Same Rules, Mixed Messages: Exploring Community Perceptions of Academic Dishonesty in Computing Education
Chandler C. Payne, Kai A. Hackney, Lucas Guarenti Zangari, Sterling R. Kalogeras, Emmanuel Munoz, Juan Sebasti\'an S\'anchez-G\'omez, Olufisayo Omojokun, and Pedro Guillermo Feij\'oo-Garc\'ia

TL;DR
This study explores how different roles in computing education perceive academic dishonesty, revealing significant perception gaps influenced by the rise of online learning and AI tools.
Contribution
It provides empirical insights into varying perceptions of cheating among instructors, TAs, and students, emphasizing the need for clearer communication and curriculum strategies.
Findings
Instructors cite grade pressure and laziness as main motivations.
Students and TAs point to knowledge gaps and time management issues.
Perceptions of cheating differ significantly across roles.
Abstract
Academic dishonesty has long been a concern in computing education, and the rapid growth of online learning and generative artificial intelligence (AI) has further complicated how cheating is perceived and addressed. We report on a study examining how different actors in the computer science (CS) classroom interpret potential cheating scenarios and the motivations behind academic dishonesty. Participants included instructors (n = 6), teaching assistants (TAs; n = 21), and undergraduate students (n = 538) enrolled in two CS courses at a large Southeastern institution in the United States. Respondents classified scenarios as serious cheating, trivial cheating, or not cheating and answered to an open-ended question about motivations for academic dishonesty. Our findings reveal notable discrepancies across groups: instructors most often attribute cheating to grade pressure and laziness,…
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