Difference between revisions of "Optimized scheduling"
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[[Category:PSLC General]] | [[Category:PSLC General]] | ||
| − | + | ==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. | 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. 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. | ||
| + | ===Examples=== | ||
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]]). | ||
| + | ==Experimental support== | ||
| + | See references. | ||
| + | ===Laboratory experiment support=== | ||
| + | ===In vivo experiment support=== | ||
| + | ==Theoretical rationale== | ||
| + | (These entries should link to one or more [[:Category:Learning Processes|learning processes]].) | ||
| + | ==Conditions of application== | ||
| + | ==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. | ||
Revision as of 21:05, 2 December 2007
Contents
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. 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.
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
(These entries should link to one or more learning processes.)
Conditions of application
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.
