· ACM Transactions on Software Engineering and Methodology· 2 citations· 77 references
TL;DR
An eye-tracking study with 49 novice programmers to compare how they comprehend FP (Racket) and IP (C++) during tasks and offers insights to inform tools, pedagogy, and strategies for making Racket and related FP instruction more approachable in similar educational settings.
While LLM-rewritten messages significantly improved subjective evaluations, with pragmatic messages rated as clearer and less cognitively demanding, these perceived gains did not translate into statistically significant improvements in objective debugging performance.
Alexandru-Radu Moraru, Shreyan Biswas, U. Gadiraju· 0 citations
Programming attracts a diverse range of learners, including individuals with dyslexia, a learning difficulty that affects reading, writing, and spelling despite average intelligence and educational opportunities. Although prior work has examined dyslexia in natural-language and code comprehension, we still know little...
Zi-Han Fang, Janice Chung, Rui-Jia Chen et al.· Proceedings of the 41st IEEE...· 0 citations
Generative artificial intelligence is reshaping programming education, yet its effects on skill development depend partly on how learners interact with artificial intelligence-supported systems. This study introduces the Artificial Intelligence-Scaffolding Interaction Framework, which conceptualizes constrained, questi...
M. Yenidogan, Zeynep Cömert, Duygu Çakır· IEEE Access· 0 citations
Contemporary software systems place greater demands on conceptual flexibility and paradigm diversity, necessitating a better understanding of how developers acquire proficiency across different programming paradigms. Therefore, this exploratory study provides empirical evidence of the learning experiences of developers...
Alfred Aminayanate Bob-Manuel· Discover Education· 0 citations
Generative AI can improve students'programming performance, but successful task completion may not reflect what they retain. We examined performance, retention, cognitive load, and ownership in a controlled between-subjects experiment with 59 undergraduate computer science students, 55 were retained for analysis. Parti...
Christian Bergh, B. Tag, Alexandra Vassar et al.· 0 citations
Validating AI contributions to scientific code rested largely on individual judgment, outside shared infrastructure for testing or review, and interfaces could support task-appropriate evaluation rather than leave it to the user.
Gabrielle O'Brien, Reed Milewicz, Nasir U. Eisty· 0 citations
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