Teaching Academic Honesty in CS50
Abstract
We aspire to teach academic honesty in CS50 at Harvard University not only by addressing academic dishonesty when it occurs but by addressing it before it does. By way of communication, course- and campus-wide awareness of policy, just-in-time prompts, interventional conversations, and problem sets have we tried to preempt submission of plagiarized work. But few interventions have had significant or lasting effects on the number of instances thereof. Most impactful has been the addition of one sentence to the course's syllabus, a "regret clause" that encourages students to come forward within 72 hours of some dishonest act on their part, before the course itself is even aware. While we might zero the work in question in such cases, we commit to not escalating the matter further to the university's honor council, where the outcome might instead be admonishment, probation, or even required withdrawal from the university itself. We instead advise students on how best to move forward and connect them as needed with support structures on campus for academics and mental health. Since 2014 have 89 students invoked the clause, between 1% and 3% of the course's student body each year.
Integrity signals
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Cites
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Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Highlighting institutional plagiarism policies and highlighting academic good practice has been associated with reduced plagiarism in CS courses.
“In such cases, highlighting institutional plagiarism policies and highlighting academic good practice has been associated with reduced plagiarism in CS courses [32,34].”
From Bob or Bot: Exploring ChatGPT's Answers to University Computer Science Assessment (2024) · cites this paper as [32]
The citation lists haven’t been fetched yet
References 12 · Cited by 15
- Authors
- Malan, D. J., Yu, B., & Lloyd, D.
- Affiliations
- Harvard University (US)
- Venue
- Proceedings of the 51st ACM Technical Symposium on Computer Science Education
- Publisher
- ACM
- Published
- 2020-02-26
- Pages
- 282-288
- OpenAlex
- W3007438360
- Cited by
- 15
- References
- 12
- Citations by year
- 2024: 5 · 2023: 4 · 2022: 1 · 2021: 3 · 2020: 1 · 2019: 1
- FWCI
- 2.33
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Technical Symposium on Computer Science Education (SIGCSE) (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion)
- Topics
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Social Sciences › Decision Sciences › Information Systems and Management › Ethics in Business and Education
- Social Sciences › Law › Legal Issues in Education
- Keywords
- academic integrity, academic dishonesty, plagiarism avoidance, student interventions, honor codes, computer science education, student support services
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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