Name: Artificial Intelligence for Student Admission and Placement in TVET: AI-Driven Field Recommendations for Incoming Students at Rwanda Polytechnic (en Inglés)
SKU: 69199211
Price: 254.99 PEN
Availability: InStock
Author: Etienne, Ntambara
ISBN: 9786630253306

portada Artificial Intelligence for Student Admission and Placement in TVET: AI-Driven Field Recommendations for Incoming Students at Rwanda Polytechnic (en Inglés)

Artificial Intelligence for Student Admission and Placement in TVET: AI-Driven Field Recommendations for Incoming Students at Rwanda Polytechnic (en Inglés)

Formato

Libro Físico

Editorial

GlobeEdit

Autor

Etienne, Ntambara

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

ISBN13

9786630253306

Artificial Intelligence for Student Admission and Placement in TVET: AI-Driven Field Recommendations for Incoming Students at Rwanda Polytechnic (en Inglés)

Etienne, Ntambara (Autor) · GlobeEdit · Tapa Blanda

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Reseña del libro "Artificial Intelligence for Student Admission and Placement in TVET: AI-Driven Field Recommendations for Incoming Students at Rwanda Polytechnic (en Inglés)"

This book presents a machine learning framework for placing students into suitable technical programs at Rwanda Polytechnic. Using 399 student records and 128 curriculum-aligned features from Rwanda's national examination results, Artificial Neural Network, XGBoost, and Random Forest models were trained and compared across 13 technical programs. The Artificial Neural Network achieved 94% testing accuracy with a minimal generalization gap. SHAP, LIME, permutation importance, and mutual-information analyses were used to explain predictions and identify influential features. The study also examines class imbalance, fairness, confidence-based human-AI decision routing, dimensionality, feature selection, robustness, and ensemble feature importance. Worked recommendation examples demonstrate practical application. A deployment blueprint aligned with available institutional hardware is also proposed. The framework offers researchers, administrators, and policymakers a transparent and practical approach to responsible AI-assisted student admission and placement in technical and vocational education.