dc.contributor.author | Saarela, Mirka | |
dc.contributor.author | Kärkkäinen, Tommi | |
dc.contributor.editor | Santos, Olga Christina | |
dc.contributor.editor | Boticario, Jesus Gonzalez | |
dc.contributor.editor | Romero, Cristobal | |
dc.contributor.editor | Pechenizkiy, Mykola | |
dc.contributor.editor | Merceron, Agathe | |
dc.contributor.editor | Mitros, Piotr | |
dc.contributor.editor | Luna, José María | |
dc.contributor.editor | Mihaescu, Cristian | |
dc.contributor.editor | Moreno, Pablo | |
dc.contributor.editor | Hershkovitz, Arnon | |
dc.contributor.editor | Ventura, Sebastian | |
dc.contributor.editor | Desmarais, Michel | |
dc.date.accessioned | 2017-01-24T12:24:39Z | |
dc.date.available | 2017-01-24T12:24:39Z | |
dc.date.issued | 2015 | |
dc.identifier.citation | Saarela, 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.other | CONVID_24831366 | |
dc.identifier.other | TUTKAID_66823 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/52814 | |
dc.description.abstract | Certain 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.iso | eng | |
dc.publisher | International Educational Data Mining Society, | |
dc.relation.ispartof | EDM 2015 : Proceedings of the 8th International Conference on Educational Data Mining | |
dc.relation.uri | http://www.educationaldatamining.org/EDM2015/uploads/papers/paper_92.pdf | |
dc.subject.other | Weighted Clustering | |
dc.subject.other | PISA | |
dc.subject.other | Sparse Cluster Indices | |
dc.subject.other | Country Stereotype | |
dc.title | Do Country Stereotypes Exist in PISA? A Clustering Approach for Large, Sparse, and Weighted Data | |
dc.type | conferenceObject | |
dc.identifier.urn | URN:NBN:fi:jyu-201508212731 | |
dc.contributor.laitos | Tietotekniikan laitos | fi |
dc.contributor.laitos | Department of Mathematical Information Technology | en |
dc.contributor.oppiaine | Tietotekniikka | fi |
dc.contributor.oppiaine | Mathematical Information Technology | en |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | |
dc.date.updated | 2015-08-21T12:15:04Z | |
dc.relation.isbn | 978-84-606-9425-0 | |
dc.type.coar | conference paper | |
dc.description.reviewstatus | peerReviewed | |
dc.format.pagerange | 156-163 | |
dc.type.version | publishedVersion | |
dc.rights.copyright | © the Authors & International Educational Data Mining Society, 2015. | |
dc.rights.accesslevel | openAccess | fi |
dc.relation.conference | International conference on educational data mining | |