City Research Online

Abstract

Driven by technological innovations, continuous digital expansion has transformed fundamentally the landscape of modern higher education, leading to discussions about evaluation techniques. The emergence of generative artificial intelligence raises questions about reliability and academic honesty regarding multiple-choice assessments in online education. In this context, this study investigates multiple-answer questions (MAQs) versus traditional single-answer questions (SAQs) in online higher-education assessments. A mixed-methods study involving quantitative field experiments and qualitative interviews was conducted with students enrolled in an online Marketing M.Sc. program. The students were divided randomly and assessed using either SAQs or MAQs, and the impacts on test performance of variables such as grade averages, study times, perceived workload, and difficulty were evaluated using independent-sample t-tests and ordinary least-squares regression analysis. The results show that although grades were lower and MAQs were perceived as being more difficult, study times and perceived workload did not differ significantly between the two formats. These findings suggest that despite their challenge, MAQs can promote deeper understanding and greater learning retention. Furthermore, even with their higher perceived difficulty and impact on performance, MAQs hold potential for dealing with academic-integrity concerns related to artificial intelligence.

Publication Type:	Article
Additional Information:	© 2024 The Author(s). Published with license by Taylor & Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-ncnd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
Publisher Keywords:	multiple-choice question; online assessment; online quiz, attainment gap, mixed-methods study, field experiment
Subjects:	H Social Sciences > HM Sociology L Education > LB Theory and practice of education > LB2300 Higher Education
Departments:	Bayes Business School Bayes Business School > Management
SWORD Depositor:	Symplectic Administrator

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