Revolutionizing Nursing and Midwifery Informatics Curriculum Evaluation in Ghana: A Data-Driven Machine Learning Approach

Iven Aabaah; Japheth Kodua Wiredu; Bakaweri Emmanuel Batowise; Nelson Abuba Seidu

doi:10.51519/journalisi.v7i1.1018

Iven Aabaah C. K. Tedam University of Technology and Applied Sciences, Ghana
Japheth Kodua Wiredu Regentropfen University College, Ghana https://orcid.org/0009-0008-0313-5011
Bakaweri Emmanuel Batowise C. K. Tedam University of Technology and Applied Sciences, Ghana
Nelson Abuba Seidu Regentropfen University College, Ghana

Keywords: Nursing and Midwifery Informatics, Machine Learning in Education, Academic Performance Prediction, Digital Health Education, Curriculum and Student Engagement Analysis

Abstract

The field of Nursing and Midwifery Informatics (NMI) aims to equip healthcare professionals with the skills to efficiently use emerging technologies in their practice. This research assessed NMI educational programs in Ghana using machine learning techniques to analyze key factors influencing student performance, engagement, and satisfaction. Data was gathered from 1,500 students across C.K. Tedam University of Technology and Applied Sciences, Bolgatanga Nursing and Midwifery Training College, Regentropfen University College, Tamale Nursing and Midwifery Training College, and University for Development Studies. The study employed Random Forest, Gradient Boosting, Support Vector Machine, K-Nearest Neighbor, and Logistic Regression algorithms, evaluated using standard performance metrics, including accuracy, precision, and recall. The Gradient Boosting model achieved the highest predictive accuracy at 95%, identifying student engagement and curriculum satisfaction as the most influential predictors of academic success. Additionally, multiple regression analysis revealed that institutional differences significantly influenced academic outcomes, with students at Tamale Nursing and Midwifery Training College outperforming their counterparts at C.K. Tedam University of Technology and Applied Sciences (β = 3.85, p = 0.021), likely due to better alignment between their curriculum and instructional methods. These findings offer actionable insights for curriculum development and healthcare policy planning in resource-constrained settings, advocating for the integration of machine learning tools into academic evaluations. The study presents a scalable predictive model that can be adapted to enhance digital health education in similar low-resource settings worldwide, offering a pathway to more effective and inclusive healthcare education systems.

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