Learning Analytics to Detect Evidence of Fraudulent Behaviour in Online Examinations

  1. Antonio Balderas 1
  2. Manuel Palomo-Duarte 1
  3. Juan Antonio Caballero-Hernández 2
  4. María Mercedes Rodríguez García
  5. Juan Manuel Dodero 1
  1. 1 Departamento de Ingeniería Informática, Universidad de Cádiz, Escuela Superior de Ingeniería, Puerto Real (Spain)
  2. 2 EVALfor Research Group, Universidad de Cádiz, Puerto Real (Spain)
  3. 3 Departamento Ingeniería en Automática, Electrónica, Arquitectura y Redes de Computadores, Universidad de Cádiz, Escuela Superior de Ingeniería, Puerto Real (Spain)
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Revista:
IJIMAI

ISSN: 1989-1660

Año de publicación: 2021

Volumen: 7

Número: 2

Páginas: 241-249

Tipo: Artículo

DOI: 10.9781/IJIMAI.2021.10.007 DIALNET GOOGLE SCHOLAR

Otras publicaciones en: IJIMAI

Resumen

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.

Referencias bibliográficas

  • D. Stuber-McEwen, P. Wiseley, S. Hoggatt, “Point, click, and cheat: Frequency and type of academic dishonesty in the virtual classroom,” Online Journal of Distance Learning Administration, vol. 12, no. 3, 2009.
  • F. J. García-Peñalvo, “El sistema universitario ante la covid-19: Corto, medio y largo plazo,” Universídad, 2020, doi: https://bit.ly/2YPUeXU.
  • C. Giovannella, M. Passarelli, D. Persico, “The effects of the covid-19 pandemic on italian learning ecosystems: The school teachers’ perspective at the steady state,” Interaction Design and Architecture(s), vol. 45, pp. 264–286, 2020.
  • R. Harper, T. Bretag, K. Rundle, “Detecting contract cheating: examining the role of assessment type,” Higher Education Research & Development, vol. 40, no. 2, pp. 263–278, 2021.
  • B. Chen, M. West, C. Zilles, “How much randomization is needed to deter collaborative cheating on asynchronous exams?,” in Proceedings of the Fifth Annual ACM Conference on Learning at Scale, 2018, pp. 1–10.
  • I. M. Sierra, M. G. Gómez, J. S. Eizaguirre, “Learning analytics for formative assessment in engineering education,” The International Journal of Engineering Education, vol. 34, no. 3, pp. 953–967, 2018.
  • A. Álvarez-Arana, M. Larrañaga-Olagaray, M. Villamañe-Gironés, “Mejora de los procesos de evaluación mediante analítica visual del aprendizaje,” Education in the Knowledge Society, vol. 21, no. 9, 2020, doi: 10.14201/eks.21554.
  • R. A. Bernardi, R. L. Metzger, R. G. S. Bruno, M. A. W. Hoogkamp, L. E. Reyes, G. H. Barnaby, “Examining the decision process of students’ cheating behavior: An empirical study,” Journal of Business Ethics, vol. 50, no. 4, pp. 397–414, 2004.
  • W. S. Albrecht, G. W. Wernz, T. L. Williams, et al., Fraud: Bringing light to the dark side of business. Irwin Professional Pub., 1995.
  • E. J. Austin, D. H. Saklofske, S. M. Mastoras, “Emotional intelligence, coping and exam-related stress in canadian undergraduate students,” Australian Journal of Psychology, vol. 62, no. 1, pp. 42–50, 2010.
  • E. T. Baloran, “Knowledge, attitudes, anxiety, and coping strategies of students during covid-19 pandemic,” Journal of Loss and Trauma, vol. 25, no. 8, pp. 635–642, 2020.
  • F. J. García-Peñalvo, A. Corell, V. Abella-García, M. Grande-de Prado, “Recommendations for mandatory online assessment in higher education during the covid-19 pandemic,” in Radical Solutions for Education in a Crisis Context, Springer, 2021, pp. 85–98.
  • G. R. Watson, J. Sottile, “Cheating in the digital age: Do students cheat more in online courses?,” Online Journal of Distance Learning Administration, no. 13.1, 2010.
  • S. Kocdar, A. Karadeniz, R. Peytcheva-Forsyth, V. Stoeva, “Cheating and plagiarism in e-assessment: students’ perspectives,” Open Praxis, vol. 10, no. 3, pp. 221–235, 2018.
  • R. W. Smith, T. Prometric, “The impact of braindump sites on item exposure and item parameter drift,” in Annual Meeting of the American Education Research Association, 2004.
  • T. Mason, A. Gavrilovska, D. A. Joyner, “Collaboration versus cheating: Reducing code plagiarism in an online ms computer science program,” in Proceedings of the 50th ACM Technical Symposium on Computer Science Education, 2019, pp. 1004–1010.
  • A. Hellas, J. Leinonen, P. Ihantola, “Plagiarism in take-home exams: Help-seeking, collaboration, and systematic cheating,” in Proceedings of the 2017 ACM conference on innovation and technology in computer science education, 2017, pp. 238–243.
  • R. R. Naik, M. B. Landge, C. N. Mahender, “A review on plagiarism detection tools,” International Journal of Computer Applications, vol. 125, no. 11, 2015.
  • A. Abdi, N. Idris, R. M. Alguliyev, R. M. Aliguliyev, “Pdlk: Plagiarism detection using linguistic knowledge,” Expert Systems with Applications, vol. 42, no. 22, pp. 8936–8946, 2015.
  • S. E. Eaton, K. L. Turner, “Exploring academic integrity and mental health during covid-19: Rapid review,” Journal of Contemporary Education Theory & Research (JCETR), vol. 4, no. 2, pp. 35–41, 2020.
  • B. Chen, S. Azad, M. Fowler, M. West, C. Zilles, “Learning to cheat: Quantifying changes in score advantage of unproctored assessments over time,” in Proceedings of the Seventh ACM Conference on Learning@ Scale, 2020, pp. 197–206.
  • C. S. Gonzalez-Gonzalez, A. Infante-Moro, J. C. Infante-Moro, “Implementation of e-proctoring in online teaching: A study about motivational factors,” Sustainability, vol. 12, no. 8, p. 3488, 2020.
  • A. Balderas, J. M. Dodero, M. Palomo-Duarte, I. Ruiz-Rube, “A domain specific language for online learning competence assessments,” International Journal of Engineering Education, vol. 31, no. 3, pp. 851–862, 2015.
  • M. L. Sein-Echaluce, A. Fidalgo-Blanco, J. Esteban-Escano, F. J. García-Peñalvo, M. A. Conde-González, “Using learning analytics to detect authentic leadership characteristics in engineering students,” International Journal of Engineering Education, vol. 34, no. 3, pp. 851–864, 2018.
  • A. Balderas, L. De-La-Fuente-Valentin, M. Ortega-Gomez, J. M. Dodero, D. Burgos, “Learning management systems activity records for students’ assessment of generic skills,” IEEE access, vol. 6, pp. 15958–15968, 2018.
  • A. Balderas, M. Palomo-Duarte, J. M. Dodero, M. S. Ibarra-Sáiz, G. Rodríguez-Gómez, “Scalable authentic assessment of collaborative work assignments in wikis,” International Journal of Educational Technology in Higher Education, vol. 15, no. 1, pp. 1–21, 2018.
  • F. Riquelme, R. Munoz, R. Mac Lean, R. Villarroel, T. S. Barcelos, V. H. C. de Albuquerque, “Using multimodal learning analytics to study collaboration on discussion groups,” Universal Access in the Information Society, vol. 18, no. 3, pp. 633–643, 2019.
  • D. Von Gruenigen, F. B. d. A. e Souza, B. Pradarelli, A. Magid, M. Cieliebak, “Best practices in e-assessments with a special focus on cheating prevention,” in 2018 IEEE Global Engineering Education Conference (EDUCON), 2018, pp. 893–899, IEEE.
  • S. Manoharan, “Cheat-resistant multiple-choice examinations using personalization,” Computers & Education, vol. 130, pp. 139–151, 2019.
  • P. Denny, S. Manoharan, U. Speidel, G. Russello, A. Chang, “On the fairness of multiple-variant multiple-choice examinations,” in Proceedings of the 50th ACM Technical Symposium on Computer Science Education, 2019, pp. 462–468.
  • L. C. O. Tiong, H. J. Lee, “E-cheating prevention measures: Detection of cheating at online examinations using deep learning approach–a case study,” arXiv preprint arXiv:2101.09841, 2021.
  • D. Jaramillo-Morillo, J. Ruipérez-Valiente, M. F. Sarasty, G. RamírezGonzalez, “Identifying and characterizing students suspected of academic dishonesty in spocs for credit through learning analytics,” International Journal of Educational Technology in Higher Education, vol. 17, no. 1, pp. 1–18, 2020.
  • K. A. Villanueva, S. A. Brown, N. P. Pitterson, D. S. Hurwitz, A. Sitomer, “Teaching evaluation practices in engineering programs: Current approaches and usefulness,” International Journal of Engineering Education, vol. 33, no. 4, pp. 1317–1334, 2017.
  • F. J. García-Peñalvo, A. Corell, V. Abella-García, M. Grande, “Online assessment in higher education in the time of covid-19,” Education in the Knowledge Society, vol. 21, 2020.
  • Á. Fidalgo-Blanco, M. L. Sein-Echaluce, F. J. García-Peñalvo, M. Á. Conde, “Using learning analytics to improve team-work assessment,” Computers in Human Behavior, vol. 47, pp. 149–156, 2015.
  • M. Palomo-Duarte, A. Berns, A. Balderas, J. M. Dodero, D. Camacho, “Evidence-based assessment of student performance in virtual worlds,” Sustainability, vol. 13, no. 1, p. 244, 2021.
  • Chegg.org, “Global student survey 2021,” Chegg Inc., 2021. [Online]. Available: https://www.chegg.org/global-student-survey-2021.
  • S. Iglesias-Pradas, Á. Hernández-García, J. Chaparro-Peláez, L. Prieto, “Emergency remote teaching and students’ academic performance in higher education during the covid-19 pandemic: A case study,” Computers in Human Behavior, p. 106713, 2021.
Fuente de los datos: Dialnet