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dc.contributor.authorSaarela, Mirka
dc.contributor.authorKärkkäinen, Tommi
dc.date.accessioned2018-02-16T07:12:11Z
dc.date.available2018-02-16T07:12:11Z
dc.date.issued2014
dc.identifier.citationSaarela, M., & Kärkkäinen, T. (2014). Discovering Gender-Specific Knowledge from Finnish Basic Education using PISA Scale Indices. In J. Stamper, Z. Pardos, M. Mavrikis, & B. McLaren (Eds.), <em>EDM 2014 : Proceedings of the 7th International Conference on Educational Data Mining</em> (pp. 60-67). International Educational Data Mining Society (IEDMS). Retrieved from <a href="http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/60_EDM-2014-Full.pdf">http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20pape...</a>
dc.identifier.otherTUTKAID_65030
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/57089
dc.description.abstractThe Programme for International Student Assessment, PISA, is a worldwide study to assess knowledge and skills of 15- year-old students. Results of the latest PISA survey conducted in 2012 were published in December 2013. According to the results, Finland is one of the few countries where girls performed better in mathematics than boys. The purpose of this work is to refine the analysis of this observation by using education data mining techniques. More precisely, as part of standard PISA preprocessing phase certain scale indices are constructed based on information gathered from the background questionnaire of each participating student. The indices describe, e.g., students’ engagement, drive and self-beliefs, especially related to mathematics, the main assessment area in PISA 2012. However, around 30% of the scale indices are missing so that a nonstructured sparsity pattern must be dealt with. We handle this using a special, robust clustering technique, which is then applied to Finnish subset of PISA data. Already direct interpretation of the created clusters reveals interesting patterns. Clusterwise analysis through relationship mining refines the confidence on our final conclusion that attitudes towards mathematics which are often gender-specific are the most important factors to explain the performance in mathematics.
dc.format.extent466
dc.language.isoeng
dc.publisherInternational Educational Data Mining Society (IEDMS)
dc.relation.ispartofEDM 2014 : Proceedings of the 7th International Conference on Educational Data Mining, ISBN 978-0-9839525-4-1
dc.relation.urihttp://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/60_EDM-2014-Full.pdf
dc.subject.otherPISA
dc.subject.otherrobust clustering
dc.subject.otherfrequent itemset
dc.subject.otherassociation rule
dc.titleDiscovering Gender-Specific Knowledge from Finnish Basic Education using PISA Scale Indices
dc.identifier.urnURN:NBN:fi:jyu-201502051262
dc.contributor.laitosTietotekniikan laitosfi
dc.contributor.laitosDepartment of Mathematical Information Technologyen
dc.contributor.oppiaineTietotekniikka
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2015-02-05T16:30:09Z
dc.relation.isbn978-0-9839525-4-1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange60-67
dc.type.versionacceptedVersion
dc.rights.copyright© the Authors & IEDMS, 2014.
dc.rights.accesslevelopenAccessfi


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