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dc.contributor.authorPsyridou, Maria
dc.contributor.authorKoponen, Tuire
dc.contributor.authorTolvanen, Asko
dc.contributor.authorAunola, Kaisa
dc.contributor.authorLerkkanen, Marja-Kristiina
dc.contributor.authorPoikkeus, Anna-Maija
dc.contributor.authorTorppa, Minna
dc.date.accessioned2024-01-12T11:31:23Z
dc.date.available2024-01-12T11:31:23Z
dc.date.issued2024
dc.identifier.citationPsyridou, M., Koponen, T., Tolvanen, A., Aunola, K., Lerkkanen, M.-K., Poikkeus, A.-M., & Torppa, M. (2024). Early prediction of math difficulties with the use of a neural networks model. <i>Journal of Educational Psychology</i>, <i>116</i>(2), 212-232. <a href="https://doi.org/10.1037/edu0000835" target="_blank">https://doi.org/10.1037/edu0000835</a>
dc.identifier.otherCONVID_194369443
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/92780
dc.description.abstractThe early prediction of math difficulties (MD) is important as it facilitates timely support. MD are multifaceted, and several factors are involved in their manifestation. This makes the accurate early prediction of MD particularly challenging. In the present study, we aim to predict MD in Grade 6 with kindergarten-age (age 6) measures by applying a neural networks model. We use a set of 49 variables assessed during kindergarten from the domains of early arithmetic skills, cognitive skills, the home learning environment, parental measures, motivation, behavioral problems, and gender, which have been shown to have associations with mathematical development and/or MD. A two-step approach was used: First, we examined whether the neural networks approach can provide a solution for the effective early identification of MD based on all 49 variables and, then, by using the most important predictors as identified by the initial model. The initial model achieved an area under the curve (AUC) of .818, demonstrating excellent performance. The most important predictors of Grade 6 MD came from the domains of arithmetic and cognitive skills (arithmetic skills, rapid automatized naming, number concepts, spatial skills, counting) and behavioral problems (attention-orientation). The model with only the most important predictors achieved an AUC of .776, indicating good performance. Our results provided proof of concept for using neural networks in MD prediction in Grade 6 using information already available in kindergarten. In schools, these results could be used to identify children at potential risk of developing MD and to provide access to early support.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherAmerican Psychological Association
dc.relation.ispartofseriesJournal of Educational Psychology
dc.rightsIn Copyright
dc.subject.otherarithmetic
dc.subject.othermath difficulties
dc.subject.otherprediction
dc.subject.otherneural networks model
dc.subject.otherkindergarten age
dc.titleEarly prediction of math difficulties with the use of a neural networks model
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202401121279
dc.contributor.laitosPsykologian laitosfi
dc.contributor.laitosKasvatustieteiden laitosfi
dc.contributor.laitosOpettajankoulutuslaitosfi
dc.contributor.laitosKasvatustieteiden ja psykologian tiedekuntafi
dc.contributor.laitosDepartment of Psychologyen
dc.contributor.laitosDepartment of Educationen
dc.contributor.laitosDepartment of Teacher Educationen
dc.contributor.laitosFaculty of Education and Psychologyen
dc.contributor.oppiainePsykologiafi
dc.contributor.oppiaineKasvatuspsykologiafi
dc.contributor.oppiaineHyvinvoinnin tutkimuksen yhteisöfi
dc.contributor.oppiainePsychologyen
dc.contributor.oppiaineKasvatuspsykologiaen
dc.contributor.oppiaineSchool of Wellbeingen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange212-232
dc.relation.issn0022-0663
dc.relation.numberinseries2
dc.relation.volume116
dc.type.versionacceptedVersion
dc.rights.copyright© American Psychological Association 2023
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber339418
dc.relation.grantnumber268586
dc.relation.grantnumber284439
dc.relation.grantnumber292466
dc.relation.grantnumber276239
dc.relation.grantnumber101002966
dc.relation.grantnumber101002966
dc.relation.grantnumber313768
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/101002966/EU//EARLYMATH
dc.subject.ysoneuroverkot
dc.subject.ysoaritmetiikka
dc.subject.ysomatematiikka
dc.subject.ysooppiminen
dc.subject.ysomatemaattiset taidot
dc.subject.ysooppimisvaikeudet
dc.subject.ysoennusteet
dc.subject.ysoleikki-ikäiset
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p7292
jyx.subject.urihttp://www.yso.fi/onto/yso/p3159
jyx.subject.urihttp://www.yso.fi/onto/yso/p3160
jyx.subject.urihttp://www.yso.fi/onto/yso/p2945
jyx.subject.urihttp://www.yso.fi/onto/yso/p23002
jyx.subject.urihttp://www.yso.fi/onto/yso/p5302
jyx.subject.urihttp://www.yso.fi/onto/yso/p3297
jyx.subject.urihttp://www.yso.fi/onto/yso/p6915
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1037/edu0000835
dc.relation.funderResearch Council of Finlanden
dc.relation.funderResearch Council of Finlanden
dc.relation.funderResearch Council of Finlanden
dc.relation.funderResearch Council of Finlanden
dc.relation.funderResearch Council of Finlanden
dc.relation.funderEuropean Commissionen
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
dc.relation.funderSuomen Akatemiafi
dc.relation.funderSuomen Akatemiafi
dc.relation.funderSuomen Akatemiafi
dc.relation.funderSuomen Akatemiafi
dc.relation.funderEuroopan komissiofi
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramPostdoctoral Researcher, AoFen
jyx.fundingprogramAcademy Project, AoFen
jyx.fundingprogramResearch costs of Academy Research Fellow, AoFen
jyx.fundingprogramResearch profiles, AoFen
jyx.fundingprogramAcademy Research Fellow, AoFen
jyx.fundingprogramERC Consolidator Granten
jyx.fundingprogramResearch costs of Academy Research Fellow, AoFen
jyx.fundingprogramTutkijatohtori, SAfi
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundingprogramAkatemiatutkijan tutkimuskulut, SAfi
jyx.fundingprogramProfilointi, SAfi
jyx.fundingprogramAkatemiatutkija, SAfi
jyx.fundingprogramERC Consolidator Grantfi
jyx.fundingprogramAkatemiatutkijan tutkimuskulut, SAfi
jyx.fundinginformationThe First Steps study was supported by the Academy of Finland (Grants 213486, 263891, 268586, and 292466). In addition, Maria Psyridou was supported by the Academy of Finland (Grant 339418). Minna Torppa was supported by the Academy of Finland (Grants 276239, 284439, and 313768) and by the European Research Council under the European Union’s Horizon 2020 Research and Innovation Program (Grant Agreement 101002966).
dc.type.okmA1


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