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Cited by 20Academic integrity and plagiarismBenford’s Law and Fraud DetectionInnovations in Medical Education

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Investigating the existence of social networks in cheating behaviors in medical students

Open access
Monteiro, Jorge, Silva-Pereira, Fernanda, Severo, Milton·2018·BMC Medical Education
DOI 10.1186/s12909-018-1299-7PMC PMC6085665
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Abstract

BACKGROUND: Most studies on academic cheating rely on self-reported questionnaires and focus on the individual, overlooking cheating as a group activity. The aim of this study is to estimate the true prevalence of cheating/anomalies among medical students using a statistical index developed for this purpose, and to explore the existence of social networks between anomalies in students' results. METHODS: Angoff's A index was applied to a sample of 30 written examinations, with a total of 1487 students and 7403 examinations taken, from the 2014/2015 academic year of the Faculty of Medicine of the University of Porto to detect anomaly pairs. All analyses are within the same academic year and not across years. Through simulations, the sensitivity and specificity of the statistical method was determined, and the true prevalence of anomalies/cheating was estimated. Networks of anomaly pairs were created to search for patterns and to calculate their density. RESULTS: The percentage of students who cheated at least once increased with the year of medical school, being lowest in the first year (3.4%) and highest in the fifth (17.3%). The year of medical school was associated with anomalies (p < 0.05). The network's density was also lowest in the first year (1.12E-04) and highest in the fifth (8.20E-04). The true prevalence of anomalies was estimated to be 1.85% (95%CI: 1.07-3.20%). CONCLUSIONS: These findings suggest that some students are involved in social networks of cheating, which grow over time, resulting in an increase of anomalies/cheating in later academic years.

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Fig. 1 Plot of all pairs of students Rij against the square root(RiRj) from one examination. The red line indicates the mean and the thresholds where the similarity equals the significance level with the correction of Šidák
Fig. 1 Plot of all pairs of students Rij against the square root(RiRj) from one examination. The red line indicates the mean and the thresholds where the similarity equals the significance level with the correction of ŠidákCC BY · PubMed Central
Fig. 2 Percentage of students detected in anomaly/cheating pairs by year of medical school
Fig. 2 Percentage of students detected in anomaly/cheating pairs by year of medical schoolCC BY · PubMed Central
Fig. 3 Social network of anomalies/cheating by means of copying answers by year of medical school. Vertices without connections (students not detected) are not represented
Fig. 3 Social network of anomalies/cheating by means of copying answers by year of medical school. Vertices without connections (students not detected) are not representedCC BY · PubMed Central

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Citations

Cites

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Metadata

Authors
Monteiro, J., Silva-Pereira, F., & Severo, M.
Affiliations
Universidade do Porto (PT)
ORCID
Venue
BMC Medical Education
Publisher
Springer Science and Business Media LLC
Published
2018-08-09
Volume
18
Issue
1
Pages
193-193
ISSN
1472-6920 (ISSN-L)
DOI
10.1186/s12909-018-1299-7
PMC
PMC6085665
OpenAlex
W2887554296
Cited by
20
References
2
Citations by year
2026: 1 · 2025: 6 · 2024: 1 · 2023: 5 · 2022: 3 · 2021: 2 · 2020: 2
FWCI
1.99
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Medical Education (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, cc-by)
Topics
  • Social Sciences › Education › Academic integrity and plagiarism
  • Physical Sciences › Mathematics › Statistics and Probability › Benford’s Law and Fraud Detection
  • Social Sciences › Education › Innovations in Medical Education
Keywords
academic dishonesty, medical students, social networks, anomaly detection, written examinations, prevalence estimation, network density, year of medical school, academic integrity
MeSH terms
Educational Measurement, Humans, Deception, Surveys and Questionnaires, Schools, Medical, Social Support, Students, Medical, Time Factors, Self Report

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