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PISA data clusters reveal student and school inequality that affects results

The data from the PISA survey show that student performance correlates with socio-economic background, that private schools have higher results and more privileged students, and that this varies between countries. We explore this further and analyze the PISA data using methods from network theory an...

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Autor principal: Neuman, Magnus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9094565/
https://www.ncbi.nlm.nih.gov/pubmed/35544465
http://dx.doi.org/10.1371/journal.pone.0267040
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author Neuman, Magnus
author_facet Neuman, Magnus
author_sort Neuman, Magnus
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description The data from the PISA survey show that student performance correlates with socio-economic background, that private schools have higher results and more privileged students, and that this varies between countries. We explore this further and analyze the PISA data using methods from network theory and find clusters of countries whose students have similar performance and socio-economic background. Interestingly, we find a cluster of countries, including China, Spain and Portugal, characterized by less privileged students performing well. When considering private schools only, some countries, such as Portugal and Brazil, are in a cluster with mostly wealthy countries characterized by privileged students. Swedish grades are compared to PISA results, and we see that the higher grades in private schools are in line with the PISA results, suggesting that there is no grade inflation in this case, but differences in socio-economic background suggest that this is due to school segregation.
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spelling pubmed-90945652022-05-12 PISA data clusters reveal student and school inequality that affects results Neuman, Magnus PLoS One Research Article The data from the PISA survey show that student performance correlates with socio-economic background, that private schools have higher results and more privileged students, and that this varies between countries. We explore this further and analyze the PISA data using methods from network theory and find clusters of countries whose students have similar performance and socio-economic background. Interestingly, we find a cluster of countries, including China, Spain and Portugal, characterized by less privileged students performing well. When considering private schools only, some countries, such as Portugal and Brazil, are in a cluster with mostly wealthy countries characterized by privileged students. Swedish grades are compared to PISA results, and we see that the higher grades in private schools are in line with the PISA results, suggesting that there is no grade inflation in this case, but differences in socio-economic background suggest that this is due to school segregation. Public Library of Science 2022-05-11 /pmc/articles/PMC9094565/ /pubmed/35544465 http://dx.doi.org/10.1371/journal.pone.0267040 Text en © 2022 Magnus Neuman https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Neuman, Magnus
PISA data clusters reveal student and school inequality that affects results
title PISA data clusters reveal student and school inequality that affects results
title_full PISA data clusters reveal student and school inequality that affects results
title_fullStr PISA data clusters reveal student and school inequality that affects results
title_full_unstemmed PISA data clusters reveal student and school inequality that affects results
title_short PISA data clusters reveal student and school inequality that affects results
title_sort pisa data clusters reveal student and school inequality that affects results
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9094565/
https://www.ncbi.nlm.nih.gov/pubmed/35544465
http://dx.doi.org/10.1371/journal.pone.0267040
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