Self-regulated profiles and academic achievement.

To date, research on the relation between learning self-regulation and academic achievement has generally show disparate results. This work intends to look into this relation from a new perspective, which consists in classifying the students as more or less self-regulated depending on diverse indicators and using cluster analysis. The aim of this work was to identify the possible self-regulated learning profiles in a sample of university students. By means of stepwise linear regression analysis, we determined which of the selected variables better predicted metacognitive self-regulation. Then, three significantly different self-regulated learning profiles were obtained by two-step cluster analysis with those variables. Lastly, ANOVA was used to analyse the relation between the self-regulated learning profiles and academic achievement. The implications of these data for the educational practice at university are discussed.