Relating Natural Language Aptitude to Individual Differences in Learning Programming Languages

This experiment employed an individual differences approach to test the hypothesis that learning modern programming languages resembles second “natural” language learning in adulthood. Behavioral and neural (resting-state EEG) indices of language aptitude were used along with numeracy and fluid cogn...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports Vol. 10; no. 1; p. 3817
Main Authors: Prat, Chantel S., Madhyastha, Tara M., Mottarella, Malayka J., Kuo, Chu-Hsuan
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 02.03.2020
Nature Publishing Group
Subjects:
631/378/1595/1636
631/378/2649/1594
631/477/2811
Adolescent
Adult
Aptitude - physiology
Cognitive ability
EEG
Electroencephalography
Female
Humanities and Social Sciences
Humans
Individuality
Learning
Learning - physiology
Male
Memory
multidisciplinary
Programming Languages
Science
Science (multidisciplinary)
Short term memory
Young Adult
ISSN:2045-2322, 2045-2322
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This experiment employed an individual differences approach to test the hypothesis that learning modern programming languages resembles second “natural” language learning in adulthood. Behavioral and neural (resting-state EEG) indices of language aptitude were used along with numeracy and fluid cognitive measures (e.g., fluid reasoning, working memory, inhibitory control) as predictors. Rate of learning, programming accuracy, and post-test declarative knowledge were used as outcome measures in 36 individuals who participated in ten 45-minute Python training sessions. The resulting models explained 50–72% of the variance in learning outcomes, with language aptitude measures explaining significant variance in each outcome even when the other factors competed for variance. Across outcome variables, fluid reasoning and working-memory capacity explained 34% of the variance, followed by language aptitude (17%), resting-state EEG power in beta and low-gamma bands (10%), and numeracy (2%). These results provide a novel framework for understanding programming aptitude, suggesting that the importance of numeracy may be overestimated in modern programming education environments.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-020-60661-8