Billets de Michelle Dawson
Liens cités structurés
Irene L. Graafsma, Eva Marinus, Serje Robidoux, Lyndsey Nickels, Nathan Caruana (2022). No evidence that autistic traits predict programming learning outcomes. Computers in Human Behavior Reports, 7, 100215. Elsevier BV.
Résumé
With the increased importance of computer programming in society, researchers have been searching for ways to predict which students are most likely to succeed, as well as those who may have difficulty when beginning to learn to program. It has been suggested that autistic traits relate to increased interest and aptitude for abstract science, and that people with higher numbers of autistic traits have a stronger tendency to ‘systemize’, which can be advantageous for studying natural and manmade systems. This could also mean that higher autistic traits are associated with greater programming abilities. In this study, we therefore investigated whether autistic traits, measured with the Autism Spectrum Quotient (AQ), predicted course grades and performance on an independent programming test at the end of an introductory undergraduate programming course. We also examined the relationship between AQ scores and five cognitive skills that were measured at the start of the programming course (logical reasoning, pattern recognition, algebra, vocabulary learning, grammar learning). We found that the participants scored higher on autistic traits than the general population. However, overall autistic traits did not predict programming skill at the end of the course. Similarly, no individual subscale of the AQ predicted programming skills, nor were there any correlations between cognitive skills and autistic traits. Therefore, there is no evidence to support autistic traits being reliably related to programming skill acquisition.
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