What Helps Afghan Girls Succeed in Online College?

A data-driven study on online learning effectiveness for Afghan women in higher education

This research project investigates the factors that enable Afghan girls to succeed in online university programs. Through an anonymous survey of 322 students, we examined infrastructure challenges, teaching quality, safety concerns, and support systems to provide evidence-based recommendations for improving online higher education access.

Figure 1: Survey demographics and key outcome metrics

1. Which conditions most strongly influence satisfaction and persistence in online learning?
2. How do infrastructure constraints limit the effectiveness of pedagogical practices?
3. Do safety and family support buffer against connectivity challenges?

Afghanistan is the only country in the world banning girls from secondary and women from higher education (UNESCO, 2024). Approximately 2.2 million girls remain out of school, making online learning a critical lifeline for those seeking education.

• September 29-30: Nationwide internet and telecom blackout

These conditions make understanding what helps students persist despite challenges critically important.

Our research draws on four established frameworks to understand online learning success:

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│   Theoretical   │     │  Survey Design  │     │   Actionable    │
│   Frameworks    │ ──► │   (25 items)    │ ──► │   Insights      │
│   CoI, SDT,     │     │  Infrastructure │     │  for improving  │
│   Tinto, TAM    │     │  Pedagogy, Safety│    │  online learning│
└─────────────────┘     └─────────────────┘     └─────────────────┘

📁 View Full Research Background →

Students face significant connectivity and power issues, especially inside Afghanistan.

Figure 2: Internet and electricity reliability gaps by location

• Internet Reliability: 2.61/5.0 (below neutral)
• Electricity Reliability: 2.61/5.0 (below neutral)
• Implication: Offline materials and flexible deadlines are critical

Teaching quality is the strongest driver of student satisfaction.

Figure 3: Teaching quality ratings across different dimensions

Nearly half of students face fear or lack adequate family support.

Figure 4: Support and safety levels across key dimensions

• Can Study Without Fear: 42.8%
• Have Family Support: 52.8%
• Feel Safe Online: Higher digital security perception

Logistic regression reveals the strongest factors influencing student continuation.

Figure 5: Top predictors of student persistence (standardized coefficients)

Top Positive Predictors:

1. 🏆 Instructor feedback quality
2. 👨‍👩‍👧 Family support
3. 📝 Assignment clarity
4. 🏠 Ability to study without fear
5. 📖 Materials access

Figure 6: Five key recommendation areas based on our findings

Figure 7: Strong correlation between satisfaction and intention to continue

📁 View Complete Analysis Details →

Based on our analysis, we propose five evidence-based strategies for improving online learning outcomes:

• Descriptive Statistics — Sample characteristics and variable distributions
• Correlation Analysis — Relationships between factors and outcomes
• Logistic Regression — Predictors of student persistence
• K-Means Clustering — Student segmentation for targeted interventions
• Thematic Coding — Qualitative analysis of open-ended responses

Raw Survey → PII Masking → Farsi→English Translation → Data Cleaning → Analysis

📁 View Data Dictionary →

• 📊 Longitudinal tracking — Follow students over multiple terms to measure actual persistence
• 🌍 Multi-institution study — Compare across different online programs
• 🔍 Qualitative deep-dives — In-depth interviews on safety and family dynamics
• 🧪 Intervention testing — Pilot and evaluate specific improvements
• 📱 Technology solutions — Develop and test offline-first learning tools

├── Data/
│   ├── Raw_data/          # Original survey responses (excluded from repo)
│   └── Processed_data/    # Cleaned, anonymized datasets
├── notebooks/             # Data cleaning and preparation scripts
├── Research_BG/           # Literature review and theoretical framework
├── results/               # Analysis notebooks and visualizations
│   ├── 01_exploratory_data_analysis.ipynb
│   ├── 02_infrastructure_analysis.ipynb
│   ├── 03_pedagogical_effectiveness.ipynb
│   ├── 04_safety_support_analysis.ipynb
│   ├── 05_persistence_modeling.ipynb
│   └── 08_final_visualizations.ipynb
└── collabration/          # Team documentation and retrospectives

• Python 3.8+
• Jupyter Lab/Notebook

1. Clone the repository:

git clone https://github.com/MIT-Emerging-Talent/elo2-afghan-girls-online-learning.git
cd elo2-afghan-girls-online-learning

2. Create a virtual environment and install dependencies:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\Activate.ps1
pip install -r requirements.txt

3. Launch Jupyter Lab:

jupyter lab

4. Start with the notebooks in the results/ folder to explore the analysis.

This research prioritizes participant safety and confidentiality:

• ✅ All responses are anonymous (no names, emails, or identifiable information)
• ✅ PII masking applied to open-ended responses
• ✅ Data stored securely with restricted access
• ✅ Farsi-to-English translation for accessibility
• ✅ Compliance with ethical research standards for vulnerable populations

This is an academic research project completed as part of the MIT Emerging Talent program. While the analysis is complete, we welcome:

• Issues or questions about the methodology
• Suggestions for additional analyses
• Discussions about similar research contexts

Madiha Malikzada 💻 🔗 Research · Theory Mapping · Website

Mahdia Ahmadi 💻 🔗 Repo Setup · Analysis · Documentation

Safiya Hashimi 💻 🔗 Data Cleaning · Documentation · Presentation

Madiha Malikzada — Led data collection, background research, and the mapping of CoI, SDT, Tinto, and TAM/UTAUT frameworks to survey design. Masked identifiable information in open-text responses, translated Farsi/Dari inputs, contributed to analysis and documentation, and built the project website.

Mahdia Ahmadi — Led the technical implementation of the project. Initialized and structured the repository, developed the complete analysis pipeline including exploratory data analysis, infrastructure analysis, pedagogical effectiveness assessment, safety & support analysis, persistence modeling (logistic regression), student segmentation (k-means clustering), and thematic coding of qualitative responses. Created publication-ready visualizations, organized research outputs, and contributed extensively to documentation.

Safiya Hashimi — Cleaned and prepared the dataset for analysis, contributed to documentation, and helped present results from the analysis.

Program: MIT Emerging Talent — ELO2 Cohort
Timeline: October – December 2025

🌍 Website	📊 Presentation
Explore our interactive research findings	View our final presentation slides

We extend our gratitude to:

• The 322 Afghan women who participated in this survey, sharing their experiences to help improve online education
• MIT Emerging Talent Program for providing the framework and support for this research
• Our mentors and reviewers for guidance throughout the project
• The online university that facilitated survey distribution

This project is licensed under the MIT License - see the LICENSE file for details.

If you use this research or data in your work, please cite:

@misc{malik2025afghan,
  author = {Malik, Madiha and Ahmadi, Mahdia and Hashimi, Safiya},
  title = {What Helps Afghan Girls Succeed in Online College?},
  year = {2025},
  publisher = {GitHub},
  journal = {GitHub Repository},
  howpublished = {\url{https://github.com/MIT-Emerging-Talent/elo2-afghan-girls-online-learning}},
  note = {MIT Emerging Talent Program}
}

Or in plain text:

Malik, M., Ahmadi, M., & Hashimi, S. (2025). What Helps Afghan Girls Succeed in Online College? MIT Emerging Talent Program. https://github.com/MIT-Emerging-Talent/elo2-afghan-girls-online-learning