Exploring Gender Bias in Remote Pair Programming among Software Engineering Students: The twincode Original Study and First External Replication
Amador Durán; Pablo Fernández; Beatriz Bernárdez; Nathaniel Weinman; Aslıhan Akalın; Armando Fox · 2023 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
Context. Software Engineering (SE) has low female representation due to gender bias that men are better at programming. Pair programming (PP) is common in industry and can increase student interest in SE, especially women; but if gender bias affects PP, it may discourage women from joining the field. Objective. We explore gender bias in PP. In a remote setting where students cannot see their peers' gender, we study how perceived productivity, technical competency and collaboration/interaction behaviors of SE students vary by perceived gender of their remote partner. Method. We developed an onl
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Metadata source: arXiv