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.

Additional Metadata
Keywords Example-based learning, Self-assessment, Self-regulated learning, Task selection, Transfer
Persistent URL dx.doi.org/10.1002/acp.3392, hdl.handle.net/1765/104635
Journal Applied Cognitive Psychology
Citation
Raaijmakers, S.F, Baars, M.A, Paas, G.W.C, van Merriënboer, J.J.G, & van Gog, T. (2018). Training self-assessment and task-selection skills to foster self-regulated learning: Do trained skills transfer across domains?. Applied Cognitive Psychology. doi:10.1002/acp.3392