Difference between revisions of "Accelerated future learning"
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| − | + | We can distinguish two types of accelerated future learning measures: | |
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Type A: Complex test items that teach a new [[knowledge component]] on the test & then ask students to apply it. | Type A: Complex test items that teach a new [[knowledge component]] on the test & then ask students to apply it. | ||
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Both involve instruction on new knowledge. | Both involve instruction on new knowledge. | ||
| − | For example in algebra, given the instructional problems are something like: 3x + 10 = 20, 14 - 2x = 40, 25 = 5x - 12 ... then an accelerated future learning measure would involve new instruction that provides | + | For example in algebra, given the instructional problems are something like: 3x + 10 = 20, 14 - 2x = 40, 25 = 5x - 12 ... then an accelerated future learning measure would involve new instruction that provides an annotated worked example of solving a new type of problem like “3x + 8 = 4x + 2” and then gives students similar problems like "9 + 4x = 7x - 4". To distinguish accelerated future learning from [[transfer]], a study also needs a contrast with student performance on the target items (e.g., "9 + 4x = 7x - 4") without the new instruction (e.g., the annotated worked example of solving “3x + 8 = 4x + 2”). |
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| + | A related idea is preparation for future learning (Bransford & Schwartz, 1999). | ||
| + | ''From the Refinement and Fluency cluster'': | ||
| − | + | Learning that proceeds more effectively and more rapidly because of prior learning. It differs from transfer in its putative generality, not dependent on encounters with similar materials that require similar procedures (transfer). It may include what are called “learning to learn” skills. However, by hypothesis the robust learning produces accelerated learning through component competencies or through gains in efficiency that arise from procedures (e.g. [[chunking]]) that can apply to new learning. These procedures do not imply the use of deliberate [[sense making]] [[strategies]] associated with “learning to learn” -- such would be in the domain of one of the sense-making research clusters. | |
| − | + | === References === | |
| + | * Bransford, J. D., & Schwartz, D. (1999). Rethinking transfer: A simple proposal with multiple implications. In A. Iran-Nejad & P. D. Pearson (Eds.), Review of Research in Education (Vol. 24). Washington, DC: American Educational Research Association. | ||
[[Category:Glossary]] | [[Category:Glossary]] | ||
[[Category:Dependent Variables]] | [[Category:Dependent Variables]] | ||
| + | [[Category:Instructional Outcome]] | ||
[[Category:PSLC General]] | [[Category:PSLC General]] | ||
| + | [[Category:IPL]] | ||
Latest revision as of 17:37, 31 October 2008
We can distinguish two types of accelerated future learning measures:
Type A: Complex test items that teach a new knowledge component on the test & then ask students to apply it.
Type B: Assessment data collection during future instruction (e.g., next on-line course unit) with treatment no longer in place.
Both involve instruction on new knowledge.
For example in algebra, given the instructional problems are something like: 3x + 10 = 20, 14 - 2x = 40, 25 = 5x - 12 ... then an accelerated future learning measure would involve new instruction that provides an annotated worked example of solving a new type of problem like “3x + 8 = 4x + 2” and then gives students similar problems like "9 + 4x = 7x - 4". To distinguish accelerated future learning from transfer, a study also needs a contrast with student performance on the target items (e.g., "9 + 4x = 7x - 4") without the new instruction (e.g., the annotated worked example of solving “3x + 8 = 4x + 2”).
A related idea is preparation for future learning (Bransford & Schwartz, 1999).
From the Refinement and Fluency cluster:
Learning that proceeds more effectively and more rapidly because of prior learning. It differs from transfer in its putative generality, not dependent on encounters with similar materials that require similar procedures (transfer). It may include what are called “learning to learn” skills. However, by hypothesis the robust learning produces accelerated learning through component competencies or through gains in efficiency that arise from procedures (e.g. chunking) that can apply to new learning. These procedures do not imply the use of deliberate sense making strategies associated with “learning to learn” -- such would be in the domain of one of the sense-making research clusters.
References
- Bransford, J. D., & Schwartz, D. (1999). Rethinking transfer: A simple proposal with multiple implications. In A. Iran-Nejad & P. D. Pearson (Eds.), Review of Research in Education (Vol. 24). Washington, DC: American Educational Research Association.
