• español
    • English
  • Login
  • English 
    • español
    • English

UniversidaddeCádiz

Área de Biblioteca, Archivo y Publicaciones
Communities and Collections
View Item 
  •   RODIN Home
  • Producción Científica
  • Artículos Científicos
  • View Item
  •   RODIN Home
  • Producción Científica
  • Artículos Científicos
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Learning Analytics to Detect Evidence of Fraudulent Behaviour in Online Examinations

Thumbnail
Identificadores

URI: http://hdl.handle.net/10498/26174

DOI: 10.9781/ijimai.2021.10.007

ISSN: 1989-1660

Files
2021_916.pdf (767.0Kb)
Statistics
View statistics
Metrics and citations
 
Share
Export
Export reference to MendeleyRefworksEndNoteBibTexRIS
Metadata
Show full item record
Author/s
Balderas Alberico, AntonioAuthority UCA; Palomo Duarte, ManuelAuthority UCA; Caballero Hernández, Juan AntonioAuthority UCA; Rodríguez García, María MercedesAuthority UCA; Dodero Beardo, Juan ManuelAuthority UCA
Date
2021-12
Department
Ingeniería en Automática, Electrónica, Arquitectura y Redes de Computadores; Ingeniería Informática
Source
International Journal Of Interactive Multimedia And Artificial Intelligence, 7(Regular Issue), 241-249
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.
Subjects
Cheating; Evaluation; Learning Analytics; Learning Management System; Learning Records
Collections
  • Artículos Científicos [4980]
  • Artículos Científicos INDESS [390]
  • Articulos Científicos Ing. Inf. [142]
  • Articulos Científicos Ing. Sis. Aut. [60]
Atribución 4.0 Internacional
This work is under a Creative Commons License Atribución 4.0 Internacional

Browse

All of RODINCommunities and CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

LoginRegister

Statistics

View Usage Statistics

Información adicional

AboutDeposit in RODINPoliciesGuidelinesRightsLinksStatisticsNewsFrequently Asked Questions

RODIN is available through

OpenAIREOAIsterRecolectaHispanaEuropeanaBaseDARTOATDGoogle Academic

Related links

Sherpa/RomeoDulcineaROAROpenDOARCreative CommonsORCID

RODIN está gestionado por el Área de Biblioteca, Archivo y Publicaciones de la Universidad de Cádiz

Contact informationSuggestionsUser Support