Learning Analytics to Detect Evidence of Fraudulent Behaviour in Online Examinations

Author
Keywords
Abstract
Lecturers are often reluctant to set examinations online because of the potential problems of fraudulent behaviour from their students. This concern has increased during the coronavirus pandemic because courses that were previously designed to be taken face-to-face have to be conducted online. The courses have had to be redesigned, including seminars, laboratory sessions and evaluation activities. This has brought lecturers and students into conflict because, according to the students, the activities and examinations that have been redesigned to avoid cheating are also harder. The lecturers’ concern is that students can collaborate in taking examinations that must be taken individually without the lecturers being able to do anything to prevent it, i.e. fraudulent collaboration. This research proposes a process model to obtain evidence of students who attempt to fraudulently collaborate, based on the information in the learning environment logs. It is automated in a software tool that checks how the students took the examinations and the grades that they obtained. It is applied in a case study with more than 100 undergraduate students. The results are positive and its use allowed lecturers to detect evidence of fraudulent collaboration by several clusters of students from their submission timestamps and the grades obtained.
Year of Publication
2021
Journal
International Journal of Interactive Multimedia and Artificial Intelligence
Volume
7
Issue
Regular Issue
Number
2
Number of Pages
241-249
Date Published
12/2021
ISSN Number
1989-1660
URL
https://www.ijimai.org/journal/sites/default/files/2021-11/ijimai7_2_21_0.pdf
DOI
10.9781/ijimai.2021.10.007
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