Antecedents and relative importance of student motivation for science and mathematics achievement in TIMSS

Periodical
Frontiers in Education
Year
2021
Relates to study/studies
TIMSS 2011

Antecedents and relative importance of student motivation for science and mathematics achievement in TIMSS

Abstract

Although motivation has been shown to have substantial influence on learning, the relative significance of Students’ motivational characteristics, compared to other school-related factors, for student learning and performance is still unclear. Furthermore, knowledge about the relative importance of different situational variables for predicting these motivational characteristics is crucial for educational decisions about how to enhance student motivation. This study examined (1) the relative importance of motivational characteristics derived from five different theories on motivation and epistemic beliefs, compared to almost 300 situational factors, for predicting student performance on the TIMSS 2011 achievement tests in science and mathematics, and (2) how student motivational characteristics can be predicted by the background variables in the TIMSS 2011 questionnaires and an additional questionnaire about motivation accompanying TIMSS in Sweden. Up to 52% of the variation in student performance could be predicted by models containing all background variables, and student motivational characteristics were among the most important variables in the model. Models that comprised only student motivational characteristics from several motivation theories predicted up to 27% of student performance on the achievement test, while models using only single motivational characteristics predicted, on average, 7%. Results emphasize teachers’ importance for student motivation. Five teacher features were consistently among the most important variables in predicting Students’ motivational characteristics. These five variables predicted as much of the variation in important student motivational characteristics as the remaining 300 situational variables together.