Difference between revisions of "Optimized scheduling"

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(Conditions of application)
(Theoretical rationale)
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===In vivo experiment support===
 
===In vivo experiment support===
 
==Theoretical rationale==  
 
==Theoretical rationale==  
Optimized scheduling is often a method for providing optimal [[repetition]]. The optimized scheduling of Pavlik (2005; 2007)balances the speed (reduced time cost of practice) advantage of [[recency]] with the long-term learning advantage of [[spaced practice]].
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Optimized scheduling is often a method for providing optimal [[repetition]]. The optimized scheduling of Pavlik (2005; 2007) balances the speed (reduced time cost of practice) advantage of [[recency]] with the long-term learning advantage of [[spaced practice]]. This speed advantage typically occurs for [[drill practice]] because more recent drill practice has fewer failures and therefore less need for costly review practice.
  
 
==Conditions of application==
 
==Conditions of application==

Revision as of 16:36, 2 December 2007


Brief statement of principle

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.

Description of principle

Operational definition

Scheduling practice to maximize some future measure of learning given a fixed time of current practice.

Examples

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).

Experimental support

See references.

Laboratory experiment support

In vivo experiment support

Theoretical rationale

Optimized scheduling is often a method for providing optimal repetition. The optimized scheduling of Pavlik (2005; 2007) balances the speed (reduced time cost of practice) advantage of recency with the long-term learning advantage of spaced practice. This speed advantage typically occurs for drill practice because more recent drill practice has fewer failures and therefore less need for costly review practice.

Conditions of application

A limiting condition of using the optimized scheduling of Pavlik is that it relies on a recency advantage for practice. Without this recency advantage, it is often true that maximal spacing is optimal as suggested in the practice guidelines. For example, if each practice trial has a fixed duration this will result in no recency advanatge and maximal (or very wide) spacing will be optimal. However, many procedures uses the test trials since the testing effect has shown that tests result in stronger learning tha passive study. Tests often have a strong advantage when they occur with greater recency since this recency reduces the need for review in the case of failure.

Caveats, limitations, open issues, or dissenting views

Many studies of spacing effects (e.g. Pashler, 2003) come to incorrect conclusions about optimal scheduling because they fail to control for time on task. This is a pervasive flaw and dozens of examples can be cited.

Variations (descendants)

Generalizations (ascendants)

References

  • Pavlik Jr., P. I. (2005). The microeconomics of learning: Optimizing paired-associate memory. Dissertation Abstracts International: Section B: The Sciences and Engineering, 66(10-B), 5704.
  • Pavlik Jr., P. I. (2007). Timing is an order: Modeling order effects in the learning of information. In F. E., Ritter, J. Nerb, E. Lehtinen & T. O'Shea (Eds.), In order to learn: How order effects in machine learning illuminate human learning (pp. 137-150). New York: Oxford University Press.
  • Pavlik Jr., P. I., Presson, N., Dozzi, G., Wu, S.-m., MacWhinney, B., & Koedinger, K. R. (2007). The FaCT (Fact and Concept Training) System: A new tool linking cognitive science with educators. In D. McNamara & G. Trafton (Eds.), Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society (pp. 397-402). Mahwah, NJ: Lawrence Erlbaum.
  • Pavlik Jr., P. I., Presson, N., & Koedinger, K. R. (2007). Optimizing knowledge component learning using a dynamic structural model of practice. In R. Lewis & T. Polk (Eds.), Proceedings of the Eighth International Conference of Cognitive Modeling. Ann Arbor: University of Michigan.
  • Pashler, H., Zarow, G., & Triplett, B. (2003). Is temporal spacing of tests helpful even when it inflates error rates? Journal of Experimental Psychology: Learning, Memory, and Cognition, 29(6), 1051-1057.