The 10 most wanted test cheaters in medical education

Royal, Kenneth D.; Hedgpeth, Marian-Wells; Mulkey, Jamie R.; Fremer, John J.

doi:10.1111/medu.13096

Cited by 7 publications

(10 citation statements)

References 20 publications

(25 reference statements)

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“…The adverse effect of this pressure is to frequently compare their work with that of others. The effectiveness of our system on students is not only to reduce cheating but also to reinforce the idea of a safe environment that reduces stress among students toward a fair environment for testing [2].…”

Section: Resultsmentioning

confidence: 94%

“…Throughout this study, we prefer to use the term "cheating" to provide clarity to the context. A study [2] showed that the prevention of cheating is essential as test validity is crucial to overall learning, especially in medical schools. The method of cheating varies by classroom; they include the use of hidden notes, and using cellphones to get extra help or to cheat.…”

Section: Introductionmentioning

confidence: 99%

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Relationship Between Variables of pre- and post-Randomized Questions in Computer-based Tests

Sung¹

2021

APJCRI

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Cheating during assessments can be a result of poor physical arrangement of exam settings. In multiple-choice question of computer-based tests (CBT), shuffling items is used to deter students from copying answers from their colleagues. Randomizing questions and shuffling items in multiplechoice questions are meant to set a copying-disabled environment for students. This study aims to statistically verify that shuffling is suitable for preventing cheating using quantitative data. Since object awareness may compromise the effectiveness of a survey method, we used quantitative data from the students' selection of items without them being aware and to further avoid compromising the data. A total of 472 CBT data from two academic semester years of 2013 and 2016 were collected from first-year students enrolled in two exams of Cell Regulation Mechanism and Basic Human Structure. Participants data collected from test score, multiple-question selection number, and seat number. The question item number of each student was compared with the item selection of five nearby students seated in different positions. Item selection data from students enrolled in Basic Human Structure exam compared to five nearby colleagues showed a correlation coefficient of less than 0.8, indicating that there were no cases of cheating confirmed. In the case of Cell Regulation Mechanism exam, by ignoring the front seat position, (considered limitation of this study) the measurement of comparing individual students to five nearby colleagues showed a correlation coefficient above 0.8 among 13 students (n=13), about 5.8% of total students, meaning 5% of students either cheat or selected items based on their knowledge. Practically, by simply looking at the monitor of a colleague, justifying cheating is difficult; however, the measurement of quantitative data produced by the students'selection of items showed that question-shuffling and item randomization reduce cheating among students. The results of this study is also intended to introduce the feeling of a safer environment by justifying that shuffling would deter cheating.

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Section: Resultsmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Relationship Between Variables of pre- and post-Randomized Questions in Computer-based Tests

Sung¹

2021

APJCRI

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“…For example, a best practice in testing recommends faculty staff alter their exams each year as a preventative measure to combat cheating, as students often share information about items appearing on exams. 12 When items are replaced with new ones, it is unlikely that the new items are any better in terms of construction quality, especially if the items were generated as last minute substitutes which faculty staff acknowledge is often the case. Of course, the extent to which faculty staff heed recommendations about improving their exams also remains unknown.…”

Section: Discussion Substantive Resultsmentioning

confidence: 99%

The Prevalence of Item Construction Flaws in Medical School Examinations and Innovative Recommendations for Improvement

Royal

Hedgpeth

2017

EMJ Innov

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The purpose of this study was to calculate the prevalence and nature of item construction flaws within one large medical school and to identify several innovative approaches that may serve as potential remedies for these problems. Results indicated that approximately one in five items contained a construction flaw, with the overwhelming majority of flaws involving poor quality distractors. A series of innovative recommendations are presented, including modern psychometric analytical techniques to more thoroughly inspect data, item manipulation techniques, and the use of innovative item types that may alleviate the need for distractors altogether.

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“…Finally, given the high stakes associated with medical education assessments and the pressures to perform well, some unscrupulous individuals will attempt to harvest items, thus rendering them ineffective for all future examinations. 6,7 Each of the aforementioned limitations, coupled with increased demands for new and more numerous items for both high-stakes examinations and practice tests, have made it difficult for the field of medical education to keep pace with current demands. Fortunately, a major breakthrough in psychometric science, called automated item generation (AIG), holds the potential to overcome many of the weaknesses and challenges associated with MCQ.…”

Section: Automated Item Generation: the Future Of Medical Education A...mentioning

confidence: 99%

Automated Item Generation: The Future of Medical Education Assessment

et al. 2018

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A major innovation in psychometric science, termed automated item generation (AIG), holds the potential to revolutionise assessment in medical education. In short, AIG involves leveraging the expertise of content specialists, item templates, and computer algorithms to create a variety of item permutations, often resulting in hundreds or thousands of new items based on a single item model. AIG may significantly improve item writing capabilities, reduce human error, streamline efficiencies, and reduce costs for individuals in the medical and health professions. Thus, the purpose of this work is to provide readers with a current overview of AIG and discuss its potential advantages, future possibilities, and current limitations.

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The 10 most wanted test cheaters in medical education

Cited by 7 publications

References 20 publications

Relationship Between Variables of pre- and post-Randomized Questions in Computer-based Tests

Relationship Between Variables of pre- and post-Randomized Questions in Computer-based Tests

The Prevalence of Item Construction Flaws in Medical School Examinations and Innovative Recommendations for Improvement

Automated Item Generation: The Future of Medical Education Assessment

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