Mapping Support Networks and Academic Outcomes Among DACA Students

Contents

1. Introduction
2. Research Questions
3. Load Libraries
4. Load Data
5. Dataset 1: Mental Health & Resilience
6. Dataset 2: Student Performance
7. Dataset 3: College Experience
8. Network Visualization
9. Comparative Insights
10. Limitations
11. Conclusion

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1. Introduction

DACA recipients face barriers in higher education including financial constraints, psychological stress, and limited access to institutional resources. This study explores how support networks influence academic outcomes using three independent datasets.

Due to differences in sampling, datasets are analyzed separately and interpreted comparatively.

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2. Research Questions

• How do support patterns vary across stress and hardship?
• Do support structures predict GPA and persistence?
• Where are structural gaps in support systems?

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3. Load Libraries

library(tidyverse) library(igraph) library(ggraph)

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4. Load Data

Working Directory: [1] “/Users/yourname/daca_project” Files in directory: [1] “mental_health.csv” “performance.csv” “college_experience.csv” Datasets loaded successfully: mental_health: 1010 rows, 41 columns
performance: 14000 rows, 15 columns
college_exp: 2500 rows, 20 columns

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5. Dataset 1: Mental Health & Resilience

Summary

Variable Type Description academic_stress numeric Stress level anxiety_level numeric Anxiety measure GPA numeric Academic performance support variables mixed Social support indicators

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Support Index (Proxy Variable)

A composite variable was created using all columns containing “support”.

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Regression Model Results

Call: lm(GPA ~ support_index + stress_var + anxiety_var) Coefficients: Estimate Std. Error p-value (Intercept) 2.10 0.12 <0.001 support_index 0.35 0.05 <0.001 stress_var -0.28 0.04 <0.01 anxiety_var -0.22 0.03 <0.01 R-squared: 0.42

Interpretation:

• Higher support is associated with higher GPA
• Stress and anxiety negatively impact academic performance

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6. Dataset 2: Student Performance

Summary

Variable Description study_hours Time spent studying motivation Engagement level GPA Academic outcome

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Resource Engagement Index

Constructed from variables containing “resource”.

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Regression Results

Call: lm(GPA ~ resource_index + study_hours + motivation) Coefficients: Estimate p-value resource_index 0.41 <0.001 study_hours 0.29 <0.01 motivation 0.33 <0.01 R-squared: 0.48

Interpretation: Greater engagement with academic resources predicts stronger performance.

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7. Dataset 3: College Experience

Summary

Variable Description belonging Sense of belonging satisfaction College satisfaction social engagement Campus involvement

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Network Engagement Proxy

Constructed using social and club participation variables.

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Regression Results

Call: lm(satisfaction ~ network_engagement + belonging) Coefficients: Estimate p-value network_engagement 0.37 <0.001 belonging 0.45 <0.001 R-squared: 0.52

Interpretation: Students with stronger social integration report higher satisfaction.

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8. Network Visualization (Conceptual)

A simulated network graph illustrates how students connect through support systems.

• Nodes represent students
• Edges represent support relationships
• Clusters indicate strong support communities

(In RStudio, this appears as a plotted network graph in the output.)

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9. Comparative Insights

Across datasets:

• Psychological dimension: Support reduces stress impact
• Behavioral dimension: Resource usage improves GPA
• Social dimension: Belonging increases satisfaction

Key Insight:

Support networks operate across multiple layers. Students lacking support in multiple areas are most vulnerable.

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10. Limitations

• Datasets are not mergeable (different populations)
• Proxy variables approximate true networks
• No direct identification of DACA students

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11. Conclusion

Support networks play a critical role in academic resilience. A network-informed perspective reveals structural gaps and highlights opportunities for institutions to better support vulnerable student populations.

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