Clustering of countries according to programme for international student assessment (PISA) scores

Abstract

This study aims to cluster 65 countries based on PISA results. In the study, PISA results (Science-Mathematics-Reading) published by OECD in 2015 and 2018 were used. The main purpose of the analysis is to apply cluster analysis using a multivariate data structure to identify similarities and differences in education systems between countries. In this analysis, the k-means method and the hierarchical clustering algorithm were used to group countries into specific groups, so that countries with similar educational performance were included in the same cluster. In addition, Dunn, Connectivity and Silhouette indexes were used to increase the reliability of the analysis and to determine the optimal number of clusters. According to the validation indexes, k-means method with k=2 was used for 2015 PISA scores while hierarchical clustering algorithm with k=2 was used for 2018 PISA scores. In 2015, Turkey was the only country that changed clusters between the countries clustered according to their PISA scores and the countries clustered according to their PISA scores in 2018, and the reasons for this change were discussed. It is also observed that Turkey was in Cluster-1 in 2015, which includes countries with lower performance, and in Cluster-2 in 2018, which includes countries with higher performance. The clustering methods and indexes used provide a more robust and informed interpretation of the results obtained and make an important contribution to understanding the education systems of countries based on PISA results and grouping countries with similar performance.