Mustafe Khadar Abdi
Biographic Data
| ID | 4428884 |
|---|---|
| NAME | Mustafe Khadar Abdi |
| GIVEN NAMES | Mustafe Khadar |
| FAMILY NAME | Abdi |
| SIGNATURE | ABDI M K |
| AFFILIATIONS | Amoud University |
| ORCID | 0009-0008-7345-910X |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Beyond the average: A machine learning typology for differentiated foundational learning in Somaliland
Geographic and school-level disparities as primary predictors of numeracy skills: A supervised machine learning approach of Somaliland's national learning assessment
This study investigates the primary drivers of numeracy skill disparities among Grade 3 students in Somaliland by applying a supervised machine learning approach to a national dataset of 5834 students from the 2022 Early Grade Mathematics Assessment. To address the severe class imbalance in student outcomes (91.5 % pass rate), the Synthetic Minority Over-sampling Technique (SMOTE) was implemented within a robust, 10-fold grouped cross-validation …
Geography of opportunity: A multilevel analysis of regional and school-level inequities in Somaliland’s educational outcomes
Persistent regional and school-level inequities continue to shape students’ educational outcomes in Somaliland. Despite policy advances, empirical evidence quantifying how much variation in academic achievement is attributable to individual, school, and regional factors remains limited. This study applies the geography of opportunity framework to investigate how location and institutional context influence student performance across Somaliland’s …
Farming, displacement, and education: A multilevel analysis of school attendance among Somali children using national survey data
No prominent works on this page.
Geographic and school-level disparities as primary predictors of numeracy skills: A supervised machine learning approach of Somaliland's national learning assessment
This study investigates the primary drivers of numeracy skill disparities among Grade 3 students in Somaliland by applying a supervised machine learning approach to a national dataset of 5834 students from the 2022 Early Grade Mathematics Assessment. To address the severe class imbalance in student outcomes (91.5 % pass rate), the Synthetic Minority Over-sampling Technique (SMOTE) was implemented within a robust, 10-fold grouped cross-validation …
Geography of opportunity: A multilevel analysis of regional and school-level inequities in Somaliland’s educational outcomes
Persistent regional and school-level inequities continue to shape students’ educational outcomes in Somaliland. Despite policy advances, empirical evidence quantifying how much variation in academic achievement is attributable to individual, school, and regional factors remains limited. This study applies the geography of opportunity framework to investigate how location and institutional context influence student performance across Somaliland’s …
Farming, displacement, and education: A multilevel analysis of school attendance among Somali children using national survey data
Beyond the average: A machine learning typology for differentiated foundational learning in Somaliland
Cognitive and developmental aspects of mathematical skills (2 works) · Multilevel model (2 works) · Numeracy (2 works) · Psychometric Methodologies and Testing (2 works) · School Choice and Performance (2 works) · Agricultural Innovations and Practices (1 works) · Agricultural risk and resilience (1 works) · Attendance (1 works) · Certification (1 works) · Early Childhood Education and Development (1 works)