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dc.contributor.authorSaarela, Mirka
dc.contributor.authorKärkkäinen, Tommi
dc.date.accessioned2015-08-21T11:40:02Z
dc.date.available2015-08-21T11:40:02Z
dc.date.issued2015
dc.identifier.citationSaarela, M., & Kärkkäinen, T. (2015). Analysing Student Performance using Sparse Data of Core Bachelor Courses. <i>Journal of Educational Data Mining</i>, <i>7</i>(1), 3-32.
dc.identifier.otherCONVID_24829390
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/46677
dc.description.abstractCurricula for Computer Science (CS) degrees are characterized by the strong occupational orientation of the discipline. In the BSc degree structure, with clearly separate CS core studies, the learning skills for these and other required courses may vary a lot, which is shown in students’ overall performance. To analyze this situation, we apply nonstandard educational data mining techniques on a preprocessed log file of the passed courses. The joint variation in the course grades is studied through correlation analysis while intrinsic groups of students are created and analyzed using a robust clustering technique. Since not all students attended all courses, there is a nonstructured sparsity pattern to cope with. Finally, multilayer perceptron neural network with cross-validation based generalization assurance is trained and analyzed using analytic mean sensitivity to explain the nonlinear regression model constructed. Local (withinmethods) and global (between-methods) triangulation of different analysis methods is argued to improve the technical soundness of the presented approaches, giving more confidence to our final conclusion that general learning capabilities predict the students’ success better than specific IT skills learned as part of the core studies.
dc.language.isoeng
dc.publisherInternational Working Group on Educational Data Mining
dc.relation.ispartofseriesJournal of Educational Data Mining
dc.subject.otherSparse Educational Data
dc.subject.otherCurricula Refinement
dc.subject.otherCorrelation Analysis
dc.subject.otherRobust Clustering
dc.subject.otherMultilayer Perceptron
dc.titleAnalysing Student Performance using Sparse Data of Core Bachelor Courses
dc.typeresearch article
dc.identifier.urnURN:NBN:fi:jyu-201508212722
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/JournalArticle
dc.date.updated2015-08-21T09:15:08Z
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange3-32
dc.relation.issn2157-2100
dc.relation.numberinseries1
dc.relation.volume7
dc.type.versionacceptedVersion
dc.rights.copyright© the Authors. This is a final draft version of an article whose final and definitive form has been published by International Working Group on Educational Data Mining.
dc.rights.accesslevelopenAccessfi
dc.type.publicationarticle
dc.subject.ysokolmiomittaus
jyx.subject.urihttp://www.yso.fi/onto/yso/p10719
dc.type.okmA1


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