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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Relating Natural Language Aptitude to Individual Differences in Learning Programming Languages.

Chantel S Prat1,2,3,4, Tara M Madhyastha5,6, Malayka J Mottarella5

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Learning programming is like acquiring a second language. Language aptitude, not just numeracy, significantly predicts success in learning Python, offering a new perspective on programming education.

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Area of Science:

  • Cognitive Psychology
  • Neuroscience
  • Computer Science Education

Background:

  • The cognitive processes underlying programming acquisition are not fully understood.
  • Existing research often emphasizes numeracy, potentially overlooking other crucial aptitudes.

Purpose of the Study:

  • To investigate the parallels between learning modern programming languages and second natural language acquisition.
  • To identify key predictors of programming learning success, including language aptitude and cognitive abilities.

Main Methods:

  • An individual differences approach was used with 36 participants learning Python.
  • Predictors included behavioral and neural (resting-state EEG) measures of language aptitude, numeracy, and fluid cognitive abilities (reasoning, working memory, inhibition).
  • Outcomes measured were learning rate, programming accuracy, and declarative knowledge.

Main Results:

  • Models explained 50-72% of the variance in programming learning outcomes.
  • Language aptitude significantly predicted learning outcomes, even when controlling for other factors.
  • Fluid reasoning and working memory capacity were the strongest predictors (34%), followed by language aptitude (17%).
  • Resting-state EEG (beta and low-gamma bands) accounted for 10%, and numeracy for only 2%.

Conclusions:

  • Programming aptitude is strongly linked to language learning abilities, challenging the overemphasis on numeracy.
  • A novel framework suggests that cognitive and linguistic factors are paramount for understanding programming acquisition.
  • Educational approaches may benefit from incorporating language learning principles and focusing on broader cognitive skills.