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. 2020 Mar 2;10(1):3817.
doi: 10.1038/s41598-020-60661-8.

Relating Natural Language Aptitude to Individual Differences in Learning Programming Languages

Chantel S Prat  1   2   3   4 , Tara M Madhyastha  5   6 , Malayka J Mottarella  5 , Chu-Hsuan Kuo  5
Affiliations

Relating Natural Language Aptitude to Individual Differences in Learning Programming Languages

Chantel S Prat et al. Sci Rep. .

Abstract

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.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Individual differences in rate of learning to program in Python through Codecademy. (A) Individual learning rates computed by regressing last lesson completed during each of 10 training sessions. Each color represents an individual participant, ordered according to the visual light spectrum, ranging from red for the fastest learner, through violet for the slowest. (B–D) Scatterplots depict the relation between rate of learning on Y axis and (B) Language aptitude as measured by the MLAT, (C) Numeracy, as measured by the Abbreviated Numeracy Scale, and (D) Fluid reasoning, as measured by the Raven’s Advanced Progressive Matrices.
Figure 2
Figure 2
Topomaps displaying the correlations between resting-state EEG power and Python learning outcomes across electrode locations and scatterplots showing data concatenated across right fronto-temporal networks (F8, FC6, T8). (A) Correlations between mean beta power (13–29.5 Hz) and Python learning rate across channels, with the relation between mean fronto-temporal beta power and learning rate depicted in scatter plot. (B) Correlations between mean low-gamma power (30–40 Hz) and post-test declarative knowledge across channels, with the relation between mean fronto-temporal low-gamma power and declarative knowledge depicted in scatter plot.
Figure 3
Figure 3
Percentage of total variance explained in stepwise regression analyses of three Python learning outcomes by general cognitive measures (fluid reasoning and working memory), in red, language aptitude (salmon), resting-state EEG (beige), and numeracy (light blue). Unexplained variance is in dark blue. Average predictive utility across outcome variables appears in right-most column.
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