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Plagiarism in Take-home Exams: Help-seeking, Collaboration, and Systematic Cheating

Published: 28 June 2017 Publication History

Abstract

Due to the increased enrollments in Computer Science education programs, institutions have sought ways to automate and streamline parts of course assessment in order to be able to invest more time in guiding students' work.
This article presents a study of plagiarism behavior in an introductory programming course, where a traditional pen-and-paper exam was replaced with multiple take-home exams. The students who took the take-home exam enabled a software plugin that recorded their programming process. During an analysis of the students' submissions, potential plagiarism cases were highlighted, and students were invited to interviews.
The interviews with the candidates for plagiarism highlighted three types of plagiarism behaviors: help-seeking, collaboration, and systematic cheating. Analysis of programming process traces indicates that parts of such behavior are detectable directly from programming process data.

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    cover image ACM Conferences
    ITiCSE '17: Proceedings of the 2017 ACM Conference on Innovation and Technology in Computer Science Education
    June 2017
    412 pages
    ISBN:9781450347044
    DOI:10.1145/3059009
    Permission to make digital or hard copies of all or part 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 components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 28 June 2017

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    Author Tags

    1. educational data mining
    2. plagiarism
    3. programming process data

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    ITiCSE '17 Paper Acceptance Rate 56 of 175 submissions, 32%;
    Overall Acceptance Rate 552 of 1,613 submissions, 34%

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    • (2024)Examinator v4.0 : Cheating Detection in Online Take-Home ExamsProceedings of the Eleventh ACM Conference on Learning @ Scale10.1145/3657604.3664659(330-334)Online publication date: 9-Jul-2024
    • (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)Influence of Personality Traits on Plagiarism Through Collusion in Programming AssignmentsProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 110.1145/3632620.3671121(143-153)Online publication date: 12-Aug-2024
    • (2024)"I Didn't Know": Examining Student Understanding of Academic Dishonesty in Computer ScienceProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 110.1145/3626252.3630753(757-763)Online publication date: 7-Mar-2024
    • (2024)Navigating the Pitfalls: Analyzing the Behavior of LLMs as a Coding Assistant for Computer Science Students—A Systematic Review of the LiteratureIEEE Access10.1109/ACCESS.2024.344362112(112605-112625)Online publication date: 2024
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    • (2023)“It’s Weird That it Knows What I Want”: Usability and Interactions with Copilot for Novice ProgrammersACM Transactions on Computer-Human Interaction10.1145/361736731:1(1-31)Online publication date: 29-Nov-2023
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    • (2022)Flipped Exams as a Reliable and Formative Assessment Tool in Higher EducationHandbook of Research on Teacher and Student Perspectives on the Digital Turn in Education10.4018/978-1-6684-4446-7.ch012(237-263)Online publication date: 24-Jun-2022
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