Training self-assessment and task-selection skills to foster self-regulated learning: Do trained skills transfer across domains?

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

17 Citations (Web of Science)

Abstract

Students' ability to accurately self-assess their performance and select a suitable subsequent learning task in response is imperative for effective self-regulated learning. Video modeling examples have proven effective for training self-assessment and task-selection skills, andimportantlysuch training fostered self-regulated learning outcomes. It is unclear, however, whether trained skills would transfer across domains. We investigated whether skills acquired from training with either a specific, algorithmic task-selection rule or a more general heuristic task-selection rule in biology would transfer to self-regulated learning in math. A manipulation check performed after the training confirmed that both algorithmic and heuristic training improved task-selection skills on the biology problems compared with the control condition. However, we found no evidence that students subsequently applied the acquired skills during self-regulated learning in math. Future research should investigate how to support transfer of task-selection skills across domains.
Original languageEnglish
Pages (from-to)270-277
Number of pages8
JournalApplied Cognitive Psychology
Volume32
Issue number2
DOIs
Publication statusPublished - 1 Mar 2018

Keywords

  • example-based learning
  • self-assessment
  • self-regulated learning
  • task selection
  • transfer
  • COGNITIVE-LOAD
  • DIRECTIONS
  • SCAFFOLDS
  • FUTURE

Cite this