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On the Fairness of Multiple-Variant Multiple-Choice Examinations

Published: 22 February 2019 Publication History

Abstract

Academic dishonesty is a widely acknowledged problem in tertiary education, and a range of safeguards and tools exist to both deter and detect cheating. During tests and examinations students are often monitored closely by invigilators, however incidents of misconduct still occur. Multiple-choice questions (MCQs) are particularly susceptible in this regard due to the simplicity with which answers can be communicated. Nonetheless, they are commonly used in large classes due to their convenience. In this work we investigate multiple-variant MCQs, where answer options are randomly selected from predefined sets. This format is less prone to student cheating and overcomes several weaknesses of similar methods. We evaluate this approach in two large computer science courses through student questionnaires and simple item analysis. We find that multiple-variant MCQ exams are perceived as an effective way to reduce cheating behavior, however not all question variants are equally difficult. This introduces concerns around fairness and we discuss approaches to mitigate these issues in the future.

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    cover image ACM Conferences
    SIGCSE '19: Proceedings of the 50th ACM Technical Symposium on Computer Science Education
    February 2019
    1364 pages
    ISBN:9781450358903
    DOI:10.1145/3287324
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 22 February 2019

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    • (2024)Bob or Bot: Exploring ChatGPT's Answers to University Computer Science AssessmentACM Transactions on Computing Education10.1145/363328724:1(1-32)Online publication date: 14-Jan-2024
    • (2023)A narrative review of adaptive testing and its application to medical educationMedEdPublish10.12688/mep.19844.113(221)Online publication date: 24-Oct-2023
    • (2023)Beyond Question Shuffling: Randomization Techniques in Programming Assessment2023 IEEE Frontiers in Education Conference (FIE)10.1109/FIE58773.2023.10342976(1-9)Online publication date: 18-Oct-2023
    • (2023)Are we answering the question that has been set? Exploring the gap between research and practice around examinations in higher educationStudies in Higher Education10.1080/03075079.2023.228378449:11(1928-1944)Online publication date: 27-Nov-2023
    • (2023)Issues of Question Equivalence in Online Exam PoolsJournal of College Science Teaching10.1080/0047231X.2023.1229062952:4(3-5)Online publication date: 30-Aug-2023
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    • (2022)Objective Tests in Automated Grading of Computer Science Courses: An OverviewHandbook on Intelligent Techniques in the Educational Process10.1007/978-3-031-04662-9_12(239-268)Online publication date: 16-Jun-2022
    • (2021)The Impact of Multiple Choice Question Design on Predictions of PerformanceProceedings of the 23rd Australasian Computing Education Conference10.1145/3441636.3442306(66-72)Online publication date: 2-Feb-2021
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