Towards Academic Integrity: Using Bloom’s Taxonomy and Technology to Deter Cheating in Online Courses
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Remote settings weaken student-instructor connections and make academic dishonesty easier for students to rationalize.
“Studies by Swartz and Cole (2013) and Agha, Zhu, & Chikwa (2022) show that remote settings weaken student-instructor connections and make academic dishonesty easier for students to rationalize.”
From Generative AI-Enhanced Oral Exams in Economics Education (2026) · cites this paper as Agha, Zhu, & Chikwa (2022)
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- Authors
- Agha, K., Zhu, X., & Chikwa, G.
- Affiliations
- Skyline University College (AE)
- The Open University (GB)
- University of Bradford (GB)
- Corresponding author
- Agha, Kakul
- Venue
- Studies in Computational Intelligence
- Publisher
- Springer International Publishing
- Published
- 2022
- Pages
- 447-466
- ISSN
- 1860-949X (ISSN-L)
- 1860-9503
- OpenAlex
- W4213065959
- Cited by
- 6
- References
- 52
- Citations by year
- 2026: 1 · 2025: 2 · 2023: 1 · 2022: 2
- FWCI
- 4.46
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Studies in computational intelligence (publishedVersion)
- Open Research Online (The Open University) (submittedVersion)
- Topics
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology in Education and Learning
- Keywords
- academic integrity, academic dishonesty, cheating prevention, Bloom's taxonomy, online courses, assessment design, educational technology, higher education, COVID-19 remote teaching, inclusivity
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