Application of learning analytics to study the accuracy of self-reported working patterns in self-regulated learning questionnaires

Abstract

Time management strategies and self-regulated learning have received much attention. Nevertheless, there is a lack of research in terms of student self-awareness related to their own self-regulation and autonomy. This study aims to validate whether students are self-conscious about their working patterns and their time management. In order to do so, several parameters like work sessions regularity or invested time have been computed and analyzed. This invested time is measured thanks to a data-gathering tool that collects events generated by students. Results show that students are, in general, self-aware of their own working patterns and that the regularity of their weekly work time is correlated with their final marks.