Comparing the effectiveness of multiple-answer and single-answer multiple-choice questions in assessing student learning

The continued expansion of the digital age, driven by technological innovations, has fundamentally transformed the landscape of modern higher education services, leading to discussions about evaluation techniques. The emergence of generative artificial intelligence (AI) applications has raised questions about the reliability and academic honesty of multiple-choice question assessments within the realm of online education. In this context, this study investigates the effects of multiple-answer questions (MAQs) versus traditional single-answer questions (SAQs) within online higher education assessments. We conducted a mixed-methods study with quantitative field experiments and qualitative interviews with students enrolled in an Online Marketing MSc programme. Students were divided randomly and assessed using different structures: SAQs or MAQs. We evaluated variables such as grade averages, study times, perceived workload, and difficulty using independent sample t-tests. The effect of independent variables on coursework grades was explored via regression analysis. We found that, although grades were lower and MAQs were perceived as more difficult, study times and perceived workload showed no significant differences between formats. These findings suggest that MAQs—despite their challenge—can promote deeper understanding and greater learning retention. Furthermore, even with the higher perceived difficulty and impact on performance, MAQs hold potential for dealing with AI-related academic integrity concerns. However, careful implementation and further research are recommended to explore best practices in integrating MAQs into online assessment design.