Component Practice and Exposure to a Problem-Solving Context
Abstract
The component fluency hypothesis suggests that practicing component skills can increase the fluency of target task performance. Three experiments examined boundary conditions for this hypothesis. College students practiced individual Boolean functions, then solved transfer problems using these functions. In Experiment 1, varying the amount of component practice had no detectable effect on problem solving fluency. In Experiment 2, subjects had 480 trials of component practice, with exposure to the target task after 0, 240, or 480 trials. A fourth group had 192 trials of component practice. The results partially replicated Experiment 1: exposure to the target context after 240 trials produced the best final performance. Experiment 3 replicated Experiment 2, using a more sensitive target task and an additional group exposed to the target task after 120 trials of component practice. Results confirmed the importance of target task exposure. These findings suggest limits on the component fluency hypothesis.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
According to the 4C/ID model, additional part-task practice starts only after learners have been introduced to the recurrent aspects in the context of the learning tasks, so that part-task practice takes place in a fruitful cognitive context that allows learners to identify the activities required to integrate the recurrent aspects in the whole task.
“According to the 4C/ID model, additional part-task practice starts only after the learners have been introduced to the recurrent aspects in the context of the learning tasks, so that part-task practice takes place in a fruitful cognitive context that allows learners to identify the activities that are required to integrate the recurrent aspects in the whole task (Carlson, Khoo, & Elliot, 1990).”
From Taking the Load Off a Learner's Mind: Instructional Design for Complex Learning (2003) · cites this paper as Carlson, Khoo, & Elliot, 1990
Cited by
Sorted by citations
Taking the Load Off a Learner's Mind: Instructional Design for Complex Learning
van Merrienboer, J. J. G., Kirschner, P. A., & Kester, L. (2003) · Educational Psychologist · doi:10.1207/s15326985ep3801_2 · Cited by 857 · see citing works
Blueprints for complex learning: The 4C/ID-model
van Merriënboer, J. J. G., Clark, R. E., & de Croock, M. B. M. (2002) · Educational Technology Research and Development · doi:10.1007/bf02504993 · Cited by 708 · see citing works
- Authors
- Carlson, R. A., Khoo, B. H., & Elliott, R. G.
- Affiliations
- Pennsylvania State University (US)
- State University of New York at Oswego (US)
- Venue
- Human Factors: The Journal of the Human Factors and Ergonomics Society
- Publisher
- SAGE Publications
- Published
- 1990-06
- Volume
- 32
- Issue
- 3
- Pages
- 267-286
- ISSN
- 0018-7208 (ISSN-L)
- 1547-8181
- OpenAlex
- W1950622542
- Cited by
- 28
- References
- 0
- Citations by year
- 2021: 2 · 2015: 1 · 2014: 2 · 2012: 3
- FWCI
- 0.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Human Factors The Journal of the Human Factors and Ergonomics Society (publishedVersion)
- Funders
- Pennsylvania State University
- Army Research Institute for the Behavioral and Social Sciences
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Social Sciences › Psychology › Developmental and Educational Psychology › Sport Psychology and Performance
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
- Keywords
- Boolean functions, transfer of learning, boundary conditions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review