Using data analytics to detect possible collusion in a multiple choice quiz test
Lang, Michael
Lang, Michael
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Publication Date
2021-08-25
Type
Conference Paper
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Citation
Lang, M. (2021). Using Data Analytics to Detect Possible Collusion in a Multiple Choice Quiz Test. In: Dennehy, D., Griva, A., Pouloudi, N., Dwivedi, Y.K., Pappas, I., Mäntymäki, M. (eds) Responsible AI and Analytics for an Ethical and Inclusive Digitized Society. I3E 2021. Lecture Notes in Computer Science, vol 12896. Springer, Cham. https://doi.org/10.1007/978-3-030-85447-8_63
Abstract
This paper reports on the experiences of using an on-line MCQ test to assess students’ knowledge for a postgraduate module. Because of the COVID-19 pandemic, the test was taken in a remote non-proctored environment. Although it was executed under timed conditions with students seeing questions in a randomised order, algorithmic analysis of the response patterns suggests that collusion occurred during the test. Practical implications for assessment design and administration are discussed.
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Publisher
Springer
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CC BY-NC-ND 3.0 IE