Difference between revisions of "Understanding paired associate transfer effects based on shared stimulus components"

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== Applying optimal scheduling of practice in the Chinese Learnlab ==
  
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=== Abstract ===
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=== Glossary ===
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* [[Optimal Spacing Interval]]
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* [[Expanding Spacing Interval]]
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* [[Wide Spacing Interval]]
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* [[Narrow Spacing Interval]]
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=== Research question ===
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How can the optimal sequence of learning be computed?
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=== Background and significance ===
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=== Dependent variables ===
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Measures of normal and robust learning.
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=== Independent variables ===
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Alternative structures of [[instructional schedule]] for [[knowledge component training]] based on the predictions of an ACT-R based cognitive model. Further independent variables include how the material is presented for each learning event and the assumptions of the model used to presents schedule the learning. The assumptions of the model include alternative analyses of task demands, the structure of relevant knowledge components, and learner background.
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=== Hypothesis ===
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Robust learning is increased by instructional activities that require the learner to  attend to the relevant knowledge components of a learning task.
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=== Findings ===
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=== Explanation ===
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Attention to features of the task domain as a knowledge component is processed leads to associating those features with the knowledge component.  If the features are valid, then forming or strengthening such associations facilitates retrieval during subsequent assessment or instruction, and thus leads to more robust learning.
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=== Descendents ===
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=== Annotated bibliography ===
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Forthcoming
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[[Category:Study]]
  
 
[[Category:Study]]
 
[[Category:Study]]

Revision as of 16:15, 14 September 2006

Applying optimal scheduling of practice in the Chinese Learnlab

Abstract

Glossary

Research question

How can the optimal sequence of learning be computed?

Background and significance

Dependent variables

Measures of normal and robust learning.

Independent variables

Alternative structures of instructional schedule for knowledge component training based on the predictions of an ACT-R based cognitive model. Further independent variables include how the material is presented for each learning event and the assumptions of the model used to presents schedule the learning. The assumptions of the model include alternative analyses of task demands, the structure of relevant knowledge components, and learner background.

Hypothesis

Robust learning is increased by instructional activities that require the learner to attend to the relevant knowledge components of a learning task.

Findings

Explanation

Attention to features of the task domain as a knowledge component is processed leads to associating those features with the knowledge component. If the features are valid, then forming or strengthening such associations facilitates retrieval during subsequent assessment or instruction, and thus leads to more robust learning.

Descendents

Annotated bibliography

Forthcoming