Learning Analytics to Detect Evidence of Fraudulent Behaviour in Online Examinations
| Autor: | Juan Manuel Dodero, Antonio Balderas, Manuel Palomo-Duarte, Juan Antonio Caballero-Hernández, Mercedes Rodriguez-Garcia |
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| Přispěvatelé: | Ingeniería en Automática, Electrónica, Arquitectura y Redes de Computadores, Ingeniería Informática |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
learning analytics Technology evaluation Computer Networks and Communications Computer science Learning analytics IJIMAI Learning Management System cheating Data science Learning Analytics Computer Science Applications learning records Artificial Intelligence Signal Processing Cheating ComputingMilieux_COMPUTERSANDEDUCATION Computer Vision and Pattern Recognition learning management systems Evaluation Learning Records |
| Zdroj: | International Journal of Interactive Multimedia and Artificial Intelligence, Vol 7, Iss 2, Pp 241-249 (2021) International Journal Of Interactive Multimedia And Artificial Intelligence, 7(Regular Issue), 241-249 RODIN. Repositorio de Objetos de Docencia e Investigación de la Universidad de Cádiz instname Re-Unir. Archivo Institucional de la Universidad Internacional de La Rioja |
| ISSN: | 1989-1660 |
| Popis: | 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. This research was funded by Spanish National Research Agency (AEI), through the project VISAIGLE (TIN2017-85797-R) with ERDF funds. |
| Databáze: | OpenAIRE |
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