Training self-assessment and task-selection skills: A cognitive approach to improving self-regulated learning

Danny Kostons*, Tamara van Gog, Fred Paas

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

106 Citations (Scopus)

Abstract

For self-regulated learning to be effective, students need to be able to accurately assess their own performance on a learning task and use this assessment for the selection of a new learning task. Evidence suggests, however, that students have difficulties with accurate self-assessment and task selection, which may explain the poor learning outcomes often found with self-regulated learning. In experiment 1, the hypothesis was investigated and confirmed that observing a human model engaging in self-assessment, task selection, or both could be effective for secondary education students' (N = 80) acquisition of self-assessment and task-selection skills. Experiment 2 investigated and confirmed the hypothesis that secondary education students' (N = 90) acquisition of self-assessment and task-selection skills, either through examples or through practice, would enhance the effectiveness of self-regulated learning. It can be concluded that self-assessment and task-selection skills indeed play an important role in self-regulated learning and that training these skills can significantly increase the amount of knowledge students can gain from self-regulated learning in which they choose their own learning tasks. (C) 2011 Elsevier Ltd. All rights reserved.

Original languageEnglish
Pages (from-to)121-132
Number of pages12
JournalLearning and Instruction
Volume22
Issue number2
DOIs
Publication statusPublished - Apr-2012

Keywords

  • Self-regulated learning
  • Self-assessment
  • Task selection
  • Example-based learning
  • COMPUTER-BASED INSTRUCTION
  • WORKED EXAMPLES
  • MENTAL EFFORT
  • LOAD THEORY
  • HYPERMEDIA
  • EFFICACY
  • ACHIEVEMENT
  • STUDENTS
  • PERFORMANCE
  • KNOWLEDGE

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