No evidence that autistic traits predict programming learning outcomes

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

Computer programming skills: A cognitive perspective

The studies in the current thesis examine programming skills from a broad cognitive perspective. Study 1 aims to validate two short versions of an existing programming test. Studies 2 and 3 ask whether cognitive skills and autistic traits predict programming performance in an undergraduate course. The results show that logical reasoning is the most reliable predictor of programming skill and that autistic traits do not predict programming performance. In the final study, brain activity is measured in an EEG experiment. The results show that a programming language may be processed similarly in the brain to natural languages

Irene Graafsma's Quick Files

The Quick Files feature was discontinued and it’s files were migrated into this Project on March 11, 2022. The file URL’s will still resolve properly, and the Quick Files logs are available in the Project’s Recent Activity

The cognition of programming:logical reasoning, algebra and vocabulary skills predict programming performance following an introductory computing course

In the current study we aimed to determine which cognitive skills play a role when learning to program. We examined five cognitive skills (pattern recognition, algebra, logical reasoning, grammar learning and vocabulary learning) as predictors of course-related programming performance and their generalised programming performance in 282 students in an undergraduate introductory programming course. Initial skills in algebra, logical reasoning, and vocabulary learning predicted performance for generalised programming skill, while only logical reasoning skills predicted course-related programming performance. Structural equation modelling showed support for a model where the cognitive skills were grouped into a language factor and an algorithmic/mathematics factor. Of these two factors, only the algorithmic/mathematics factor was found to predict generalised and course-related programming skills. Our results suggested that algorithmic/mathematical skills are most relevant when predicting generalised programming success, but also showed a role for memory-related language skills.</p

The cognition of programming:logical reasoning, algebra and vocabulary skills predict programming performance following an introductory computing course

In the current study we aimed to determine which cognitive skills play a role when learning to program. We examined five cognitive skills (pattern recognition, algebra, logical reasoning, grammar learning and vocabulary learning) as predictors of course-related programming performance and their generalised programming performance in 282 students in an undergraduate introductory programming course. Initial skills in algebra, logical reasoning, and vocabulary learning predicted performance for generalised programming skill, while only logical reasoning skills predicted course-related programming performance. Structural equation modelling showed support for a model where the cognitive skills were grouped into a language factor and an algorithmic/mathematics factor. Of these two factors, only the algorithmic/mathematics factor was found to predict generalised and course-related programming skills. Our results suggested that algorithmic/mathematical skills are most relevant when predicting generalised programming success, but also showed a role for memory-related language skills.</p

A multiple-response frequency-tagging paradigm measures graded changes in consciousness during perceptual filling-in

Perceptual filling-in (PFI) occurs when a physically present visual target disappears from conscious perception, with its location filled-in by the surrounding visual background. These perceptual changes are complete, near instantaneous, and can occur for multiple separate locations simultaneously. Here, we show that contrasting neural activity during the presence or absence ofmulti-target PFI can complement other findings frommultistable phenomena to reveal the neural correlates of consciousness (NCC).We presented four peripheral targets over a background dynamically updating at 20Hz.While participants reported on target disappearances/reappearances via button press/release, we tracked neural activity entrained by the background during PFI using steady-state visually evoked potentials (SSVEPs) recorded in the electroencephalogram. We found background SSVEPs closely correlated with subjective report, and increased with an increasing amount of PFI. Unexpectedly, we found that as the number of filled-in targets increased, the duration of target disappearances also increased, suggesting that facilitatory interactions exist between targets in separate visual quadrants.We also found distinct spatiotemporal correlates for the background SSVEP harmonics. Prior to genuine PFI, the response at the second harmonic (40Hz) increased before the first (20Hz), which we tentatively link to an attentional effect, while no such difference between harmonics was observed for physically removed stimuli. These results demonstrate that PFI can be used to studymulti-object perceptual suppression when frequency-tagging the background of a visual display, and because there are distinct neural correlates for endogenously and exogenously induced changes in consciousness, that it is ideally suited to study the NCC