(For the full article, click on the link at the end of this blog post):
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.
Results: Taken together, the results reported provide foundational information about the neurocognitive characteristics of “high aptitude” learners of Python, and by virtue, about who may struggle given equal access to learning environments. We argue, as have others before us2,6, that both educational and engineering practices have proceeded without this critical knowledge about why, and for whom, learning to program is difficult. Contrary to widely held stereotypes, the “computer whisperers” investigated herein were facile problem solvers with a high aptitude for natural languages. Although numeracy was a reliable predictor of programming aptitude, it was far from the most significant predictor. Importantly, this research also begins the process of identifying the neural characteristics of individual differences in Python learning aptitude, which can be used as targets for technologies such as neurofeedback and neurostimulation that modify patterns of connectivity and alter corresponding behaviors33,34.
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