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Process Mining Techniques for Collusion Detection in Online Exams

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Process Mining Workshops (ICPM 2023)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 503))

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Abstract

Honesty and fairness are essential. As many skills, practicing those values starts in the classroom. Whether students are examined online or on-site, only testing their knowledge righteously, educators can assess their skills and room for improvement. As online exams increase, we are provided with more suitable data for analysis. Process mining methods as anomaly detection and trace clustering techniques have been used to identify dishonest behavior in other fields, as e.g. fraud detection. In this paper, we investigate collusion detection in online exams as a process mining task. We explore trace ordering for anomaly detection (TOAD) as well as hierarchical agglomerative trace clustering (HATC). Promising preliminary results exemplify, how process mining techniques empower teachers in their decision making, while via flexible configuration of parameters, leaves the last word to them.

A. Maldonado and L. Zellner—Equal contribution.

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Notes

  1. 1.

    https://github.com/lmu-dbs/collusion_det_on_exams.

References

  1. Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: OPTICS: ordering points to identify the clustering structure. ACM SIGMOD Rec. 28(2), 49–60 (1999)

    Article  Google Scholar 

  2. Bala, S., Revoredo, K., Mendling, J.: Process mining for analyzing open questions computer-aided examinations. In: Montali, M., Senderovich, A., Weidlich, M. (eds.) ICPM 2022. LNBIP, vol. 468, pp. 565–576. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-27815-0_41

    Chapter  Google Scholar 

  3. Barrio, F.: Legal and pedagogical issues with online exam proctoring. Eur. J. Law Technol. 13(1) (2022)

    Google Scholar 

  4. Becker, K., Meng, H.: Identifying statistically actionable collusion in remote proctored exams. J. Appl. Test. Technol. 23, 54–61 (2022)

    Google Scholar 

  5. Bekker, J., Davis, J.: Learning from positive and unlabeled data: a survey. Mach. Learn. 109, 719–760 (2020)

    Article  MathSciNet  Google Scholar 

  6. Bogarín, A., Cerezo, R., Romero, C.: A survey on educational process mining. Wiley Interdisc. Rev. DMKD 8(1), e1230 (2018)

    Google Scholar 

  7. Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 1–58 (2009)

    Article  Google Scholar 

  8. Cleophas, C., Hönnige, C., Meisel, F., Meyer, P.: Who’s cheating? Mining patterns of collusion from text and events in online exams. INFORMS Trans. Educ. 23(2), 84–94 (2023). https://doi.org/10.1287/ited.2021.0260

    Article  Google Scholar 

  9. D’Souza, K.A., Siegfeldt, D.V.: A conceptual framework for detecting cheating in online and take-home exams. Decis. Sci. J. Innov. Educ. 15(4), 370–391 (2017). https://doi.org/10.1111/dsji.12140

    Article  Google Scholar 

  10. Fask, A., Englander, F., Wang, Z.: On the integrity of online testing for introductory statistics courses: a latent variable approach. PARE 20(1), n10 (2015)

    Google Scholar 

  11. Finch, H.: Comparison of distance measures in cluster analysis with dichotomous data. J. Data Sci. 3(1), 85–100 (2005)

    Article  Google Scholar 

  12. Fortenbacher, A., et al.: LeMo: a learning analytics application focussing on user path analysis and interactive visualization. In: Proceedings of the IDAACS, vol. 2 (2013)

    Google Scholar 

  13. Fraser, R.: Collaboration, collusion and plagiarism in computer science coursework. Inform. Educ. Int. J. 13(2), 179–195 (2014)

    Google Scholar 

  14. Harmon, O.R., Lambrinos, J.: Are online exams an invitation to cheat? J. Econ. Educ. 39(2), 116–125 (2008). https://doi.org/10.3200/jece.39.2.116-125

    Article  Google Scholar 

  15. Hidalgo, L., Munoz-Gama, J.: Domain-driven event abstraction framework for learning dynamics in MOOCs sessions. In: Montali, M., Senderovich, A., Weidlich, M. (eds.) ICPM 2022. LNBIP, vol. 468, pp. 552–564. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-27815-0_40

    Chapter  Google Scholar 

  16. Hobeck, R., Pufahl, L., Weber, I.: Process mining on curriculum-based study data: a case study at a German university. In: Montali, M., Senderovich, A., Weidlich, M. (eds.) ICPM 2022. LNBIP, vol. 468, pp. 577–589. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-27815-0_42

    Chapter  Google Scholar 

  17. Huser, V.: Process mining: discovery, conformance and enhancement of business processes (2012)

    Google Scholar 

  18. Ingrisone, S.J., Ingrisone, J.N.: Hierarchical agglomerative clustering to detect test collusion on computer-based tests. EM:IP 42(3), 39–49 (2023)

    Google Scholar 

  19. Küppers, B., Opgen-Rhein, J., Eifert, T., Schroeder, U.: Cheating detection: identifying fraud in digital exams. Eur. J. High. Educ. IT (2019)

    Google Scholar 

  20. Langerbein, J., et al.: A data mining approach for detecting collusion in unproctored online exams. In: Proceedings of the EDM (2023)

    Google Scholar 

  21. Matcha, W., Gašević, D., Uzir, N.A., Jovanović, J., Pardo, A.: Analytics of learning strategies: associations with academic performance and feedback. In: LAK (2019)

    Google Scholar 

  22. Richter, F., Lu, Y., Zellner, L., Sontheim, J., Seidl, T.: TOAD: trace ordering for anomaly detection. In: Proceedings of the ICPM. IEEE (2020)

    Google Scholar 

  23. Rohani, N., Gal, K., Gallagher, M., Manataki, A.: Discovering students’ learning strategies in a visual programming MOOC through process mining techniques. In: Montali, M., Senderovich, A., Weidlich, M. (eds.) ICPM 2022. LNBIP, vol. 468, pp. 539–551. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-27815-0_39

    Chapter  Google Scholar 

  24. Romero, C., Ventura, S.: Educational data mining and learning analytics: an updated survey. WIREs Data Min. Knowl. Discov. 10(3), e1355 (2020). https://doi.org/10.1002/widm.1355

    Article  Google Scholar 

  25. Song, M., Günther, C.W., van der Aalst, W.M.P.: Trace clustering in process mining. In: Ardagna, D., Mecella, M., Yang, J. (eds.) BPM 2008. LNBIP, vol. 17, pp. 109–120. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00328-8_11

    Chapter  Google Scholar 

  26. Strickroth, S., Kiy, A.: E-Assessment etablieren: Auf dem Weg zu (dezentralen) E-Klausuren. In: Lehre und Lernen entwickeln. No. 6 in Potsdamer Beiträge zur Hochschulforschung, Universitätsverlag Potsdam (2020). https://doi.org/10.25932/publishup-49303

  27. Strickroth, S., Olivier, H., Pinkwart, N.: Das GATE-System: Qualitätssteigerung durch Selbsttests für Studenten bei der Onlineabgabe von Übungsaufgaben? In: Proceedings of the DELFI (2011). https://dl.gi.de/handle/20.500.12116/4740

  28. Umer, R., Susnjak, T., Mathrani, A., Suriadi, S.: On predicting academic performance with process mining in learning analytics. JRIT 10(2), 160–176 (2017)

    Article  Google Scholar 

  29. Wagner, M., et al.: A combined approach of process mining and rule-based AI for study planning and monitoring in higher education. In: Montali, M., Senderovich, A., Weidlich, M. (eds.) ICPM 2022. LNBIP, vol. 468, pp. 513–525. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-27815-0_37

    Chapter  Google Scholar 

  30. Walker, M., Townley, C.: Contract cheating: a new challenge for academic honesty? J. Acad. Ethics 10, 27–44 (2012)

    Article  Google Scholar 

  31. Watson, G., Sottile, J.: Cheating in the digital age: do students cheat more in on-line courses? In: Proceedings of the SITE. AACE (2008)

    Google Scholar 

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Authors and Affiliations

  1. Ludwig-Maximilians-Universität München, Munich, Germany

    Andrea Maldonado, Ludwig Zellner, Sven Strickroth & Thomas Seidl

  2. Munich Center for Machine Learning (MCML), Munich, Germany

    Andrea Maldonado & Thomas Seidl

Authors
  1. Andrea Maldonado
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  2. Ludwig Zellner
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  3. Sven Strickroth
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  4. Thomas Seidl
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Corresponding authors

Correspondence to Andrea Maldonado or Ludwig Zellner .

Editor information

Editors and Affiliations

  1. KU Leuven, Leuven, Belgium

    Johannes De Smedt

  2. University of Haifa, Haifa, Israel

    Pnina Soffer

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Maldonado, A., Zellner, L., Strickroth, S., Seidl, T. (2024). Process Mining Techniques for Collusion Detection in Online Exams. In: De Smedt, J., Soffer, P. (eds) Process Mining Workshops. ICPM 2023. Lecture Notes in Business Information Processing, vol 503. Springer, Cham. https://doi.org/10.1007/978-3-031-56107-8_26

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  • DOI: https://doi.org/10.1007/978-3-031-56107-8_26

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