Gesture during math instruction specifically benefits learners with high visuospatial working memory capacity.


Journal

Cognitive research: principles and implications
ISSN: 2365-7464
Titre abrégé: Cogn Res Princ Implic
Pays: England
ID NLM: 101697632

Informations de publication

Date de publication:
09 06 2020
Historique:
received: 01 10 2019
accepted: 12 02 2020
entrez: 11 6 2020
pubmed: 11 6 2020
medline: 22 6 2021
Statut: epublish

Résumé

Characteristics of both teachers and learners influence mathematical learning. For example, when teachers use hand gestures to support instruction, students learn more than others who learn the same concept with only speech, and students with higher working memory capacity (WMC) learn more rapidly than those with lower WMC. One hypothesis for the effect of gesture on math learning is that gestures provide a signal to learners that can reduce demand on working memory resources during learning. However, it is not known what sort of working memory resources support learning with gesture. Gestures are motoric; they co-occur with verbal language and they are perceived visually. In two studies, we investigated the relationship between mathematical learning with or without gesture and individual variation in verbal, visuospatial, and kinesthetic WMC. Students observed a videotaped lesson in a novel mathematical system that either included instruction with both speech and gesture (Study 1) or instruction with only speech (Study 2). After instruction, students solved novel problems in the instructed system and transfer problems in a related system. Finally, students completed verbal, visuospatial, and kinesthetic working memory assessments. There was a positive relationship between visuospatial WMC and math learning when gesture was present, but no relationship between visuospatial WMC and math learning when gesture was absent. Rather, when gesture was absent, there was a relationship between verbal WMC and math learning. Providing gesture during instruction appears to change the cognitive resources recruited when learning a novel math task.

Sections du résumé

BACKGROUND
Characteristics of both teachers and learners influence mathematical learning. For example, when teachers use hand gestures to support instruction, students learn more than others who learn the same concept with only speech, and students with higher working memory capacity (WMC) learn more rapidly than those with lower WMC. One hypothesis for the effect of gesture on math learning is that gestures provide a signal to learners that can reduce demand on working memory resources during learning. However, it is not known what sort of working memory resources support learning with gesture. Gestures are motoric; they co-occur with verbal language and they are perceived visually.
METHODS
In two studies, we investigated the relationship between mathematical learning with or without gesture and individual variation in verbal, visuospatial, and kinesthetic WMC. Students observed a videotaped lesson in a novel mathematical system that either included instruction with both speech and gesture (Study 1) or instruction with only speech (Study 2). After instruction, students solved novel problems in the instructed system and transfer problems in a related system. Finally, students completed verbal, visuospatial, and kinesthetic working memory assessments.
RESULTS
There was a positive relationship between visuospatial WMC and math learning when gesture was present, but no relationship between visuospatial WMC and math learning when gesture was absent. Rather, when gesture was absent, there was a relationship between verbal WMC and math learning.
CONCLUSION
Providing gesture during instruction appears to change the cognitive resources recruited when learning a novel math task.

Identifiants

pubmed: 32519045
doi: 10.1186/s41235-020-00215-8
pii: 10.1186/s41235-020-00215-8
pmc: PMC7283399
doi:

Types de publication

Journal Article Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

27

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Auteurs

Mary Aldugom (M)

Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, 52242, USA. mary-aldugom@uiowa.edu.

Kimberly Fenn (K)

Department of Psychology, Michigan State University, East Lansing, MI, USA.

Susan Wagner Cook (SW)

Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, 52242, USA.

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Classifications MeSH