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

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 paper reports an innovative software tool named AssignmentWatch which supports academic integrity by actively monitoring for 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 hours. 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.