Improving Self-Regulated Learning: Effects of Training and Feedback on Self-Assessment and Task-Selection Accuracy

Research output: ThesisDoctoral thesis 1 (Research UU / Graduation UU)


The ability to self-regulate one’s own learning is increasingly important in current society. In almost every line of work it is necessary to regularly update one’s knowledge and skills. This requires effective self-regulated learning skills. However, most people do not possess effective self-regulated learning skills, which is why educational researchers have been searching for way of training and supporting self-regulated learning skills. Previous research indicates that it is feasible to train self-regulated learning skills in secondary education using video modeling examples (Kostons, Van Gog, & Paas, 2012). Video modeling examples are videos in which a student shows how he or she performs a task. In our case, the students in the videos selected subsequent tasks from a task database. Yet, successful implementation of such training in secondary education implies that these skills – once learned – can be used in other domains than the one they have been acquired in. In other words, if the self-regulated learning skills transfer to other domains? This question was addressed in Chapters 2 and 3 of this dissertation. The study presented in Chapter 2 showed that students who were trained were better at selecting appropriate tasks for other students in another domain. This indicated that the trained self-regulated learning skills might transfer to other domains. However, in this experiment students chose tasks for other, fictitious, students. Chapter 3 explored whether the self-regulated learning skills might also transfer when students actually used the trained skills in another domain. This turned out not to be the case. Even though the students, again, could select tasks appropriately for other students, they were not able to take advantage of this skill for themselves. One explanation might be that having to solve problems yourself as well as having to regulate your learning might create an excessively high cognitive load. To overcome this cognitive load additional support might be needed. Another approach to improving the task selection made by students is to improve the accuracy of their self-assessments. As task selections are based on self-assessments, improving these self-assessments would automatically improve their task selections. This is why, in Chapter 4, students were provided with feedback that focused on the accuracy of their self-assessments (i.e., if students could indicate accurately if they had performed a task correctly). Such self-assessment accuracy feedback did not improve the accuracy of self-assessments. Rather, there were indications of a negative effect of the feedback. This negative effect disappeared when self-assessments could be made in the presence of the correct answers. In these experiments, students selected tasks based on a combination of perceived mental effort and their self-assessments of performance. In Chapter 5, it was tested if providing feedback might alter the perception of mental effort that was needed to perform a task. This was clearly the case as mental effort was higher after negatively valenced feedback and lower after positively valenced feedback.
Original languageEnglish
Awarding Institution
  • Utrecht University
  • van Gog, Tamara, Primary supervisor
  • Merriënboer, J.J.G., Supervisor, External person
  • Baars, Martine, Co-supervisor, External person
Award date16 Feb 2018
Print ISBNs978-90-393-6927-2
Publication statusPublished - 16 Feb 2018


  • Self-regulated learning
  • Problem solving
  • Self-assessment
  • Task selection
  • Example-based learning


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