# Difference between revisions of "Optimized scheduling"

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Applying an [[instructional schedule]] that has been ordered to maximize [[robust learning]]. Mathematical models may often be used to produce optimized schedules by computing the knowledge component that will be most efficiently learned if practiced next. | Applying an [[instructional schedule]] that has been ordered to maximize [[robust learning]]. Mathematical models may often be used to produce optimized schedules by computing the knowledge component that will be most efficiently learned if practiced next. | ||

Examples of optimized scheduling include [[learning event scheduling]] (see [[Optimizing the practice schedule|Pavlik's study]]), the [[knowledge tracing]] algorithm used in [[Cognitive Tutors]] (see [[Cen's study]]), and adaptive [[fading]] of [[scaffolding]] or [[assistance]] (see [[Does learning from worked-out examples improve tutored problem solving? |Renkl's study]]). | Examples of optimized scheduling include [[learning event scheduling]] (see [[Optimizing the practice schedule|Pavlik's study]]), the [[knowledge tracing]] algorithm used in [[Cognitive Tutors]] (see [[Cen's study]]), and adaptive [[fading]] of [[scaffolding]] or [[assistance]] (see [[Does learning from worked-out examples improve tutored problem solving? |Renkl's study]]). |

## Revision as of 16:57, 2 December 2007

Applying an instructional schedule that has been ordered to maximize robust learning. Mathematical models may often be used to produce optimized schedules by computing the knowledge component that will be most efficiently learned if practiced next.

Examples of optimized scheduling include learning event scheduling (see Pavlik's study), the knowledge tracing algorithm used in Cognitive Tutors (see Cen's study), and adaptive fading of scaffolding or assistance (see Renkl's study).