling across the financial stress spectrum. This constitutes strong empirical support for H1.

Q2 — Does social support reduce burnout?

Mean burnout decreases from 2.45 (Low support) to 1.19 (High support) as social support increases — an inverse relationship of comparable magnitude to Q1. This provides initial support for H2.

Q3 — Which factor correlates most strongly with burnout?

financial_stress shows the highest positive correlation with burnout (r = 0.32), followed by family_expectation (r = 0.23). social_support yields the strongest negative correlation (r = −0.23), confirming its role as a protective factor rather than an additional stressor.

Q4 — Does burnout vary across age groups?

Burnout levels remain relatively stable across age groups (1.64 for <20, **1.83** for 21–23, **1.84** for >23), suggesting that age is not a primary driver of burnout in this dataset.

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Figure 5 — Correlation Heatmap

The heatmap confirms that academic_burnout_level is most strongly correlated with financial_stress (r = 0.32) and negatively with social_support (r = −0.23). Notably, financial_stress and social_support exhibit a low inter-correlation (r ≈ −0.02), indicating these are largely independent constructs — students with high financial stress are not systematically less likely to have social support, which strengthens the validity of treating them as separate predictors.

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Figure 6 — Multivariate Analysis: The Buffering Effect ⭐

This chart constitutes the key finding of the study. Among students with high financial stress, those with low social support report an average burnout of 3.83, while those with high social support report only 1.80 — a reduction of more than 50%. This dramatic attenuation of the stress-burnout relationship at high support levels provides compelling support for H2: social support functions as a genuine psychological buffer against environmental stressors.

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🏁 Final Conclusions

Hypothesis Outcomes

Hypothesis	Status	Evidence
H1: Financial stress & family expectations → higher burnout	✅ Confirmed	Burnout doubles from Low → High stress group; r = 0.32 for financial stress
H2: Social support buffers the stress-burnout relationship	✅ Confirmed	50%+ reduction in burnout among high-stress students with high support

Main Takeaway

The central finding of this analysis is that social support is not merely a correlate of lower burnout — it actively moderates the damage caused by financial stress. A student experiencing high financial pressure is not destined for high burnout; robust social support networks can cut that risk in half.

From a policy standpoint, this suggests that university interventions targeting burnout should focus not only on reducing stressors (e.g., financial aid, managing family expectations) but critically on strengthening social support systems — peer programs, counseling access, and community-building initiatives — particularly for students in high-pressure financial circumstances.

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🛠️ Technical Stack

Language    : Python 3.12
Data        : pandas, numpy
Visualization: matplotlib, seaborn
Dataset Hub : kagglehub
Environment : Google Colab

Library	Version	Purpose
pandas	≥ 2.0	Data loading, cleaning, feature engineering
seaborn	≥ 0.13	Statistical visualizations (heatmap, barplots)
matplotlib	≥ 3.7	Plot rendering and layout management
kagglehub	≥ 1.0	Dataset download from Kaggle
numpy	≥ 1.24	Numerical operations

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📁 Repository Contents

📦 student-burnout-eda/
├── 📓 Assignment_1_EDA_Dataset-4.ipynb   # Full analysis notebook
├── 📄 README.md                           # This file
└── 🎥 presentation.mp4                   # 2–3 min walkthrough video

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Analysis conducted as part of a Data Science coursework assignment — March 2026.