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An intelligent tool for combatting contract cheating behaviour by facilitating scalable student-tutor discussions

Published: 01 October 2020 Publication History

Abstract

With the global increase in demand for online tertiary education, teachers are facing unique challenges in scaling assessment activities and meaningful student engagement. One such aspect is contract cheating behaviours exhibited in the modern online environment --- posing a threat to the academic integrity of tertiary education. These obstacles amplify when applied to traditionally difficult domains like introductory programming education. Prior research on contract cheating identification proposes that while challenging, techniques such as developing strong teacher-student relationships, and real-time discussions may lead to instances of identifying contract cheating behaviours. The proposition, then, is to scale real-time, student-teacher discussions with large, online cohorts --- similar to those discussions which traditionally took place in the classroom. This poster paper presents Intelligent Discussion Comments (IDCs): A scalable, teacher-asynchronous system which engages students in real-time discussions to extract authentic student understanding. Artificial intelligence services such as voice identification and transcription enrich the discussion process, supporting the teaching team in their decision-making process.

References

[1]
Rowena Harper, T Bretag, C Ellis, P Newton, P Rozenberg, S Saddiqui, and K van Haeringen. 2018. Contract cheating: a survey of Australian university staff. Studies in Higher Education 1, 1 (April 2018), 1--17.
[2]
Thomas Lancaster and Robert Clarke. 2015. Contract cheating: the outsourcing of assessed student work. Springer (2015). https://link.springer.com/content/pdf/10.1007/978-981-287-079-7_17-1.pdf
[3]
Andrew Luxton-Reilly, Simon, Ibrahim Albluwi, Brett A Becker, Michail Giannakos, Amruth N Kumar, Linda Ott, James Paterson, Michael James Scott, Judy Sheard, and Claudia Szabo. 2018. Introductory programming: a systematic literature review. ACM.
[4]
Shailendra Palvia, Prageet Aeron, Parul Gupta, Diptiranjan Mahapatra, Ratri Parida, Rebecca Rosner, and Sumita Sindhi. 2018. Online Education: Worldwide Status, Challenges, Trends, and Implications. Journal of Global Information Technology Management 21, 4 (Nov. 2018), 233--241.
[5]
Jake Renzella and Andrew Cain. 2020. Enriching Programming Student Feedback with Audio Comments. In Proceedings of IEEE/ACM 42nd International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET). ACM, Seoul, South Korea. to appear.
[6]
UCube. 2019. uCube - Higher Education Statistics. Technical Report. http://highereducationstatistics.education.gov.au/
[7]
Mary Walker and Cynthia Townley. 2012. Contract cheating: a new challenge for academic honesty? Journal of Academic Ethics 10, 1 (Feb. 2012), 27--44.

Cited By

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  • (2024)Could the Use of AI in Higher Education Hinder Students With Disabilities? A Scoping ReviewIEEE Access10.1109/ACCESS.2024.336536812(27810-27828)Online publication date: 2024
  • (2024)Transforming Education Through Integrating AI: A Systematic Mapping Review for Enhanced User ExperienceProceedings of TEEM 202310.1007/978-981-97-1814-6_17(176-189)Online publication date: 5-Aug-2024
  • (2022)A systematic literature review on online assessment securityComputers and Security10.1016/j.cose.2021.102544113:COnline publication date: 6-May-2022

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  1. An intelligent tool for combatting contract cheating behaviour by facilitating scalable student-tutor discussions

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          cover image ACM Conferences
          ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Companion Proceedings
          June 2020
          357 pages
          ISBN:9781450371223
          DOI:10.1145/3377812
          Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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          • KIISE: Korean Institute of Information Scientists and Engineers
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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          Published: 01 October 2020

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          Author Tags

          1. computing assessment
          2. computing education
          3. contract cheating detection
          4. human computer interaction
          5. learning management system
          6. online education
          7. programming education

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          View all
          • (2024)Could the Use of AI in Higher Education Hinder Students With Disabilities? A Scoping ReviewIEEE Access10.1109/ACCESS.2024.336536812(27810-27828)Online publication date: 2024
          • (2024)Transforming Education Through Integrating AI: A Systematic Mapping Review for Enhanced User ExperienceProceedings of TEEM 202310.1007/978-981-97-1814-6_17(176-189)Online publication date: 5-Aug-2024
          • (2022)A systematic literature review on online assessment securityComputers and Security10.1016/j.cose.2021.102544113:COnline publication date: 6-May-2022

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