Clustering National Higher Education Systems Worldwide Using Sustainable Development Goals 4 Indicators and Self-Organizing Maps
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| Title: | Clustering National Higher Education Systems Worldwide Using Sustainable Development Goals 4 Indicators and Self-Organizing Maps |
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| Language: | English |
| Authors: | Felipe Guilherme Oliveira-Melo (ORCID |
| Source: | International Journal of Educational Management. 2026 40(1-2):268-301. |
| Availability: | Emerald Publishing Limited. Howard House, Wagon Lane, Bingley, West Yorkshire, BD16 1WA, UK. Tel: +44-1274-777700; Fax: +44-1274-785201; e-mail: emerald@emeraldinsight.com; Web site: http://www.emerald.com/insight |
| Peer Reviewed: | Y |
| Page Count: | 34 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Foreign Countries, Sustainable Development, Higher Education, Cluster Grouping, Foreign Policy, Educational Indicators, Comparative Education, Access to Education, Career and Technical Education, College Enrollment, Lifelong Learning |
| DOI: | 10.1108/IJEM-12-2024-0834 |
| ISSN: | 0951-354X 1758-6518 |
| Abstract: | Purpose: This study aims to assess the performance of national education systems worldwide through key indicators of Sustainable Development Goal 4 (SDG 4), focusing on higher education access, vocational training participation, and lifelong learning engagement. The study was guided by two research questions: (1) How are countries grouped according to their performance on SDG 4 Target 4.3 indicators? and (2) What patterns emerge from these groupings that can inform more effective international policy dialogues and interventions in higher education? Design/methodology/approach: A secondary data analysis was conducted using data from the UNESCO Institute for Statistics (UIS) of 71 countries, covering the period from 2022 to 2024. Three key SDG 4 Target 4.3 indicators, disaggregated by sex, were examined. Data were analyzed using a two-stage clustering approach, combining self-organizing maps and hierarchical clustering methods. Findings: The clustering results reveal four distinct groups of countries based on their performance on SDG 4 Target 4.3 indicators, highlighting the interactions between socioeconomic development, cultural factors, and gender dynamics. Countries in Cluster 1 exhibit strong lifelong learning policies and narrow gender gaps. In contrast, Clusters 2 and 3 show lower participation rates, pronounced gender disparities, and limited integration of vocational education. Cluster 4, characterized by balanced systems, presents narrower gaps in tertiary education but persistent male dominance in vocational training. These findings underscore the need for policy transfers from high-performing clusters, targeted reforms, and cultural shifts to promote equitable and inclusive educational outcomes. Originality/value: This study offers an original contribution by filling a key gap in the literature on SDG 4 monitoring. Empirically, it develops a cross-national typology of 71 countries using the most recent UIS data from 2022 to 2024, enabling a nuanced understanding of disparities in tertiary education, vocational training, and lifelong learning. Theoretically, it advances dialogue by integrating institutional theory and the capability approach to explain how institutional arrangements and capability expansion shape educational outcomes. Methodologically, it applies a two-stage clustering approach combining self-organizing maps with hierarchical clustering, which is a robust but still underutilized technique in global education policy research. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1503082 |
| Database: | ERIC |
| Abstract: | Purpose: This study aims to assess the performance of national education systems worldwide through key indicators of Sustainable Development Goal 4 (SDG 4), focusing on higher education access, vocational training participation, and lifelong learning engagement. The study was guided by two research questions: (1) How are countries grouped according to their performance on SDG 4 Target 4.3 indicators? and (2) What patterns emerge from these groupings that can inform more effective international policy dialogues and interventions in higher education? Design/methodology/approach: A secondary data analysis was conducted using data from the UNESCO Institute for Statistics (UIS) of 71 countries, covering the period from 2022 to 2024. Three key SDG 4 Target 4.3 indicators, disaggregated by sex, were examined. Data were analyzed using a two-stage clustering approach, combining self-organizing maps and hierarchical clustering methods. Findings: The clustering results reveal four distinct groups of countries based on their performance on SDG 4 Target 4.3 indicators, highlighting the interactions between socioeconomic development, cultural factors, and gender dynamics. Countries in Cluster 1 exhibit strong lifelong learning policies and narrow gender gaps. In contrast, Clusters 2 and 3 show lower participation rates, pronounced gender disparities, and limited integration of vocational education. Cluster 4, characterized by balanced systems, presents narrower gaps in tertiary education but persistent male dominance in vocational training. These findings underscore the need for policy transfers from high-performing clusters, targeted reforms, and cultural shifts to promote equitable and inclusive educational outcomes. Originality/value: This study offers an original contribution by filling a key gap in the literature on SDG 4 monitoring. Empirically, it develops a cross-national typology of 71 countries using the most recent UIS data from 2022 to 2024, enabling a nuanced understanding of disparities in tertiary education, vocational training, and lifelong learning. Theoretically, it advances dialogue by integrating institutional theory and the capability approach to explain how institutional arrangements and capability expansion shape educational outcomes. Methodologically, it applies a two-stage clustering approach combining self-organizing maps with hierarchical clustering, which is a robust but still underutilized technique in global education policy research. |
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| ISSN: | 0951-354X 1758-6518 |
| DOI: | 10.1108/IJEM-12-2024-0834 |
