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

نویسندگان

  • Steven F Raaijmakers
  • Martine Baars
  • Fred Paas
  • Jeroen J G van Merriënboer
  • Tamara van Gog
چکیده

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, and-importantly-such 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.

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عنوان ژورنال:

دوره 32  شماره 

صفحات  -

تاریخ انتشار 2018