Hanna Eklöf – författare

En stor del av lärares vardag ägnas åt bedömning, vilket tar  tid och kräver mycket tankemöda. Många lärare strävar efter att vara kompetenta bedömare, men brottas samtidigt med frågor som ”Hur tar jag fram ett bra prov?”, ”Kan jag lita på den information som jag har fått?”  och ”Hur sätter jag rättvisa betyg?”

Denna antologi är skriven av 13 forskare verksamma vid framförallt Umeå och Göteborgs universitet. Tillsammans klargör de vad vetenskapen säger om bedömning och betygsättning, och hur detta kan praktiseras direkt i klassrummet. Varje kapitel kan läsas för sig, men bildar tillsammans en givande helhet genom att ta upp teori och praktik, samt både lärares och elevers perspektiv.

Boken ger handfasta tips på hur lärare kan konstruera bra och inkluderande prov för att kunna göra så rättvisa bedömningar som möjligt. Den förklarar även hur lärare kan hantera AI och digitala verktyg på bästa sätt och hur de skapar ett positivt bedömningsklimat i klassrummet, med trygga och motiverade elever.

Boken riktar sig främst till blivande och yrkesverksamma lärare, men också till andra som har ett intresse för området.

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.

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.