electricsheepafrica/africa-synth-education-teacher-absenteeism-nigeria

⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

Nigeria Education – Teacher Absenteeism

Dataset Description

Synthetic Teachers & Workforce data for Nigeria education sector.

Category: Teachers & Workforce
Rows: 140,000
Format: CSV, Parquet
License: MIT
Synthetic: Yes (generated using reference data from WAEC, JAMB, UBEC, NBS, UNESCO)

Dataset Structure

Schema

• id: string
• date: string
• state: string
• value: float
• category: string

Sample Data

| id           | date       | state   |   value | category   |
|:-------------|:-----------|:--------|--------:|:-----------|
| REC-00434236 | 2024-03-09 | Kaduna  |    41.5 | C          |
| REC-00499556 | 2023-12-21 | Bauchi  |    78.8 | A          |
| REC-00690289 | 2025-03-09 | Kaduna  |    57.3 | A          |
| REC-00217517 | 2022-10-17 | Ondo    |    60.7 | A          |
| REC-00076339 | 2022-06-07 | Ekiti   |    47.5 | A          |

Data Generation Methodology

This dataset was synthetically generated using:

1. Reference Sources:

   • WAEC (West African Examinations Council) - exam results, pass rates, grade distributions
   • JAMB (Joint Admissions and Matriculation Board) - UTME scores, subject combinations
   • UBEC (Universal Basic Education Commission) - enrollment, infrastructure, teacher data
   • NBS (National Bureau of Statistics) - education surveys, literacy rates
   • UNESCO - Nigeria education statistics, enrollment ratios
   • UNICEF - Out-of-school children, gender parity indices

2. Domain Constraints:

   • WAEC grading system (A1-F9) with official score ranges
   • JAMB UTME scoring (0-400 points, 4 subjects)
   • Nigerian curriculum structure (Primary, JSS, SSS)
   • Academic calendar (3 terms: Sep-Dec, Jan-Apr, May-Jul)
   • Regional disparities (North-South education gap)
   • Gender parity indices by region and level

3. Quality Assurance:

   • Distribution testing (WAEC grade distributions match national patterns)
   • Correlation validation (attendance-performance, teacher quality-outcomes)
   • Causal consistency (educational outcome models)
   • Multi-scale coherence (student → school → state aggregations)
   • Ethical considerations (representative, unbiased, privacy-preserving)

See QUALITY_ASSURANCE.md in the repository for full methodology.

Use Cases

• Machine Learning: Performance prediction, dropout forecasting, admission modeling, resource allocation
• Policy Analysis: Education program evaluation, gender parity assessment, regional disparity studies
• Research: Teacher effectiveness, infrastructure impact, exam performance patterns
• Education Planning: School placement, teacher deployment, budget allocation

Limitations

• Synthetic data: While grounded in real distributions from WAEC/JAMB/UBEC, individual records are not real observations
• Simplified dynamics: Some complex interactions (e.g., peer effects, teacher-student matching) are simplified
• Temporal scope: Covers 2022-2025; may not reflect longer-term trends or future policy changes
• Spatial resolution: State/LGA level; does not capture micro-level heterogeneity within localities

Citation

If you use this dataset, please cite:

@dataset{nigeria_education_2025,
  title = {Nigeria Education – Teacher Absenteeism},
  author = {Electric Sheep Africa},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/electricsheepafrica/nigerian_education_teacher_absenteeism}
}

Related Datasets

This dataset is part of the Nigeria Education Sector collection:

• https://huggingface.co/collections/electricsheepafrica/nigeria-education-sector

Contact

For questions, feedback, or collaboration:

• Organization: Electric Sheep Africa
• Collection: Nigeria Education Sector
• Repository: https://github.com/electricsheepafrica/nigerian-datasets

Changelog

Version 1.0.0 (October 2025)

• Initial release
• 140,000 synthetic records
• Quality-assured using WAEC/JAMB/UBEC/NBS reference data