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dc.contributor.authorSaarela, Mirka
dc.contributor.authorKärkkäinen, Tommi
dc.contributor.editorSantos, Olga Christina
dc.contributor.editorBoticario, Jesus Gonzalez
dc.contributor.editorRomero, Cristobal
dc.contributor.editorPechenizkiy, Mykola
dc.contributor.editorMerceron, Agathe
dc.contributor.editorMitros, Piotr
dc.contributor.editorLuna, José María
dc.contributor.editorMihaescu, Cristian
dc.contributor.editorMoreno, Pablo
dc.contributor.editorHershkovitz, Arnon
dc.contributor.editorVentura, Sebastian
dc.contributor.editorDesmarais, Michel
dc.date.accessioned2017-01-24T12:24:39Z
dc.date.available2017-01-24T12:24:39Z
dc.date.issued2015
dc.identifier.citationSaarela, M., & Kärkkäinen, T. (2015). Do Country Stereotypes Exist in PISA? A Clustering Approach for Large, Sparse, and Weighted Data. In O. C. Santos, J. G. Boticario, C. Romero, M. Pechenizkiy, A. Merceron, P. Mitros, J. M. Luna, C. Mihaescu, P. Moreno, A. Hershkovitz, S. Ventura, & M. Desmarais (Eds.), <i>EDM 2015 : Proceedings of the 8th International Conference on Educational Data Mining</i> (pp. 156-163). International Educational Data Mining Society,. <a href="http://www.educationaldatamining.org/EDM2015/uploads/papers/paper_92.pdf" target="_blank">http://www.educationaldatamining.org/EDM2015/uploads/papers/paper_92.pdf</a>
dc.identifier.otherCONVID_24831366
dc.identifier.otherTUTKAID_66823
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/52814
dc.description.abstractCertain stereotypes can be associated with people from different countries. For example, the Italians are expected to be emotional, the Germans functional, and the Chinese hard-working. In this study, we cluster all 15-year-old students representing the 68 different nations and territories that participated in the latest Programme for International Student Assessment (PISA 2012). The hypothesis is that the students will start to form their own country groups when clustered according to the scale indices that summarize many of the students’ characteristics. In order to meet PISA data analysis requirements, we use a novel combination of our previously published algorithmic components to realize a weighted sparse data clustering approach. This enables us to work with around half a million observations with large number of missing values, which represent the population of more than 24 million students globally. Three internal cluster indices suitable for sparse data are used to determine the number of clusters and the whole procedure is repeated recursively to end up with a set of clusters on three different refinement levels. The results show that our final clusters can indeed be explained by the actual student performance but only to a marginal degree by the country.
dc.language.isoeng
dc.publisherInternational Educational Data Mining Society,
dc.relation.ispartofEDM 2015 : Proceedings of the 8th International Conference on Educational Data Mining
dc.relation.urihttp://www.educationaldatamining.org/EDM2015/uploads/papers/paper_92.pdf
dc.subject.otherWeighted Clustering
dc.subject.otherPISA
dc.subject.otherSparse Cluster Indices
dc.subject.otherCountry Stereotype
dc.titleDo Country Stereotypes Exist in PISA? A Clustering Approach for Large, Sparse, and Weighted Data
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201508212731
dc.contributor.laitosTietotekniikan laitosfi
dc.contributor.laitosDepartment of Mathematical Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2015-08-21T12:15:04Z
dc.relation.isbn978-84-606-9425-0
dc.type.coarconference paper
dc.description.reviewstatuspeerReviewed
dc.format.pagerange156-163
dc.type.versionpublishedVersion
dc.rights.copyright© the Authors & International Educational Data Mining Society, 2015.
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational conference on educational data mining


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