Official publication of Rawalpindi Medical University
Enhancing Assessment Integrity: The Role of Plausible Distractors in Multiple-Choice Question Design
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Keywords

medical students
Medical Education
Distractors
Exam Design
MCQ development
Assessment
Item analysis

How to Cite

1.
Ansari SK, Zehra F, Asifa GZ, Aslam N. Enhancing Assessment Integrity: The Role of Plausible Distractors in Multiple-Choice Question Design. JRMC [Internet]. 2026 Jun. 30 [cited 2026 Jul. 13];30(2):166-70. Available from: https://journalrmc.com/index.php/JRMC/article/view/2908

Abstract

Objective: The study aims to untangle the intricate relationship between distractors in MCQs and their impact on learning and assessment outcomes within the constructivist framework in medical education.

Methods: At Niazi Medical and Dental College (NMDC), Sargodha, the Midterm exam period was different in the 3rd, 4th, and 5th-year MBBS (total sample size is 320); therefore, the time period for assessment was five (05) months from May to September 2024.  Each sessional year in conventional learning has four (04) different subjects for assessments with equal numbers of MCQs posing a single best answer(SBA), that is, 45 MCQs in all subjects except Pharmacology and Pathology, which have 65 MCQs per subject in the midterm assessment. The total average of Non-Plausible Distractor (NPDs) was 1500 options (40%), which was later used as new 300 MCQs, leading to questions in the stem for the next summative assessment.

Results: The exploration of question production exposed that across the papers counted in this study, the regular number of PDs per Item (<5% of the cohort) was 1.91.  In total, 600 MCQ-SBA with 3000 options (2400 distractors and 600 correct responses) were examined. The reassembly of 300 MCQs with 1,500 options was then supplemented with well-constructed PDs, and the results were remarkable. After applying the paired sample t-tests, the result was remarkable (p= .000).

Conclusions: For MCQs to effectively assess advanced medical education, distractors must be plausibly related to the content being tested, challenging yet not misleading, and relevant to the curriculum.

Key words: Assessment, Critical Thinking, Design, Exam, Medical education, Medical Students, Plausible, Questions

https://doi.org/10.37939/jrmc.v30i2.2908
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References

Puthiaparampil T, Rahman MM, Gudum HR, Brohi IB, Lim IF, Saimon R. How to grade items for a question bank and rank tests based on student performance. MedEdPublish 2020;9(1). https://doi.org/10.15694/mep.2020.000260.1

Shin J, Guo Q, Gierl MJ. Multiple-choice item distractor development using topic modeling approaches. Front Psychol 2019;10(APR):1–14. https://doi.org/10.3389/fpsyg.2019.00825

Amo-Salas M, Arroyo-Jimenez MDM, Bustos-Escribano D, Fairén-Jiménez E, López-Fidalgo J. New indices for refining multiple choice questions. J Probab Stat 2014;2014. https://doi.org/10.1155/2014/240263

Farooq M-Z, Mashood S. Quality Assurance of Multiple-Choice Questions Test Through Item Analysis. Life and Science 2023;4(4):7. https://doi.org/10.37185/LnS.1.1.315

Testa S, Toscano A, Rosato R. Distractor efficiency in an item pool for a statistics classroom exam: Assessing its relation with item cognitive level classified according to Bloom’s taxonomy. Front Psychol 2018;9(AUG):1–12. https://doi.org/10.3389/fpsyg.2018.01585

Kurtz JB, Lourie MA, Holman EE, Grob KL, Monrad SU. Creating assessments as an active learning strategy: what are students’ perceptions? A mixed methods study. Med Educ Online 2019;24(1). https://doi.org/10.1080/10872981.2019.1630239

Maliha Ansari, Rabia Sadaf, Aisha Akbar, Sabahat Rehman, Zunnera Rashid Chaudhry, Sabeen Shakir. Assessment of distractor efficiency of MCQS in item analysis. The Professional Medical Journal 2022;29(05):730–734. https://doi.org/10.29309/TPMJ/2022.29.05.6955

Hingorjo MR, Jaleel F. Analysis of one-best MCQs: The difficulty index, discrimination index, and distractor efficiency. J Pak Med Assoc 2012;62(2):142–147.

Bitew SK, Deleu J, Develder C, Demeester T. Distractor generation for multiple-choice questions with predictive prompting and large language models. InJoint European Conference on Machine Learning and Knowledge Discovery in Databases 2023 Sep 18 (pp. 48-63). Cham: Springer Nature Switzerland. https://doi.org/10.48550/arXiv.2307.16338

Ludewig U, Schwerter J, McElvany N. The Features of Plausible but Incorrect Options: Distractor Plausibility in Synonym-Based Vocabulary Tests. J Psychoeduc Assess 2023;41(7):711–731. https://doi.org/10.1177/07342829231167892

Memon MA, Thurasamy R, Ting H, Cheah JH. CONVENIENCE SAMPLING: A REVIEW AND GUIDELINES FOR QUANTITATIVE RESEARCH. Journal of Applied Structural Equation Modeling 2025;9(2). https://doi.org/10.47263/JASEM.9(2)01

Benjamin AS, Bawa S. Distractor plausibility and criterion placement in recognition. J Mem Lang 2004;51(2):159–172. https://doi.org/10.1016/j.jml.2004.04.001

Taladngoen U, Esteban RH. Assumptions on Plausible Lexical Distractors in the Redesigned TOEIC Question-Response Listening Test. LEARN Journal: Language Education and Acquisition Research Network. 2022;15(2):802-29.

Velou M, Ahila E. Refine the Multiple Choice Questions Tool with Item Analysis. Iaim 2020;7(8):80–85.

Shakurnia A, Ghafourian M, Khodadadi A, Ghadiri A, Amari A, Shariffat M. Evaluating Functional and Non-Functional Distractors and Their Relationship with Difficulty and Discrimination Indices in Four-Option Multiple-Choice Questions. Education in Medicine Journal 2022;14(4):55–62. https://doi.org/10.21315/eimj2022.14.4.5

Ha LA, Yaneva V, Baldwin P, Mee J. Predicting the difficulty of multiple choice questions in a high-stakes medical exam. ACL 2019 - Innovative Use of NLP for Building Educational Applications, BEA 2019 - Proceedings of the 14th Workshop 2019;11–20. https://doi.org/10.18653/v1/W19-4402

Kurdi G, Leo J, Matentzoglu N, Parsia B, Sattler U, Forge S, et al. A comparative study of methods for a priori prediction of MCQ difficulty. Semantic Web. 2021 Mar 9;12(3):449-65.https://doi.org/10.3233/SW-200390

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Copyright (c) 2026 Sadaf Konain Ansari, Farheen Zehra, Gul Zareen Asifa, Nazia Aslam