Datasets:

electricsheepafrica
/

nigerian_education_out_of_school_children

child_id stringlengths 14 14	state stringclasses 37 values	age int64 6 17	gender stringclasses 2 values	reason stringclasses 7 values
CHILD-09010092	Kaduna	10	female	disability
CHILD-16754931	Nasarawa	14	female	distance_to_school
CHILD-07268469	Rivers	7	male	early_marriage
CHILD-05330354	Taraba	9	female	poverty
CHILD-04103863	Gombe	9	female	disability
CHILD-00643651	Cross River	8	male	poverty
CHILD-10311449	Borno	9	male	distance_to_school
CHILD-19686586	Katsina	17	male	early_marriage
CHILD-08124788	Gombe	13	male	nomadic
CHILD-01062653	Yobe	13	male	distance_to_school
CHILD-14577943	Osun	8	male	disability
CHILD-03074520	Osun	12	female	poverty
CHILD-02422883	Adamawa	11	male	early_marriage
CHILD-08953364	Ekiti	10	female	nomadic
CHILD-09823546	Oyo	16	male	poverty
CHILD-03095003	Edo	16	female	nomadic
CHILD-13985704	Borno	14	female	child_labor
CHILD-16160325	Ondo	7	female	poverty
CHILD-17643655	Edo	13	male	poverty
CHILD-13610145	Delta	16	female	disability
CHILD-17902735	Anambra	8	female	early_marriage
CHILD-02661154	Ebonyi	10	male	distance_to_school
CHILD-16820265	Ogun	6	male	child_labor
CHILD-14879637	Ekiti	17	female	nomadic
CHILD-01790509	Jigawa	15	male	poverty
CHILD-04432065	Katsina	6	female	nomadic
CHILD-10448939	Ogun	10	female	distance_to_school
CHILD-07485456	Plateau	17	male	poverty
CHILD-05132686	Delta	16	female	disability
CHILD-16765511	Kogi	10	male	disability
CHILD-06746016	Kaduna	16	female	disability
CHILD-19332258	Plateau	16	male	poverty
CHILD-13258961	Enugu	12	female	child_labor
CHILD-06213869	Benue	10	female	early_marriage
CHILD-00555141	Kwara	13	female	distance_to_school
CHILD-15266877	Edo	15	female	poverty
CHILD-07966477	Kwara	11	male	poverty
CHILD-01303792	Plateau	14	male	nomadic
CHILD-12410298	Kaduna	6	female	child_labor
CHILD-18564458	Oyo	9	male	disability
CHILD-03186004	Kaduna	17	female	child_labor
CHILD-15326533	Ebonyi	13	female	nomadic
CHILD-08381281	Nasarawa	12	male	distance_to_school
CHILD-04711879	Niger	15	male	disability
CHILD-06980351	Gombe	9	female	distance_to_school
CHILD-17211115	Benue	11	female	nomadic
CHILD-08697389	Kebbi	11	male	early_marriage
CHILD-02344695	Sokoto	17	female	poverty
CHILD-13853192	Katsina	12	female	disability
CHILD-09224635	Niger	13	female	nomadic
CHILD-05949375	Lagos	16	male	early_marriage
CHILD-09842214	Abia	12	female	distance_to_school
CHILD-11340628	Katsina	15	male	poverty
CHILD-14826429	Lagos	6	female	distance_to_school
CHILD-19273494	Gombe	7	male	early_marriage
CHILD-08464088	Bayelsa	14	male	disability
CHILD-19981629	Kogi	8	female	early_marriage
CHILD-01914042	Kogi	8	male	early_marriage
CHILD-19779702	Bayelsa	10	male	child_labor
CHILD-09185045	Katsina	9	female	distance_to_school
CHILD-07986598	Sokoto	9	male	disability
CHILD-17004332	Imo	15	male	poverty
CHILD-05342829	Ekiti	8	female	early_marriage
CHILD-06632347	FCT	16	male	disability
CHILD-04830624	Bauchi	7	male	child_labor
CHILD-17760943	Edo	9	female	disability
CHILD-11660851	Sokoto	7	female	child_labor
CHILD-17445296	Ondo	11	male	disability
CHILD-19923353	Niger	9	male	child_labor
CHILD-03683239	Zamfara	11	male	disability
CHILD-01013172	Cross River	17	male	child_labor
CHILD-03477336	Zamfara	9	male	nomadic
CHILD-05734195	FCT	14	male	nomadic
CHILD-14553098	Niger	7	male	child_labor
CHILD-10016102	Kebbi	9	male	nomadic
CHILD-16893121	Gombe	13	male	nomadic
CHILD-02740062	Anambra	10	male	early_marriage
CHILD-19787616	Lagos	13	male	poverty
CHILD-01859883	Nasarawa	9	female	child_labor
CHILD-03785126	Jigawa	8	male	poverty
CHILD-16174390	Gombe	16	male	disability
CHILD-14301267	Rivers	6	male	poverty
CHILD-00954074	Ogun	14	male	early_marriage
CHILD-03501635	Kebbi	15	female	child_labor
CHILD-19975154	Nasarawa	9	female	poverty
CHILD-15815883	Sokoto	6	male	nomadic
CHILD-14672374	Edo	7	male	disability
CHILD-02311734	Taraba	6	male	disability
CHILD-11615948	FCT	8	female	early_marriage
CHILD-02520994	Zamfara	12	male	almajiri_system
CHILD-06589916	Yobe	11	female	child_labor
CHILD-06068364	Imo	8	male	poverty
CHILD-04446123	Katsina	6	male	early_marriage
CHILD-04444673	Osun	6	female	nomadic
CHILD-09591014	Abia	16	female	distance_to_school
CHILD-04526600	Osun	15	female	early_marriage
CHILD-15255863	Osun	8	female	early_marriage
CHILD-08128159	Gombe	7	female	early_marriage
CHILD-07771442	Osun	15	male	nomadic
CHILD-03592679	Abia	14	male	distance_to_school

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Nigeria Education – Out-of-School Children

Dataset Description

Children not enrolled: Almajiri, nomadic, poverty, conflict zones.

Category: Enrollment & Attendance
Rows: 300,000
Format: CSV, Parquet
License: MIT
Synthetic: Yes (generated using reference data from WAEC, JAMB, UBEC, NBS, UNESCO)

Dataset Structure

Schema

child_id: string
state: string
age: integer
gender: string
reason: string

Sample Data

| child_id       | state    |   age | gender   | reason             |
|:---------------|:---------|------:|:---------|:-------------------|
| CHILD-09010092 | Kaduna   |    10 | female   | disability         |
| CHILD-16754931 | Nasarawa |    14 | female   | distance_to_school |
| CHILD-07268469 | Rivers   |     7 | male     | early_marriage     |
| CHILD-05330354 | Taraba   |     9 | female   | poverty            |
| CHILD-04103863 | Gombe    |     9 | female   | disability         |

Data Generation Methodology

This dataset was synthetically generated using:

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
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
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 – Out-of-School Children},
  author = {Electric Sheep Africa},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/electricsheepafrica/nigerian_education_out_of_school_children}
}