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Applying math onto mechanisms: mechanistic knowledge is associated with the use of formal mathematical strategies

Overview of attention for article published in Cognitive Research: Principles and Implications, January 2017
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Title
Applying math onto mechanisms: mechanistic knowledge is associated with the use of formal mathematical strategies
Published in
Cognitive Research: Principles and Implications, January 2017
DOI 10.1186/s41235-016-0044-1
Pubmed ID
Authors

Allison S. Liu, Christian D. Schunn

Abstract

It is notoriously difficult for people to adaptively apply formal mathematical strategies learned in school to real-world contexts, even when they possess the required mathematical skills. The current study explores whether a problem context's mechanism can act as an "embodied analogy" onto which abstract mathematical concepts can be applied, leading to more frequent use of formal mathematical strategies. Participants were asked to program a robot to navigate a maze and to create a navigation strategy that would work for differently sized robots. We compared the strategy complexity of participants with high levels of mechanistic knowledge about the robot against participants with low levels of mechanistic knowledge about the robot. Mechanistic knowledge was significantly associated with the frequency and complexity of the mathematical strategies used by participants, suggesting that learning to recognize a problem context's mechanism may promote independent mathematical problem solving in applied contexts.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 31 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 13%
Student > Ph. D. Student 4 13%
Student > Doctoral Student 3 10%
Lecturer 2 6%
Student > Bachelor 2 6%
Other 5 16%
Unknown 11 35%
Readers by discipline Count As %
Psychology 4 13%
Social Sciences 3 10%
Mathematics 3 10%
Computer Science 3 10%
Arts and Humanities 2 6%
Other 5 16%
Unknown 11 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 March 2017.
All research outputs
#14,924,861
of 22,957,478 outputs
Outputs from Cognitive Research: Principles and Implications
#250
of 319 outputs
Outputs of similar age
#242,837
of 420,093 outputs
Outputs of similar age from Cognitive Research: Principles and Implications
#14
of 17 outputs
Altmetric has tracked 22,957,478 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 319 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 44.0. This one is in the 20th percentile – i.e., 20% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 420,093 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.