Acceso usuarios
In order to examine and explain differences in self-regulated (SR) deep learning of successful immigrant and non-immigrant students we investigated a population of 650 high track 10th grade students in Amsterdarn, of which 39% had an immigrant background. By means of a questionnaire based on the MSLQ of Pintrich and De Groot (1990) the students reported their use of learning and resource management strategies, and their motivational values and attitudes. Two tests measured vocabulary and mathematical reasoning. Background characteristics, grades, track position and school composition (i.e., proportions of immigrant students) were registered as weIl.
A factor analysis yielded two learning patterns: self-regulated deep learning and self-regulated surface learning. Subsequent analyses of variance showed that, compared to boys, girls prefer SR surface learning. However, immigrant girls fit best in the SR deep learning pattern. The contrast between immigrant and non-immigrant girls is substantial. Explanations for this particular group difference, and for variance in SR deep learning in general, were explored. Of the achievement measures, only average grade explains variance in SR deep learning. Motivational values and resource management strategies do so as well. Track position does not, but school composition does affect the way immigrant students learn. Schools with more than 60% immigrant students enhance more SR deep learning than schools with less than 20% immigrant students. As the difference in SR deep learning between immigrant and non-immigrant girls is not explained by any of the covariates, we suggest alternative explanations in the discussion section.
