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Motivational Profiles in TIMSS Mathematics

Michalis P. Michaelides

  • Bindwijze: Hardcover
  • Taal: en
  • Categorie: Mens & Maatschappij
  • ISBN: 9783030261825
Exploring Student Clusters Across Countries and Time
Inhoud
Taal:en
Bindwijze:Hardcover
Oorspronkelijke releasedatum:20 september 2019
Aantal pagina's:144
Illustraties:Nee
Betrokkenen
Hoofdauteur:Michalis P. Michaelides
Tweede Auteur:Gavin T. L. Brown
Co Auteur:Elena C. Papanastasiou
Co Auteur:Elena C. Papanastasiou
Overige kenmerken
Editie:1st ed. 2019
Extra groot lettertype:Nee
Product breedte:155 mm
Product lengte:235 mm
Studieboek:Nee
Verpakking breedte:155 mm
Verpakking hoogte:235 mm
Verpakking lengte:235 mm
Verpakkingsgewicht:454 g
Overige kenmerken
Editie:1st ed. 2019
Extra groot lettertype:Nee
Product breedte:155 mm
Product lengte:235 mm
Studieboek:Nee
Verpakking breedte:155 mm
Verpakking hoogte:235 mm
Verpakking lengte:235 mm
Verpakkingsgewicht:454 g

Samenvatting

This open access book presents a person-centered exploration of student profiles, using variables related to motivation to do school mathematics derived from the IEA’s Trends in International Mathematics and Science Study (TIMSS) data.



This open access book presents a person-centered exploration of student profiles, using variables related to motivation to do school mathematics derived from the IEA’s Trends in International Mathematics and Science Study (TIMSS) data. Statistical cluster analysis is used to identify groups of students with similar motivational profiles, across grades and over time, for multiple participating countries.

While motivational variables systematically relate to school outcomes, linear relationships can obscure the diverse makeup of student subgroups, each with varying combinations of motivation, emotions, and attitudes. In this book, a person-centered analysis of distinct and meaningful motivational profiles and their differences on sociodemographic variables and mathematics performance broadens understanding about the role that motivation characteristics play in learning and achievement in mathematics.

Exploiting the richness of IEA’s TIMSS data from many countries, extracted clusters reveal consistent, as well as certain nuanced patterns that are systematically linked to sociodemographic and achievement measures. Student clusters with inconsistent motivational profiles were found in all countries; mathematics self-confidence then emerged as the variable more closely associated with average achievement. The findings demonstrate that teachers, researchers, and policymakers need to take into account differential student profiles, prioritizing techniques that target skill and competence in mathematics, in educational efforts to develop student motivation.