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dc.contributor.authorJauhiainen, Susanne
dc.contributor.authorKrosshaug, Tron
dc.contributor.authorPetushek, Erich
dc.contributor.authorKauppi, Jukka-Pekka
dc.contributor.authorÄyrämö, Sami
dc.date.accessioned2021-08-18T12:34:24Z
dc.date.available2021-08-18T12:34:24Z
dc.date.issued2021
dc.identifier.citationJauhiainen, S., Krosshaug, T., Petushek, E., Kauppi, J.-P., & Äyrämö, S. (2021). Information Extraction from Binary Skill Assessment Data with Machine Learning. <i>International Journal of Learning Analytics and Artificial Intelligence for Education</i>, <i>3</i>(1), 20-35. <a href="https://doi.org/10.3991/ijai.v3i1.24295" target="_blank">https://doi.org/10.3991/ijai.v3i1.24295</a>
dc.identifier.otherCONVID_99332798
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/77421
dc.description.abstractStrength training exercises are essential for rehabilitation, improving our health as well as in sports. For optimal and safe training, educators and trainers in the industry should comprehend exercise form or technique. Currently, there is a lack of tools measuring in-depth skills of strength training experts. In this study, we investigate how data mining methods can be used to identify novel and useful skill patterns from a binary multiple choice questionnaire test designed to measure the knowledge level of strength training experts. A skill test assessing exercise technique expertise and comprehension was answered by 507 fitness professionals with varying backgrounds. A triangulated approach of clustering and non-negative matrix factorization (NMF) was used to discover skill patterns among participants and patterns in test questions. Four distinct participant subgroups were identified in data with clustering and further question patterns with NMF. The results can be used to, for example, identify missing skills and knowledge in participants and subgroups of participants and form general and personalized or background specific guidelines for future education. In addition, the test can be optimized based on, for example, if some questions can be answered correct even without the required skill or if they seem to be measuring overlapping skills. Finally, this approach can be utilized with other multiple choice test data in future educational research.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherInternational Association of Online Engineering (IAOE)
dc.relation.ispartofseriesInternational Journal of Learning Analytics and Artificial Intelligence for Education
dc.rightsCC BY 4.0
dc.subject.otherdata mining
dc.subject.otherclustering
dc.subject.othernon-negative matrix factorization
dc.subject.otherstrength training skill test
dc.subject.otherbinary data
dc.titleInformation Extraction from Binary Skill Assessment Data with Machine Learning
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202108184584
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange20-35
dc.relation.issn2706-7564
dc.relation.numberinseries1
dc.relation.volume3
dc.type.versionpublishedVersion
dc.rights.copyright© 2021 the Authors
dc.rights.accesslevelopenAccessfi
dc.subject.ysokoneoppiminen
dc.subject.ysovoimaharjoittelu
dc.subject.ysotiedonlouhinta
dc.subject.ysoklusterianalyysi
dc.subject.ysoliikuntataidot
dc.subject.ysomittarit (mittaus)
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p16233
jyx.subject.urihttp://www.yso.fi/onto/yso/p5520
jyx.subject.urihttp://www.yso.fi/onto/yso/p27558
jyx.subject.urihttp://www.yso.fi/onto/yso/p24598
jyx.subject.urihttp://www.yso.fi/onto/yso/p21210
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.3991/ijai.v3i1.24295
jyx.fundinginformationSusanne Jauhiainen was funded by the Jenny and Antti Wihuri Foundation (grant 00190110) and by the Emil Aaltonen Foundation (grant 180063 KO).
dc.type.okmA1


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