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Towards Academic Integrity: Using Bloom’s Taxonomy and Technology to Deter Cheating in Online Courses

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Technologies, Artificial Intelligence and the Future of Learning Post-COVID-19

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1019))

Abstract

One of the biggest challenges being faced in the higher education sector is academic dishonesty. While this phenomenon has been in existence for a long time, it is understood that the transition to remote online teaching and assessment due to the Covid-19 pandemic has exacerbated the challenge in higher education institutions (HEIs) across the globe. This chapter focuses on the discussion of the concept of academic dishonesty and the importance of ensuring academic integrity in HEIs in general and highlights some approaches that can be judiciously deployed by academicians to uphold academic integrity. This includes the use of Bloom’s Taxonomy in designing assessments and the creative use of technology to mitigate and deter cheating in online courses. The chapter makes a valuable contribution by providing some practical ideas that academicians can apply to enhance academic integrity while maintaining inclusivity and equitable student outcomes.

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Author information

Authors and Affiliations

  1. Skyline University College, Sharjah, United Arab Emirates

    Kakul Agha

  2. The Open University, Milton Keynes, UK

    Xia Zhu

  3. University of Bradford, Bradford, UK

    Gladson Chikwa

Authors
  1. Kakul Agha
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  2. Xia Zhu
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  3. Gladson Chikwa
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Correspondence to Kakul Agha .

Editor information

Editors and Affiliations

  1. College of Business and Finance, Ahlia University, Manama, Bahrain

    Allam Hamdan

  2. College of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  3. AACSB International, Tampa, FL, USA

    Timothy Mescon

  4. Majan University College, Muscat, Oman

    Bahaaeddin Alareeni

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Agha, K., Zhu, X., Chikwa, G. (2022). Towards Academic Integrity: Using Bloom’s Taxonomy and Technology to Deter Cheating in Online Courses. In: Hamdan, A., Hassanien, A.E., Mescon, T., Alareeni, B. (eds) Technologies, Artificial Intelligence and the Future of Learning Post-COVID-19. Studies in Computational Intelligence, vol 1019. Springer, Cham. https://doi.org/10.1007/978-3-030-93921-2_25

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