Michaelides, Michalis P. <br/><br/>
Motivational Profiles in TIMSS Mathematics  Exploring Student Clusters Across Countries and Time /  [electronic resource] : 
by Michalis P. Michaelides, Gavin T. L. Brown, Hanna Eklöf, Elena C. Papanastasiou. 
 - 1st ed. 2019. 
 - XI, 144 páginas50 ilustraciones, 1 ilustraciones in color.  online resource. 
 - IEA Research for Education, A Series of In-depth Analyses Based on Data of the International Association for the Evaluation of Educational Achievement (IEA),  7  2366-164X ; .
 - IEA Research for Education, A Series of In-depth Analyses Based on Data of the International Association for the Evaluation of Educational Achievement (IEA),  7 .
<br/><br/>1. Introduction to Motivational Profiles in TIMSS Mathematics -- 2. The Relationship of Motivation with Achievement in Mathematics -- 3. Methodology: Cluster Analysis of Motivation Variables in the TIMSS Data -- 4. Cluster Analysis Results for TIMSS 2015 Mathematics Motivation by Grade and Jurisdiction -- 5. Cluster Analysis Findings Over 20 Years of TIMSS -- 6. Insights from Motivational Profiles in TIMSS Mathematics -- Appendix A -- Appendix B -- Appendix C. 
<br/><br/> Open Access 
<br/><br/> 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. 
<br/><br/><label>ISBN: </label>9783030261832 
<br/><br/><label>Standard No.: </label>10.1007/978-3-030-26183-2  doi 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Educational tests and measurements.<br/>Educational psychology.<br/>Social sciences-Statistical methods.<br/>International education .<br/>Comparative education.<br/>Assessment and Testing.<br/>Educational Psychology.<br/>Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy.<br/>International and Comparative Education.
<br/><br/><label>Dewey Class. No.: </label>371.26