Early detection of at-risk health sciences students: a machine learning-based predictive study using midterm grades
Reem A. M. Al Hashmi, Ilhan Ozturk, Hussein M. Elmehdi · 2025
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Description
BackgroundEarly identification of students at academic risk is critical in health sciences education, particularly in regions prioritizing healthcare workforce development. This study evaluated the application of established machine learning (ML) classifiers as a regional case study in the United Arab Emirates (UAE), using first-term midterm performance as early, formative assessment indicators of academic integration and student persistence.
Catalog record
| CREATOR | Reem A. M. Al Hashmi, Ilhan Ozturk, Hussein M. Elmehdi |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 55 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |