IDEAS home Printed from https://ideas.repec.org/a/inm/orited/v23y2023i2p84-94.html
   My bibliography  Save this article

Who’s Cheating? Mining Patterns of Collusion from Text and Events in Online Exams

Author

Listed:
  • Catherine Cleophas

    (Institute for Business Management, Kiel University, Kiel 24098, Germany)

  • Christoph Hönnige

    (Institute for Political Science, Leibniz University Hanover, Hanover 30167, Germany)

  • Frank Meisel

    (Institute for Business Management, Kiel University, Kiel 24098, Germany)

  • Philipp Meyer

    (Institute for Political Science, Leibniz University Hanover, Hanover 30167, Germany)

Abstract

As the COVID-19 pandemic motivated a shift to virtual teaching, exams have increasingly moved online too. Detecting cheating through collusion is not easy when tech-savvy students take online exams at home and on their own devices. Such online at-home exams may tempt students to collude and share materials and answers. However, online exams’ digital output also enables computer-aided detection of collusion patterns. This paper presents two simple data-driven techniques to analyze exam event logs and essay-form answers. Based on examples from exams in social sciences, we show that such analyses can reveal patterns of student collusion. We suggest using these patterns to quantify the degree of collusion. Finally, we summarize a set of lessons learned about designing and analyzing online exams.

Suggested Citation

  • Catherine Cleophas & Christoph Hönnige & Frank Meisel & Philipp Meyer, 2023. "Who’s Cheating? Mining Patterns of Collusion from Text and Events in Online Exams," INFORMS Transactions on Education, INFORMS, vol. 23(2), pages 84-94, January.
    • Handle: RePEc:inm:orited:v:23:y:2023:i:2:p:84-94
    DOI: 10.1287/ited.2021.0260
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/ited.2021.0260
    Download Restriction: no
    File URL: https://libkey.io/10.1287/ited.2021.0260?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    2. Sinjini Mitra & Gerard Beenen, 2019. "A Comparative Study of Learning Styles and Motivational Factors in Traditional and Online Sections of a Business Course," INFORMS Transactions on Education, INFORMS, vol. 20(1), pages 1-15, September.
    3. Oskar R. Harmon & James Lambrinos, 2008. "Are Online Exams an Invitation to Cheat?," The Journal of Economic Education, Taylor & Francis Journals, vol. 39(2), pages 116-125, April.
    4. Christina R. Scherrer, 2011. "Comparison of an Introductory Level Undergraduate Statistics Course Taught with Traditional, Hybrid, and Online Delivery Methods," INFORMS Transactions on Education, INFORMS, vol. 11(3), pages 106-110, May.
    5. Thomas C. Sharkey & Sarah G. Nurre, 2016. "Video Tutorials Within an Undergraduate Operations Research Course: Student Perception on Their Integration and Creating A Blended Learning Environment," INFORMS Transactions on Education, INFORMS, vol. 17(1), pages 1-12, September.
    6. John Miltenburg, 2019. "Online Teaching in a Large, Required, Undergraduate Management Science Course," INFORMS Transactions on Education, INFORMS, vol. 19(2), pages 89-104, January.
    7. Francesca Gino & Erin L. Krupka & Roberto A. Weber, 2013. "License to Cheat: Voluntary Regulation and Ethical Behavior," Management Science, INFORMS, vol. 59(10), pages 2187-2203, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. John Miltenburg, 2019. "Online Teaching in a Large, Required, Undergraduate Management Science Course," INFORMS Transactions on Education, INFORMS, vol. 19(2), pages 89-104, January.
    2. Bernhardt, Lea & Dewenter, Ralf & Thomas, Tobias, 2020. "Measuring partisan media bias in US Newscasts from 2001-2012," Working Paper 183/2020, Helmut Schmidt University, Hamburg, revised 15 Nov 2022.
    3. Ntentas, Raphael, 2021. "Quantifying political populism and examining the link with economic insecurity: evidence from Greece," LSE Research Online Documents on Economics 112579, London School of Economics and Political Science, LSE Library.
    4. Lin, Annie E. & Young, Jimmy A. & Guarino, Jeannine E., 2022. "Mother-Daughter sexual abuse: An exploratory study of the experiences of survivors of MDSA using Reddit," Children and Youth Services Review, Elsevier, vol. 138(C).
    5. Rybinski, Krzysztof, 2020. "The forecasting power of the multi-language narrative of sell-side research: A machine learning evaluation," Finance Research Letters, Elsevier, vol. 34(C).
    6. Rauh, Christian, 2015. "Communicating supranational governance? The salience of EU affairs in the German Bundestag, 1991–2013," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 16(1), pages 116-138.
    7. Grajzl, Peter & Murrell, Peter, 2021. "A machine-learning history of English caselaw and legal ideas prior to the Industrial Revolution I: generating and interpreting the estimates," Journal of Institutional Economics, Cambridge University Press, vol. 17(1), pages 1-19, February.
    8. David Bholat & Stephen Hans & Pedro Santos & Cheryl Schonhardt-Bailey, 2015. "Text mining for central banks," Handbooks, Centre for Central Banking Studies, Bank of England, number 33, April.
    9. Julia Seiermann, 2018. "Only Words? How Power in Trade Agreement Texts Affects International Trade Flows," UNCTAD Blue Series Papers 80, United Nations Conference on Trade and Development.
    10. Sami Diaf & Jörg Döpke & Ulrich Fritsche & Ida Rockenbach, 2020. "Sharks and minnows in a shoal of words: Measuring latent ideological positions of German economic research institutes based on text mining techniques," Macroeconomics and Finance Series 202001, University of Hamburg, Department of Socioeconomics.
    11. Mark W. Isken, 2014. "Translating a Lab Based Spreadsheet Modeling Course to an Online Format: Experience from a Natural Experiment," INFORMS Transactions on Education, INFORMS, vol. 14(3), pages 120-128, May.
    12. Dehler-Holland, Joris & Schumacher, Kira & Fichtner, Wolf, 2021. "Topic Modeling Uncovers Shifts in Media Framing of the German Renewable Energy Act," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 2(1).
    13. Weiss, Max & Zoorob, Michael, 2021. "Political frames of public health crises: Discussing the opioid epidemic in the US Congress," Social Science & Medicine, Elsevier, vol. 281(C).
    14. Arthur Dyevre & Nicolas Lampach, 2021. "Issue attention on international courts: Evidence from the European Court of Justice," The Review of International Organizations, Springer, vol. 16(4), pages 793-815, October.
    15. Dewenter, Ralf & Dulleck, Uwe & Thomas, Tobias, 2018. "The political coverage index and its application to government capture," Research Papers 6, EcoAustria – Institute for Economic Research.
    16. Pastwa, Anna M. & Shrestha, Prabal & Thewissen, James & Torsin, Wouter, 2021. "Unpacking the black box of ICO white papers: a topic modeling approach," LIDAM Discussion Papers LFIN 2021018, Université catholique de Louvain, Louvain Finance (LFIN).
    17. Maksym Polyakov & Morteza Chalak & Md. Sayed Iftekhar & Ram Pandit & Sorada Tapsuwan & Fan Zhang & Chunbo Ma, 2018. "Authorship, Collaboration, Topics, and Research Gaps in Environmental and Resource Economics 1991–2015," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 71(1), pages 217-239, September.
    18. Parijat Chakrabarti & Margaret Frye, 2017. "A mixed-methods framework for analyzing text data: Integrating computational techniques with qualitative methods in demography," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 37(42), pages 1351-1382.
    19. Milena Djourelova & Ruben Durante, 2019. "Media attention and strategic timing in politics: Evidence from U.S. presidential executive orders," Economics Working Papers 1675, Department of Economics and Business, Universitat Pompeu Fabra.
    20. Oto-Peralías, Daniel & Gutiérrez Mora, Dolores, 2021. "Gendered cities: Studying urban gender bias through street names," OSF Preprints b9n4k, Center for Open Science.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orited:v:23:y:2023:i:2:p:84-94. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.