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AssignmentWatch: An Automated Detection and Alert Tool for Reducing Academic Misconduct Associated with File-Sharing Websites

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Title: AssignmentWatch: An Automated Detection and Alert Tool for Reducing Academic Misconduct Associated with File-Sharing Websites
Language: English
Authors: Rick Somers (ORCID 0000-0003-1142-9462), Sam Cunningham (ORCID 0000-0002-3110-5281), Sarah Dart (ORCID 0000-0002-9655-7625), Sheona Thomson (ORCID 0000-0001-8119-0085), Caslon Chua, Edmund Pickering (ORCID 0000-0002-9133-887X)
Source: IEEE Transactions on Learning Technologies. 2024 17:310-318.
Availability: Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4620076
Peer Reviewed: Y
Page Count: 9
Publication Date: 2024
Intended Audience: Teachers
Document Type: Journal Articles
Reports - Evaluative
Descriptors: Assignments, Automation, Identification, Technology Uses in Education, Cheating, Plagiarism, Web Sites
DOI: 10.1109/TLT.2023.3234914
ISSN: 1939-1382
Abstract: Academic misconduct stemming from file-sharing websites is an increasingly prevalent challenge in tertiary education, including information technology and engineering disciplines. Current plagiarism detection methods (e.g., text matching) are largely ineffective for combatting misconduct in programming and mathematics-based assessments. For these reasons, the development of an effective, automated monitoring tool would provide significant benefit in the struggle against misconduct. To address this challenge, this article reports an innovative software tool named AssignmentWatch which supports academic integrity by actively monitoring the upload of assessment content online, with a focus on file-sharing websites. This monitoring and alert notification system reduces the time burden on educators by automating the detection process. The design of AssignmentWatch focuses on early detection which enables educators to take early action. This could include preventative education or removal of content before it is utilized by students. Through this, AssignmentWatch can reduce incidents of misconduct. The software tool is open source and made freely available to the community. AssignmentWatch was validated under controlled conditions, followed by field testing in 30 assignments, across 16 subjects, and 5 higher education institutions. AssignmentWatch was found to effectively detect uploads on a variety of websites, generally within 24 h. In field testing, AssignmentWatch received positive feedback from educators with 8 out of 10 educators stating AssignmentWatch aided academic integrity and 7 out of 10 users stating AssignmentWatch helped identify assignment content online.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1405360
Database: ERIC
FullText Text:
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  Data: AssignmentWatch: An Automated Detection and Alert Tool for Reducing Academic Misconduct Associated with File-Sharing Websites
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  Data: <searchLink fieldCode="AR" term="%22Rick+Somers%22">Rick Somers</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-1142-9462">0000-0003-1142-9462</externalLink>)<br /><searchLink fieldCode="AR" term="%22Sam+Cunningham%22">Sam Cunningham</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-3110-5281">0000-0002-3110-5281</externalLink>)<br /><searchLink fieldCode="AR" term="%22Sarah+Dart%22">Sarah Dart</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-9655-7625">0000-0002-9655-7625</externalLink>)<br /><searchLink fieldCode="AR" term="%22Sheona+Thomson%22">Sheona Thomson</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-8119-0085">0000-0001-8119-0085</externalLink>)<br /><searchLink fieldCode="AR" term="%22Caslon+Chua%22">Caslon Chua</searchLink><br /><searchLink fieldCode="AR" term="%22Edmund+Pickering%22">Edmund Pickering</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-9133-887X">0000-0002-9133-887X</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22IEEE+Transactions+on+Learning+Technologies%22"><i>IEEE Transactions on Learning Technologies</i></searchLink>. 2024 17:310-318.
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  Data: Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4620076
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  Data: <searchLink fieldCode="DE" term="%22Assignments%22">Assignments</searchLink><br /><searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Identification%22">Identification</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Cheating%22">Cheating</searchLink><br /><searchLink fieldCode="DE" term="%22Plagiarism%22">Plagiarism</searchLink><br /><searchLink fieldCode="DE" term="%22Web+Sites%22">Web Sites</searchLink>
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  Data: 10.1109/TLT.2023.3234914
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  Data: 1939-1382
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  Data: Academic misconduct stemming from file-sharing websites is an increasingly prevalent challenge in tertiary education, including information technology and engineering disciplines. Current plagiarism detection methods (e.g., text matching) are largely ineffective for combatting misconduct in programming and mathematics-based assessments. For these reasons, the development of an effective, automated monitoring tool would provide significant benefit in the struggle against misconduct. To address this challenge, this article reports an innovative software tool named AssignmentWatch which supports academic integrity by actively monitoring the upload of assessment content online, with a focus on file-sharing websites. This monitoring and alert notification system reduces the time burden on educators by automating the detection process. The design of AssignmentWatch focuses on early detection which enables educators to take early action. This could include preventative education or removal of content before it is utilized by students. Through this, AssignmentWatch can reduce incidents of misconduct. The software tool is open source and made freely available to the community. AssignmentWatch was validated under controlled conditions, followed by field testing in 30 assignments, across 16 subjects, and 5 higher education institutions. AssignmentWatch was found to effectively detect uploads on a variety of websites, generally within 24 h. In field testing, AssignmentWatch received positive feedback from educators with 8 out of 10 educators stating AssignmentWatch aided academic integrity and 7 out of 10 users stating AssignmentWatch helped identify assignment content online.
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      – SubjectFull: Web Sites
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