RT journal article
T1 Learning Analytics to Detect Evidence of Fraudulent Behaviour in Online Examinations
A1 Balderas Alberico, Antonio
A1 Palomo Duarte, Manuel
A1 Caballero Hernández, Juan Antonio
A1 Rodríguez García, María Mercedes
A1 Dodero Beardo, Juan Manuel
A2 Ingeniería en AutomáticaElectrónica, Arquitectura y Redes de Computadores
A2 Ingeniería Informática
K1 Cheating
K1 Evaluation
K1 Learning Analytics
K1 Learning Management System
K1 Learning Records
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.
PB UNIV INT RIOJA
SN 1989-1660
YR 2021
FD 2021-12
LK http://hdl.handle.net/10498/26174
UL http://hdl.handle.net/10498/26174
LA eng
NO This research was funded by Spanish National Research Agency (AEI), through the project VISAIGLE (TIN2017-85797-R) with ERDF funds.
DS Repositorio Institucional de la Universidad de Cádiz
RD 06-nov-2026