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Massive Open Online Proctor: Protecting the Credibility of MOOCs certificates

Published: 28 February 2015 Publication History

Abstract

Massive Open Online Courses (MOOCs) enable everyone to receive high-quality education. However, current MOOC creators cannot provide an effective, economical, and scalable method to detect cheating on tests, which would be required for any certification. In this paper, we propose a Massive Open Online Proctoring (MOOP) framework, which combines both automatic and collaborative approaches to detect cheating behaviors in online tests. The MOOP framework consists of three major components: Automatic Cheating Detector (ACD), Peer Cheating Detector (PCD), and Final Review Committee (FRC). ACD uses webcam video or other sensors to monitor students and automatically flag suspected cheating behavior. Ambiguous cases are then sent to the PCD, where students peer-review flagged webcam video to confirm suspicious cheating behaviors. Finally, the list of suspicious cheating behaviors is sent to the FRC to make the final punishing decision. Our experiment show that ACD and PCD can detect usage of a cheat sheet with good accuracy and can reduce the overall human resources required to monitor MOOCs for cheating.

References

[1]
ProctorU. http://www.proctoru.com/.
[2]
ProctorU: Overview and Technology Requirements. http://www.ao.uiuc.edu/support/source/student_services/proctoru_tech.html.
[3]
THE TRIBUNAL. http://na.leagueoflegends.com/tribunal/.
[4]
Aggarwal, J., and Ryoo, M. S. Human activity analysis: A review. ACM Computing Surveys (CSUR) 43, 3 (2011), 16.
[5]
Case, R., and Cabalka, P. Remote proctoring: Results of a pilot program at western governors university. Proceedings of the 25th Annual Conference on Distance Teaching and Learning 10 (2009), 2010.
[6]
Dick, M., Sheard, J., Bareiss, C., Carter, J., Joyce, D., Harding, T., and Laxer, C. Addressing student cheating: Definitions and solutions. In Working Group Reports from ITiCSE on Innovation and Technology in Computer Science Education, ITiCSE-WGR '02, ACM (New York, NY, USA, 2002), 172--184.
[7]
Eisenberg, A. Keeping an Eye on Online Test-Takers. The New York Times, 2013.
[8]
Jiang, Y.-G., Dai, Q., Xue, X., Liu, W., and Ngo, C.-W. Trajectory-based modeling of human actions with motion reference points. In Computer Vision - ECCV 2012. Springer, 2012, 425--438.
[9]
Matikainen, P., Hebert, M., and Sukthankar, R. Trajectons: Action recognition through the motion analysis of tracked features. In Computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference on, IEEE (2009), 514--521.
[10]
Mostow, J., Chang, K.-M., and Nelson, J. Toward exploiting eeg input in a reading tutor. In Proceedings of the 15th International Conference on Artificial Intelligence in Education, AIED'11, Springer-Verlag (Berlin, Heidelberg, 2011), 230--237.
[11]
Pappano, L. The Year of the MOOC. The New York Times, 2012.
[12]
Prince, D. J., Fulton, R. A., and Garsombke, T. W. Comparisons of proctored versus non-proctored testing strategies in graduate distance education curriculum. Journal of College Teaching & Learning 6, 7 (2009).
[13]
Richardson, R., and North, M. Strengthening the trust in online courses: A common sense approach. J. Comput. Sci. Coll. 28, 5 (May 2013), 266--272.
[14]
Rogers, C. F. Faculty perceptions about e-cheating during online testing. J. Comput. Sci. Coll. 22, 2 (Dec. 2006), 206--212.
[15]
Shaw, A. D., Horton, J. J., and Chen, D. L. Designing incentives for inexpert human raters. In Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work, CSCW '11, ACM (New York, NY, USA, 2011), 275--284.
[16]
Wang, H., Klaser, A., Schmid, C., and Liu, C.-L. Action recognition by dense trajectories. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, IEEE (2011), 3169--3176.
[17]
Wang, H., Kläser, A., Schmid, C., and Liu, C.-L. Dense trajectories and motion boundary descriptors for action recognition. International journal of computer vision 103, 1 (2013), 60--79.
[18]
Wang, H., Schmid, C., et al. Action recognition with improved trajectories. In International Conference on Computer Vision (2013).
[19]
Young, J. R. Dozens of Plagiarism Incidents Are Reported in Coursera's Free Online Courses. The Chronicle of Higher Education, 2012.
[20]
Zhang, Y., and van der Schaar, M. Reputation-based incentive protocols in crowdsourcing applications. In INFOCOM, 2012 Proceedings IEEE (March 2012), 2140--2148.

Cited By

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  • (2024)Multi-Perspective Adaptive Paperless Examination Cheating Detection System Based on Image RecognitionApplied Sciences10.3390/app1410404814:10(4048)Online publication date: 10-May-2024
  • (2024)Multiple Instance Learning for Cheating Detection and Localization in Online ExaminationsIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2024.334970516:4(1315-1326)Online publication date: Aug-2024
  • (2024)Multibio Authentication of Online Users in Proctoring and E-Learning2024 Third International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)10.1109/ICEEICT61591.2024.10718417(1-7)Online publication date: 24-Jul-2024
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      cover image ACM Conferences
      CSCW '15: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing
      February 2015
      1956 pages
      ISBN:9781450329224
      DOI:10.1145/2675133
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 28 February 2015

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      Author Tags

      1. cheating detection
      2. crowdsourcing
      3. education
      4. human computer interaction
      5. machine learning
      6. mooc

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      CSCW '15 Paper Acceptance Rate 161 of 575 submissions, 28%;
      Overall Acceptance Rate 2,235 of 8,521 submissions, 26%

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      Cited By

      View all
      • (2024)Multi-Perspective Adaptive Paperless Examination Cheating Detection System Based on Image RecognitionApplied Sciences10.3390/app1410404814:10(4048)Online publication date: 10-May-2024
      • (2024)Multiple Instance Learning for Cheating Detection and Localization in Online ExaminationsIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2024.334970516:4(1315-1326)Online publication date: Aug-2024
      • (2024)Multibio Authentication of Online Users in Proctoring and E-Learning2024 Third International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)10.1109/ICEEICT61591.2024.10718417(1-7)Online publication date: 24-Jul-2024
      • (2024)Semi-Supervised Skeleton-Based Covert Cheating Detection in Electronic-ExamsIranian Journal of Science and Technology, Transactions of Electrical Engineering10.1007/s40998-024-00758-248:4(1539-1551)Online publication date: 29-Sep-2024
      • (2024)Auto-proctoring using computer vision in MOOCs systemMultimedia Tools and Applications10.1007/s11042-024-20099-wOnline publication date: 9-Sep-2024
      • (2024)Detecting AI assisted submissions in introductory programming via code anomalyEducation and Information Technologies10.1007/s10639-024-12520-629:13(16841-16866)Online publication date: 16-Feb-2024
      • (2024)Video based action detection for online exam proctoring in resource-constrained settingsEducation and Information Technologies10.1007/s10639-023-12385-129:10(12077-12091)Online publication date: 1-Jul-2024
      • (2024)A Pseudo-Based Online Examination SystemICT for Intelligent Systems10.1007/978-981-97-6681-9_17(189-199)Online publication date: 13-Nov-2024
      • (2024)Advancements and Challenges in Fully Automated Online Proctoring Systems: A Comprehensive Survey of AI-Driven SolutionsSmart Trends in Computing and Communications10.1007/978-981-97-1326-4_17(199-212)Online publication date: 2-Jun-2024
      • (2023)Ensuring Academic Integrity and Trust in Online Learning Environments: A Longitudinal Study of an AI-Centered Proctoring System in Tertiary Educational InstitutionsEducation Sciences10.3390/educsci1306056613:6(566)Online publication date: 31-May-2023
      • Show More Cited By

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