Volume 7 • Issue 2 • PP: 38–42 • 2024
Predictive Modeling of Global Educational Outcomes: A Comparative Analysis Using Machine Learning Regression Techniques
Open Access & Copyright
© 2024 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
View PDF Abstract
Education is central to social and economic development, making school enrollment and the quality of learning important global priorities. This study applies machine-learning regression to the World Educational Data dataset from Kaggle. After data exploration and preprocessing, eleven models are compared: Support Vector Regression (SVR), CatBoost, Random Forest, Extra Trees, XGBoost, a multilayer perceptron (MLP), Gradient Boosting, Decision Tree, K-Nearest Neighbors, Linear Regression, and a pipeline model. Performance is evaluated using MSE, RMSE, MAE, MBE, the correlation coefficient, R2, NSE, and WI. CatBoost reports the strongest correlation and coefficient of determination (both approximately 0.99999) with an MSE of 0.04024. The results illustrate the potential of machine-learning methods to support analysis and prediction of educational outcomes and to inform educational planning.
Keywords
References
[1] M. Ahmad, Z. Ahmed, X. Yang, N. Hussain, and A. Sinha, “Financial development and environmental degradation: Do human capital and institutional quality make a difference?” Gondwana Research, vol. 105, pp. 299–310, 2022.
[2] M. K. Anser, M. Ahmad, M. A. Khan, K. Zaman, A. A. Nassani, S. E. Askar, M. M. Q. Abro, and A. Kabbani, “The role of information and communication technolo-gies in mitigating carbon emissions: Evidence from panel quantile regression,” Environmental Science and Pollution Research, vol. 28, no. 17, pp. 21 065–21 084, 2021.
[3] M. Azam, L. Liu, and N. Ahmad, “Impact of institu-tional quality on environment and energy consumption: Evidence from developing world,” Environment, De-velopment and Sustainability, vol. 23, no. 2, pp. 1646–1667, 2021.
[4] R. Banerjee, V. Mishra, and A. A. Maruta, “Energy poverty, health and education outcomes: Evidence from the developing world,” Energy Economics, vol. 101, p. 105447, 2021.
[5] C. Cheng, X. Ren, K. Dong, X. Dong, and Z. Wang, “How does technological innovation mitigate CO2 emis-sions in OECD countries? heterogeneous analysis using panel quantile regression,” Journal of Environmental Management, vol. 280, p. 111818, 2021.
[6] H.-A. H. Dang and T.-A. Trinh, “Does the COVID-19 lockdown improve global air quality? new cross-national evidence on its unintended consequences,”Journal of Environmental Economics and Management, vol. 105, p. 102401, 2021.
[7] F. Díez, A. Villa, A. L. López, and I. Iraurgi, “Impact of quality management systems in the performance of edu-cational centers: Educational policies and management processes,” Heliyon, vol. 6, no. 4, p. e03824, 2020.
[8] H. Doreswamy, K. S., Y. Km., and I. Gad, “Forecasting air pollution particulate matter (PM2.5) using machine learning regression models,” Procedia Computer Sci-ence, vol. 171, pp. 2057–2066, 2020.
[9] D. Filmer, H. Rogers, N. Angrist, and S. Sabarwal, “Learning-adjusted years of schooling (LAYS): Defin-ing a new macro measure of education,” Economics of Education Review, vol. 77, p. 101971, 2020.
[10] F. Habibi and M. A. Zabardast, “Digitalization, edu-cation and economic growth: A comparative analysis of middle east and OECD countries,” Technology in Society, vol. 63, p. 101370, 2020.
[11] M. Injadat, A. Moubayed, A. B. Nassif, and A. Shami, “Systematic ensemble model selection approach for edu-cational data mining,” Knowledge-Based Systems, vol. 200, p. 105992, 2020.
[12] M. S. I. Khan, N. Islam, J. Uddin, S. Islam, and M. K. Nasir, “Water quality prediction and classification based on principal component regression and gradient boost-ing classifier approach,” Journal of King Saud Univer-sity - Computer and Information Sciences, vol. 34, no. 8, Part A, pp. 4773–4781, 2022.
[13] K. H. Lee, H. Xu, and B. Wu, “Gender differences in quality of life among community-dwelling older adults in low- and middle-income countries: Results from the study on global ageing and adult health (SAGE),” BMC Public Health, vol. 20, no. 1, p. 114, 2020.
[14] H. Liu, M. Alharthi, A. Atil, M. W. Zafar, and I. Khan, “A non-linear analysis of the impacts of natural resources and education on environmental quality: Green energy and its role in the future,” Resources Policy, vol. 79, p. 102940, 2022.
[15] M. K. Mahalik, H. Mallick, and H. Padhan, “Do educa-tional levels influence the environmental quality? the role of renewable and non-renewable energy demand in selected BRICS countries with a new policy perspec-tive,” Renewable Energy, vol. 164, pp. 419–432, 2021.
[16] A. A. Maruta, R. Banerjee, and T. Cavoli, “Foreign aid, institutional quality and economic growth: Evi-dence from the developing world,” Economic Modelling, vol. 89, pp. 444–463, 2020.
[17] U. Mehmood, “Contribution of renewable energy to-wards environmental quality: The role of education to achieve sustainable development goals in G11 coun-tries,” Renewable Energy, vol. 178, pp. 600–607, 2021.
[18] V. Raghupathi and W. Raghupathi, “The influence of education on health: An empirical assessment of OECD countries for the period 1995–2015,” Archives of Public Health, vol. 78, no. 1, p. 20, 2020.
[19] G. Santos, C. S. Marques, E. Justino, and L. Mendes, “Understanding social responsibility’s influence on ser-vice quality and student satisfaction in higher education,” Journal of Cleaner Production, vol. 256, p. 120597, 2020.
[20] M. W. Zafar, M. Shahbaz, A. Sinha, T. Sengupta, and Q. Qin, “How renewable energy consumption contribute to environmental quality? the role of education in OECD countries,” Journal of Cleaner Production, vol. 268, p. 122149, 2020.
[21] M. W. Zafar, A. Sinha, Z. Ahmed, Q. Qin, and S. A. H. Zaidi, “Effects of biomass energy consumption on en-vironmental quality: The role of education and tech-nology in asia-pacific economic cooperation countries,” Renewable and Sustainable Energy Reviews, vol. 142, p. 110868, 2021.
[22] C. Zhang, I. Khan, V. Dagar, A. Saeed, and M. W. Zafar, “Environmental impact of information and communi-cation technology: Unveiling the role of education in developing countries,” Technological Forecasting and Social Change, vol. 178, p. 121570, 2022.
Cite This Article
Choose your preferred format
Publisher's Note
The statements, opinions, and data presented in this article are solely those of the author(s) and do not necessarily represent those of ASPG, the journal, or its editors. ASPG and the editors disclaim responsibility for any harm arising from the use of any ideas, methods, instructions, or products described in this article, to the fullest extent permitted by applicable law.