THE IMPORTANCE OF REGIONALLY DISAGGREGATED SOCIO-DEMOGRAPHIC DATA IN ASSESSING THE HIGHER EDUCATION SYSTEM
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Objective: The article aims to evaluate the significance of regionally disaggregated socio-demographic data in assessing the performance and effectiveness of the higher education system, focusing on key socio-demographic factors such as population size, age composition, territorial distribution, and educational participation rates. Method: The study employs a combination of information-analytical methods and data analysis techniques, including regional differences in socio-demographic aspects, to assess higher education systems. Descriptive and comparative statistics are used to describe regional disparities, and systems analysis is applied to evaluate the socio-demographic factors. Results: The findings reveal that regional socio-demographic disparities significantly influence higher education demand, educational access, and system functioning. The study shows that regions with high youth populations face a high demand for education, often exceeding institutional capacity, while economically disadvantaged regions encounter financial constraints, impacting enrollment despite high youth populations. Novelty: This research introduces socio-demographic data as a central tool in evaluating higher education systems, challenging traditional evaluation methods that ignore regional disparities. The study highlights the need for a region-sensitive approach to educational policy formulation and system evaluation.
S. Aarthi, R. N. Ravikumar, K. Sobirova, and Z. Mamadiyarov, “Empowering a sustainable future through digital learning and the SDGs,” in Harnessing E-Learning to Create a Sustainable Future. Hershey, PA, USA: IGI Global Scientific Publishing, 2026, pp. 145–172.
Z. Adilov, J. Tajibaev, L. Rasul-Zade, M. Tursunov, Z. Mamadiyarov, and D. Abdullayev, “Exploring virtual reality and digital twin technologies for sustainable construction training in higher education,” in E3S Web of Conferences, vol. 680, p. 00131, 2025.
K. J. Arrow, “Higher education as a filter,” Journal of Public Economics, vol. 2, no. 3, pp. 193–216, 1973.
I. Cahyani, K. Buriev, M. Ngongo, Z. Mamadiyarov, L. Ino, H. Herman, N. Saputra, and S. Baxtishodovich, “Exploring the use of TikTok application in enhancing the skill of pronunciation: A case on students’ perception,” Studies in Media and Communication, vol. 13, no. 2, pp. 150–158, 2025, doi: 10.11114/smc.v13i2.7553.
J. S. Dill and F. Van Vught, “National innovation and the academic research enterprise,” Higher Education, vol. 40, no. 2, pp. 117–138, 2010.
B. Elov, A. Kholikov, I. Abdullayeva, Z. Mamadiyarov, and A. Ruzikulova, “Effectiveness of AI chatbots in promoting informal speaking proficiency and social pragmatic skills in Uzbekistan: A multi-analysis study,” Computer-Assisted Language Learning Electronic Journal, vol. 26, no. 7, pp. 137–161, 2025.
R. Florida, The Rise of the Creative Class: And How It’s Transforming Work, Leisure, Community and Everyday Life. New York, NY, USA: Basic Books, 2014.
E. A. Hanushek and L. Woessmann, “The role of cognitive skills in economic development,” Journal of Economic Literature, vol. 46, no. 3, pp. 607–668, 2008.
E. A. Hanushek and L. Woessmann, Education Quality and Economic Growth. Washington, DC, USA: World Bank, 2007.
J. J. Heckman, “Skill formation and the economics of investing in disadvantaged children,” Science, vol. 312, no. 5782, pp. 1900–1902, 2006.
Z. Mamadiyarov, A. Atajanova, E. Iskandarov, and M. Ahmad, “Exploring the future of education: A review of VR, AR, and XR applications,” in Critical Ethical and Societal Implications of the Metaverse. Hershey, PA, USA: IGI Global Scientific Publishing, 2026, pp. 75–108.
P. McCann, Urban and Regional Economics. Oxford, UK: Oxford University Press, 2013.
F. W. Notestein, “Population—The long view,” in Food for the World. Chicago, IL, USA: University of Chicago Press, 1945, pp. 36–57.
OECD, Education at a Glance 2022: OECD Indicators. Paris, France: OECD Publishing, 2022.
M. B. Panjaitan, A. F. Siagian, L. Judijanto, M. Mufarizuddin, H. Herman, N. Saputra, and Z. Mamadiyarov, “Comparison of students’ science literacy abilities using inquiry and cooperative learning models,” Aptisi Transactions on Technopreneurship, vol. 8, no. 1, pp. 63–73, 2025, doi: 10.34306/att.v8i1.640.
R. N. Ravikumar, S. Aarthi, P. K. Rai, and Z. Mamadiyarov, “Intelligent code analysis and feedback generation: A new paradigm in programming education,” in AI Applications in Instructional Education Strategies. Hershey, PA, USA: IGI Global Scientific Publishing, 2026, pp. 137–170.
R. N. Ravikumar, S. Aarthi, F. Ruzimova, and Z. Mamadiyarov, “Training and development in AI-driven education scaling solutions: K–12 to workforce,” in Transforming Education With Data Science in the AI Era. Hershey, PA, USA: IGI Global Scientific Publishing, 2026, pp. 267–298.
UNESCO, Global Education Monitoring Report 2021/22: Non-State Actors in Education. Paris, France: UNESCO Publishing, 2022.
UNESCO, Education for All: Global Monitoring Report. Paris, France: UNESCO Publishing, 2015.
World Bank, World Development Report 2018: Learning to Realize Education’s Promise. Washington, DC, USA: World Bank, 2018.
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