1007205.pdf

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 stude...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Γλώσσα:English
Έκδοση: Springer Nature 2020
Διαθέσιμο Online:https://www.springer.com/9783030261832
id oapen-20.500.12657-22955
record_format dspace
spelling oapen-20.500.12657-229552024-03-22T19:23:35Z Motivational Profiles in TIMSS Mathematics Michaelides, Michalis P. Brown, Gavin T. L. Eklöf, Hanna Papanastasiou, Elena C. Education Assessment Educational psychology Education—Psychology Statistics  International education  Comparative education thema EDItEUR::J Society and Social Sciences::JH Sociology and anthropology::JHB Sociology::JHBC Social research and statistics thema EDItEUR::J Society and Social Sciences::JN Education thema EDItEUR::J Society and Social Sciences::JN Education::JNC Educational psychology thema EDItEUR::J Society and Social Sciences::JN Education::JND Educational systems and structures::JNDH Education: examinations and assessment 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. 2020-03-18 13:36:15 2020-04-01T08:57:39Z 2020-04-01T08:57:39Z 2019 book 1007205 http://library.oapen.org/handle/20.500.12657/22955 eng IEA Research for Education application/pdf 1007205.pdf https://www.springer.com/9783030261832 Springer Nature 10.1007/978-3-030-26183-2 10.1007/978-3-030-26183-2 6c6992af-b843-4f46-859c-f6e9998e40d5 144 Cham open access
institution OAPEN
collection DSpace
language English
description 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.
title 1007205.pdf
spellingShingle 1007205.pdf
title_short 1007205.pdf
title_full 1007205.pdf
title_fullStr 1007205.pdf
title_full_unstemmed 1007205.pdf
title_sort 1007205.pdf
publisher Springer Nature
publishDate 2020
url https://www.springer.com/9783030261832
_version_ 1799945190130057216