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predictive-analytics-model-for-early-detection-of-at-risk-undergraduate-students-2026-011

Uche UsmanUFTAN UNIVERSITYGuidance and Counselling2026

Abstract

This study proposes a predictive analytics model for identifying undergraduate students at risk of delayed academic progression. It uses academic records, attendance indicators, and supervised learning techniques to support timely intervention while emphasizing privacy, fairness, and institutional accountability.

Metadata

Author
Uche Usman
Supervisor
Dr. Aminu Y. Garba
Faculty
Education
Department
Guidance and Counselling
Document type
Masters Thesis
Year
2026
Keywords
predictive analytics, student success, machine learning, academic intervention, data privacy
Access
Restricted
License
all_rights_reserved

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