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Table of contents

  1. Front Matter
    Pages i-xi
  2. Michalis P. Michaelides, Gavin T. L. Brown, Hanna Eklöf, Elena C. Papanastasiou
    Pages 1-7 Open Access
  3. Michalis P. Michaelides, Gavin T. L. Brown, Hanna Eklöf, Elena C. Papanastasiou
    Pages 9-23 Open Access
  4. Michalis P. Michaelides, Gavin T. L. Brown, Hanna Eklöf, Elena C. Papanastasiou
    Pages 25-40 Open Access
  5. Michalis P. Michaelides, Gavin T. L. Brown, Hanna Eklöf, Elena C. Papanastasiou
    Pages 41-71 Open Access
  6. Michalis P. Michaelides, Gavin T. L. Brown, Hanna Eklöf, Elena C. Papanastasiou
    Pages 73-83 Open Access
  7. Michalis P. Michaelides, Gavin T. L. Brown, Hanna Eklöf, Elena C. Papanastasiou
    Pages 85-95 Open Access
  8. Back Matter
    Pages 97-144

About this book

Introduction

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.

Keywords

International large-scale educational assessments IEA Cross-cultural education research Achievement motivation Mathematics achievement Motivation, affect and attitudes School mathematics Cluster analysis TIMSS data Learning and achievement in mathematics Motivation profiles Mathematics self-confidence Enjoyment of mathematics Students value mathematics Trends in International Mathematics and Science Study Open Access

Authors and affiliations

  • Michalis P. Michaelides
    • 1
  • Gavin T. L. Brown
    • 2
  • Hanna Eklöf
    • 3
  • Elena C. Papanastasiou
    • 4
  1. 1.Department of PsychologyUniversity of CyprusNicosiaCyprus
  2. 2.The University of AucklandAucklandNew Zealand
  3. 3.Umeå UniversityUmeåSweden
  4. 4.Department of EducationUniversity of NicosiaNicosiaCyprus

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-26183-2
  • Copyright Information International Association for the Evaluation of Educational Achievement (IEA) 2019
  • License CC BY-NC
  • Publisher Name Springer, Cham
  • eBook Packages Education
  • Print ISBN 978-3-030-26182-5
  • Online ISBN 978-3-030-26183-2
  • Series Print ISSN 2366-1631
  • Series Online ISSN 2366-164X
  • Buy this book on publisher's site