Effects of self-assessment feedback on self-assessment and task-selection accuracy

Steven F. Raaijmakers*, Martine Baars, Fred Paas, Jeroen J. G. van Merrienboer, Tamara van Gog

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

16 Citations (Web of Science)

Abstract

Effective self-regulated learning in settings in which students can decide what tasks to work on, requires accurate self-assessment (i.e., a judgment of own level of performance) as well as accurate task selection (i.e., choosing a subsequent task that fits the current level of performance). Because self-assessment accuracy is often low, task-selection accuracy suffers as well and, consequently, self-regulated learning can lead to suboptimal learning outcomes. Recent studies have shown that a training with video modeling examples enhanced self-assessment accuracy on problem-solving tasks, but the training was not equally effective for every student and, overall, there was room for further improvement in self-assessment accuracy. Therefore, we investigated whether training with video examples followed by feedback focused on self-assessment accuracy would improve subsequent self-assessment and task-selection accuracy in the absence of the feedback. Experiment 1 showed, contrary to our hypothesis, that self-assessment feedback led to less accurate future self-assessments. In Experiment 2, we provided students with feedback focused on self-assessment accuracy plus information on the correct answers, or feedback focused on self-assessment accuracy, plus the correct answers and the opportunity to contrast those with their own answers. Again, however, we found no beneficial effect of feedback on subsequent self-assessment accuracy. In sum, we found no evidence that feedback on self-assessment accuracy improves subsequent accuracy. Therefore, future research should address other ways improving accuracy, for instance by taking into account the cues upon which students base their self-assessments.

Original languageEnglish
Pages (from-to)21-42
Number of pages22
JournalMetacognition and Learning
Volume14
Issue number1
DOIs
Publication statusPublished - Apr 2019

Keywords

  • Self-regulated learning
  • Problem solving
  • Self-assessment
  • Task-selection
  • Feedback
  • VIDEO MODELING EXAMPLES
  • ACADEMIC-ACHIEVEMENT
  • SKILL
  • METACOGNITION
  • EFFICIENCY
  • STANDARDS
  • KNOWLEDGE

Cite this