TY - JOUR
KW - Cheating
KW - Evaluation
KW - Learning Analytics
KW - Learning Management Systems
KW - Learning Records
AU - Antonio Balderas
AU - Manuel Palomo-Duarte
AU - Juan Antonio Caballero-Hernández
AU - Mercedes Rodriguez-Garcia
AU - Juan Manuel Dodero
AB - 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.
IS - Regular Issue
M1 - 2
N2 - 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.
PY - 2021
SP - 241
EP - 249
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Learning Analytics to Detect Evidence of Fraudulent Behaviour in Online Examinations
UR - https://www.ijimai.org/journal/sites/default/files/2021-11/ijimai7_2_21_0.pdf
VL - 7
SN - 1989-1660
ER -