Can we detect contract cheating using existing assessment data? Applying crime prevention theory to an academic integrity issue

Joseph Patrick Clare, Sonia Walker, Julia Hobson

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Objectives:Building on what is known about the non-random nature of crime problems and the explanatory capacity of opportunity theories of crime, this study explores the utility of using existing university administrative data to detect unusual patterns of performance consistent with a student having engaged in contract cheating (paying a third-party to produce unsupervised work on their behalf).Methods:Results from an Australian university were analysed (N= 3798 results,N= 1459 students). Performances on unsupervised and supervised assessment items were converted to percentages and percentage point differences analysed at the academic discipline-, unit-, and student-level, looking for non-random patterns of unusually large differences.Results:Non-random, unusual patterns, consistent with contract cheating, were found at the academic discipline-, unit-, and student-level, with approximately 2.1%of students producing multiple unusual patterns.Conclusions:These findings suggest it may be possible to use existing administrative data to identify assessment items that provide suitable opportunities for contract cheating. This approach could be used in conjunction with targeted problem-prevention strategies (based on situational crime prevention) to reduce the vulnerability of academic assessment items to contract cheating. This approach is worthy of additional research as it has the potential to help academic institutions around the world manage contract cheating; a problem that currently threatens the validity and integrity of tertiary qualifications.
Original languageEnglish
Pages (from-to)1-15
JournalInternational Journal for Educational Integrity
Volume13
Issue number4
DOIs
Publication statusPublished - 8 Aug 2017
Externally publishedYes

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crime prevention
integrity
student
offense
university
qualification
performance
vulnerability

Cite this

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title = "Can we detect contract cheating using existing assessment data? Applying crime prevention theory to an academic integrity issue",
abstract = "Objectives:Building on what is known about the non-random nature of crime problems and the explanatory capacity of opportunity theories of crime, this study explores the utility of using existing university administrative data to detect unusual patterns of performance consistent with a student having engaged in contract cheating (paying a third-party to produce unsupervised work on their behalf).Methods:Results from an Australian university were analysed (N= 3798 results,N= 1459 students). Performances on unsupervised and supervised assessment items were converted to percentages and percentage point differences analysed at the academic discipline-, unit-, and student-level, looking for non-random patterns of unusually large differences.Results:Non-random, unusual patterns, consistent with contract cheating, were found at the academic discipline-, unit-, and student-level, with approximately 2.1{\%}of students producing multiple unusual patterns.Conclusions:These findings suggest it may be possible to use existing administrative data to identify assessment items that provide suitable opportunities for contract cheating. This approach could be used in conjunction with targeted problem-prevention strategies (based on situational crime prevention) to reduce the vulnerability of academic assessment items to contract cheating. This approach is worthy of additional research as it has the potential to help academic institutions around the world manage contract cheating; a problem that currently threatens the validity and integrity of tertiary qualifications.",
keywords = "Academic integrity, Contract cheating, Rational choice, Routine activity theory, Situational crime prevention",
author = "Clare, {Joseph Patrick} and Sonia Walker and Julia Hobson",
year = "2017",
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doi = "10.1007/s40979-017-0015-4",
language = "English",
volume = "13",
pages = "1--15",
journal = "International Journal for Academic Integrity",
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AU - Walker, Sonia

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N2 - Objectives:Building on what is known about the non-random nature of crime problems and the explanatory capacity of opportunity theories of crime, this study explores the utility of using existing university administrative data to detect unusual patterns of performance consistent with a student having engaged in contract cheating (paying a third-party to produce unsupervised work on their behalf).Methods:Results from an Australian university were analysed (N= 3798 results,N= 1459 students). Performances on unsupervised and supervised assessment items were converted to percentages and percentage point differences analysed at the academic discipline-, unit-, and student-level, looking for non-random patterns of unusually large differences.Results:Non-random, unusual patterns, consistent with contract cheating, were found at the academic discipline-, unit-, and student-level, with approximately 2.1%of students producing multiple unusual patterns.Conclusions:These findings suggest it may be possible to use existing administrative data to identify assessment items that provide suitable opportunities for contract cheating. This approach could be used in conjunction with targeted problem-prevention strategies (based on situational crime prevention) to reduce the vulnerability of academic assessment items to contract cheating. This approach is worthy of additional research as it has the potential to help academic institutions around the world manage contract cheating; a problem that currently threatens the validity and integrity of tertiary qualifications.

AB - Objectives:Building on what is known about the non-random nature of crime problems and the explanatory capacity of opportunity theories of crime, this study explores the utility of using existing university administrative data to detect unusual patterns of performance consistent with a student having engaged in contract cheating (paying a third-party to produce unsupervised work on their behalf).Methods:Results from an Australian university were analysed (N= 3798 results,N= 1459 students). Performances on unsupervised and supervised assessment items were converted to percentages and percentage point differences analysed at the academic discipline-, unit-, and student-level, looking for non-random patterns of unusually large differences.Results:Non-random, unusual patterns, consistent with contract cheating, were found at the academic discipline-, unit-, and student-level, with approximately 2.1%of students producing multiple unusual patterns.Conclusions:These findings suggest it may be possible to use existing administrative data to identify assessment items that provide suitable opportunities for contract cheating. This approach could be used in conjunction with targeted problem-prevention strategies (based on situational crime prevention) to reduce the vulnerability of academic assessment items to contract cheating. This approach is worthy of additional research as it has the potential to help academic institutions around the world manage contract cheating; a problem that currently threatens the validity and integrity of tertiary qualifications.

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