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Investigation of Turkey's PISA 2015 Science Achievement and Associated Variables Using Hierarchical Linear Modeling

dc.contributor.authorYILDIZ, Mustafa
dc.contributor.authorERDAŞ KARTAL, Eda
dc.contributor.authorMESCİ, Günkut
dc.date.accessioned2026-01-04T14:16:57Z
dc.date.issued2020-06-30
dc.description.abstractThe purpose of this study is to investigate the variables related to science achievement of high school students in Turkey portion of PISA 2015, by using hierarchical linear modeling (HLM) approach. The findings revealed that the relationship between the science achievement and demographic characteristics of the students is stronger than the relationship between science achievement and the affective domain as well as the learning environment. Based on these findings, it is thought that improving students’ science achievement is possible by taking into account the variables that affect the success, especially with improvements that can provide equal opportunities in the context of demographic variables and the construction of inclusive school systems.
dc.description.urihttps://doi.org/10.17522/balikesirnef.663737
dc.description.urihttps://dergipark.org.tr/en/download/article-file/1177139
dc.description.urihttps://dx.doi.org/10.17522/balikesirnef.663737
dc.identifier.doi10.17522/balikesirnef.663737
dc.identifier.eissn1307-6086
dc.identifier.endpage480
dc.identifier.openairedoi_dedup___::1f9191acd65c3880138af2c5f6dba63c
dc.identifier.orcid0000-0002-3139-2698
dc.identifier.orcid0000-0002-1568-827x
dc.identifier.orcid0000-0003-0319-5993
dc.identifier.startpage450
dc.identifier.urihttps://hdl.handle.net/20.500.12597/38068
dc.identifier.volume14
dc.publisherNecatibey Faculty of Education Electronics Journal of Science and Mathematics Education
dc.relation.ispartofNecatibey Eğitim Fakültesi Elektronik Fen ve Matematik Eğitimi Dergisi
dc.rightsOPEN
dc.subject.sdg5. Gender equality
dc.subject.sdg4. Education
dc.subject.sdg10. No inequality
dc.titleInvestigation of Turkey's PISA 2015 Science Achievement and Associated Variables Using Hierarchical Linear Modeling
dc.typeArticle
dspace.entity.typePublication
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