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Ask Me Anything: Assessing Academic Dishonesty

Published: 26 February 2020 Publication History

Abstract

We provide a method for assessing self-reported rates of cheating among students. The method is both i) privacy-preserving in the sense that one cannot use answers as evidence that any particular student cheated and ii) non-anonymous in the sense that one can record each student's answer for use in future correlative studies. Because accuracy relies on students' willful participation, we describe how to convince students that they take no risk by taking the survey. This method showed that 42% of 847 students willfully cheated in an Algorithms course. Surveying 181 CS Theory students showed no difference in cheating rates on written vs. coding assignments.

References

[1]
Saul Schleimer, Daniel S Wilkerson, and Alex Aiken. Winnowing: local algorithms for document fingerprinting. In SIGMOD 2003, pages 76--85. ACM, 2003.
[2]
Stanley Warner. Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 60(309):63--69, 1965.
[3]
Lisa Yan, Nick McKeown, and Mehran Sahami, Mehran Chris Piech. Tmoss: Using intermediate assignment work to understand excessive collaboration in large classes. In SIGCSE 2018, pages 110--115. ACM, 2018.

Cited By

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  • (2024)Bob or Bot: Exploring ChatGPT's Answers to University Computer Science AssessmentACM Transactions on Computing Education10.1145/363328724:1(1-32)Online publication date: 14-Jan-2024
  • (2024)AVERT (Authorship Verification and Evaluation Through Responsive Testing): an LLM-Based Procedure that Interactively Verifies Code Authorship and Evaluates Student Understanding2024 21st International Conference on Information Technology Based Higher Education and Training (ITHET)10.1109/ITHET61869.2024.10837675(1-7)Online publication date: 6-Nov-2024
  • (2022)Online Examinations in a Large Australian CS1 CourseProceedings of the 24th Australasian Computing Education Conference10.1145/3511861.3511864(20-26)Online publication date: 14-Feb-2022

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cover image ACM Conferences
SIGCSE '20: Proceedings of the 51st ACM Technical Symposium on Computer Science Education
February 2020
1502 pages
ISBN:9781450367936
DOI:10.1145/3328778
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 February 2020

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Author Tags

  1. academic integrity
  2. cheating
  3. learning environment
  4. undergraduate instruction

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SIGCSE '20
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Overall Acceptance Rate 1,787 of 5,146 submissions, 35%

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SIGCSE TS 2025
The 56th ACM Technical Symposium on Computer Science Education
February 26 - March 1, 2025
Pittsburgh , PA , USA

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Cited By

View all
  • (2024)Bob or Bot: Exploring ChatGPT's Answers to University Computer Science AssessmentACM Transactions on Computing Education10.1145/363328724:1(1-32)Online publication date: 14-Jan-2024
  • (2024)AVERT (Authorship Verification and Evaluation Through Responsive Testing): an LLM-Based Procedure that Interactively Verifies Code Authorship and Evaluates Student Understanding2024 21st International Conference on Information Technology Based Higher Education and Training (ITHET)10.1109/ITHET61869.2024.10837675(1-7)Online publication date: 6-Nov-2024
  • (2022)Online Examinations in a Large Australian CS1 CourseProceedings of the 24th Australasian Computing Education Conference10.1145/3511861.3511864(20-26)Online publication date: 14-Feb-2022

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