# Difference between revisions of "Accelerated future learning"

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Accelerated future learning measures: | 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 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. | Type B: Assessment data collection during future instruction (e.g., next on-line course unit) with treatment no longer in place. |

## Revision as of 17:22, 26 December 2006

### Accelerated future learning

*From Koedinger's slides at the PSLC lunch*:

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 a 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”).

*From the Refinment 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.