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Training self‐assessment and task‐selection skills to foster self‐regulated learning: Do trained skills transfer across domains?
Author(s) -
Raaijmakers Steven F.,
Baars Martine,
Paas Fred,
Merriënboer Jeroen J. G.,
Gog Tamara
Publication year - 2018
Publication title -
applied cognitive psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.719
H-Index - 100
eISSN - 1099-0720
pISSN - 0888-4080
DOI - 10.1002/acp.3392
Subject(s) - task (project management) , transfer of training , psychology , selection (genetic algorithm) , transfer of learning , heuristic , task analysis , cognitive psychology , mathematics education , artificial intelligence , computer science , developmental psychology , management , economics
Summary 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.