

Language skills are a stronger predictor of programming ability than math knowledge, according to a new University of Washington study. Here, study co-author Malayka Mottarella demonstrates coding in Python while wearing a specialized headset that measures electrical activity in the brain. Credit: Justin Abernethy/U. of Washington


New research from the University of Washington finds that a natural aptitude for learning languages is a stronger predictor of learning to program than basic math knowledge, or numeracy. That's because writing code also involves learning a , an ability to learn that language's vocabulary and grammar, and how they work together to communicate ideas and intentions. Other cognitive functions tied to both areas, such as and the use of working memory, also play key roles.

"Many barriers to programming, from prerequisite courses to stereotypes of what a good programmer looks like, are centered around the idea that programming relies heavily on math abilities, and that idea is not born out in our data," said lead author Chantel Prat, an associate professor of psychology at the UW and at the Institute for Learning & Brain Sciences. "Learning to program is hard, but is increasingly important for obtaining skilled positions in the workforce. Information about what it takes to be good at programming is critically missing in a field that has been notoriously slow in closing the gender gap."






To evaluate the neural and cognitive characteristics of "programming aptitude," Prat studied a group of native English speakers between the ages of 18 and 35 who had never learned to code.

This graph shows how the skills of study participants, such as numeracy and language aptitude, contribute to the learning of Python. According to the graph, cognition and language aptitude are greater predictors of learning than numeracy. Credit: Prat et al./Scientific Reports


"Ultimately, these resting-state brain metrics might be used as culture-free measures of how someone learns," Prat said.


To learn Python, the participants were assigned 10 45-minute online instruction sessions using the Codeacademy educational tool. Each session focused on a coding concept, such as lists or if/then conditions, and concluded with a quiz that a user needed to pass in order to progress to the next session. For help, users could turn to a "hint" button, an informational blog from past users and a "solution" button, in that order.

From a shared mirror screen, a researcher followed along with each participant and was able to calculate their "learning rate," or speed with which they mastered each lesson, as well as their quiz accuracy and the number of times they asked for help.


Ultimately, researchers found that scores from the language aptitude test were the strongest predictors of participants' learning rate in Python. Scores from tests in numeracy and fluid reasoning were also associated with Python learning rate, but each of these factors explained less variance than language aptitude did.

Presented another way, across learning outcomes, participants' language aptitude, fluid reasoning and working memory, and resting-state brain activity were all greater predictors of Python learning than was numeracy, which explained an average of 2% of the differences between people. Importantly, Prat also found that the same characteristics of resting-state brain data that previously explained how quickly someone would learn to speak French, also explained how quickly they would learn to code in Python.

"This is the first study to link both the neural and cognitive predictors of natural language aptitude to individual differences in learning programming languages. We were able to explain over 70% of the variability in how quickly different people learn to program in Python, and only a small fraction of that amount was related to numeracy," Prat said. Further research could examine the connections between aptitude and programming instruction in a classroom setting, or with more complex languages such as Java, or with more complicated tasks to demonstrate coding proficiency, Prat said.

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Journal information: Scientific Reports

Citation: Not a 'math person'? You may be better at learning to code than you think (2020, March 2) retrieved 25 July 2021 from https://phys.org/news/2020-03-math-person-code.html

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