Statistical and Machine Learning Approach for Robust Assessment Modelling of Out-of-School Children - Global Perspective

Aug 5, 2026

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Out-of-school children face serious negative consequences across basic and secondary education, affecting students, families, and society. To support evidence-based policy and research, this paper proposes a hybrid machine learning approach combining least squares and support vector machine (LS-SVM) to improve robust prediction. Using UNESCO-compiled global out-of-school datasets from 1975–2020, the method is evaluated against standard SVM using NRMSE and MAPE. Results show LS-SVM achieves lower error and stronger precision, providing guidance for effective global trend assessment and forecasting.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Abstract

Introduction

Education outcomes and societal impacts

Definition and vulnerability of out-of-school children (OOSC)

Global prevalence and regional distribution

Research objective and proposed hybrid approach

Statistical and Machine Learning Approach for Robust Assessment Modelling of Out-of-School Children Rate: Global Perspective Edith Edimo Joseph* Department of Educational Psychology, University of KwaZulu-Natal, Edgewood Campus, South Africa E-mail: [edimo8383@gmail.com](edimo8383@gmail.com) ORCID iD: [https://orcid.org/0000-0001-6148-9127](https://orcid.org/0000-0001-6148-9127) *Corresponding Author Joseph Isabona Department of Physics, Federal University Lokoja, Lokoja, Kogi State, Nigeria E-mail: [joseph.isabona@fulokoja.edu.ng](joseph.isabona@fulokoja.edu.ng) ORCID iD: [https://orcid.org/0000-0002-2606-4315](https://orcid.org/0000-0002-2606-4315) Sunday Dare Department of Educational Psychology, University of KwaZulu-Natal, Edgewood Campus, South Africa E-mail: [adesojisunday@gmail.com](adesojisunday@gmail.com) Odaro Osayande Centre for Learning Resources, Covenant University, Ota, Ogun State, Nigeria E-mail: [odaro.osayande@covenantuniversity.edu.ng](odaro.osayande@covenantuniversity.edu.ng) ORCID iD: [https://orcid.org/0000-0002-5771-9548](https://orcid.org/0000-0002-5771-9548) Okiemute Roberts Omasheye Delta State College of Education, Mosogar, Nigeria E-mail: [okiemuteomasheye@yahoo.com](okiemuteomasheye@yahoo.com) Received: 14 November 2022; Revised: 04 January 2023; Accepted: 11 March 2023; Published: 08 June 2023 Abstract: The negative impact of out-of-school students' problems at the basic and high-school levels is always very weighty on the affected individuals, parents, and society at large. Owing to the weighty negative consequences, policymakers, different government agencies, educators and researchers have long been looking for how to effectively study and forecast the trends as a means of offering a concrete solution to the problem. This paper develops a better hybrid machine learning method, which combines the least square and support vector machine (LS-SVM) model for robust prediction improvement of out-of-school children trend patterns. Particularly, while other previous works only engaged some regional and few samples of out-of-school datasets, this paper focused on long-ranged global out-of-school datasets, collated by UNESCO between 1975-2020. The proposed hybrid method exhibits the optimal precision accuracies with the LS-SVM model in comparison with ones made using the ordinary SVM model. The precision performance of both LS-SVM and SVM was quantified and a lower NRMSE value is preferred. From the results, the LS-SVM attained lower error values of 0.0164, 0.0221, 0.0268, 0.0209, 0.0158, 0.0201, 0.0147 and 0.0095 0.0188, compared to the SVM model that attained higher NRMSE values of 0.041, ,0.0628, 0.0381, 0.0490, 0.0501, 0.0493, 0.0514, 0.0617 and 0.0646, respectively. By engaging the MAPE indicator, which expresses the mean disconnection between the sourced and predicted values of the out-of-school data. By means of the MAPE, LS-SVM attained lower error values of 0.51, 1.88, 0.82, 2.38, 0.62, 2.55, 0.60, 0.60, 1.63 while SVM attained 1.83, 7.39, 1.79 7.01, 2.43, 8.79, 2.58, 4.13, 6.18. This implies that the LS-SVM model has better precision performance than the SVM model. The results attained in this work can serve as an excellent guide on how to explore hybrid machine-learning techniques to effectively study and predict out-of-school students among researchers and educators. Index Terms: Global Perspective, LS-SVM, Out-of-School Children, SVM, UNESCO. 1. Introduction Proper education remained a better means of boosting individual well-being, fighting poverty, combating crime, reducing social vices, fostering peace, strengthening egalitarianism, sustaining the environment, and ensuring gender parity in society. Proper, education can also prepare and enhance learners' ability to obtain the right attitude and acquire the skills they need, including sense of purpose in order to improve their individual lives. Generally, children who had non-formal education

FAQs

What hybrid machine learning method does the paper propose for out-of-school trend prediction?

The paper develops a hybrid LS-SVM approach that combines least squares with a support vector machine model to improve robust prediction accuracy.

Which dataset and time range are used to model global out-of-school children trends?

The study uses long-range global out-of-school datasets compiled by UNESCO covering 1975–2020.

How do the LS-SVM and SVM models compare in predictive performance?

The results indicate LS-SVM attains lower NRMSE and lower MAPE errors than the standard SVM model, showing better precision and robustness.

Aug 5, 2026

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