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Eliciting explanations: Constraints on when self-explanation aids learning

Overview of attention for article published in Psychonomic Bulletin & Review, July 2016
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Title
Eliciting explanations: Constraints on when self-explanation aids learning
Published in
Psychonomic Bulletin & Review, July 2016
DOI 10.3758/s13423-016-1079-5
Pubmed ID
Authors

Bethany Rittle-Johnson, Abbey M. Loehr

Abstract

Generating explanations for oneself in an attempt to make sense of new information (i.e., self-explanation) is often a powerful learning technique. Despite its general effectiveness, in a growing number of studies, prompting for self-explanation improved some aspects of learning, but reduced learning of other aspects. Drawing on this recent research, as well as on research comparing self-explanation under different conditions, we propose four constraints on the effectiveness of self-explanation. First, self-explanation promotes attention to particular types of information, so it is better suited to promote particular learning outcomes in particular types of domains, such as transfer in domains guided by general principles or heuristics. Second, self-explaining a variety of types of information can improve learning, but explaining one's own solution methods or choices may reduce learning under certain conditions. Third, explanation prompts focus effort on particular aspects of the to-be-learned material, potentially drawing effort away from other important information. Explanation prompts must be carefully designed to align with target learning outcomes. Fourth, prompted self-explanation often promotes learning better than unguided studying, but alternative instructional techniques may be more effective under some conditions. Attention to these constraints should optimize the effectiveness of self-explanation as an instructional technique in future research and practice.

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Geographical breakdown

Country Count As %
United States 3 3%
Unknown 97 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 24%
Student > Master 12 12%
Researcher 11 11%
Student > Bachelor 7 7%
Student > Doctoral Student 6 6%
Other 24 24%
Unknown 16 16%
Readers by discipline Count As %
Psychology 29 29%
Social Sciences 17 17%
Computer Science 5 5%
Arts and Humanities 5 5%
Mathematics 4 4%
Other 20 20%
Unknown 20 20%