Automated Item Generation: The Future of Medical Education Assessment-Reference-Cited by-同舟云学术

Automated Item Generation: The Future of Medical Education Assessment

Published:2018-01-09 Issue: Volume: Page:88-93
ISSN:2513-8634
Container-title:EMJ Innovations
language:en
Short-container-title:EMJ Innov

Author:

Royal Kenneth D.¹,Hedgpeth Mari-Wells²,Jeon Tae³,Colford Cristin M.⁴

Affiliation:

1. Department of Clinical Sciences, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, USA; Department of Family Medicine, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA

2. Department of Clinical Sciences, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, USA

3. Educational Support Services, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, USA

4. Department of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA

Abstract

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.

Publisher

European Medical Group

Subject

General Medicine

Reference11 articles.

1. Drasgow F et al., “Technology and testing,” Brennan RL (ed.), Educational Measurement (2006) 4th edition, Washington, DC: American Council on Education, pp.471-516.

2. Rodriguez MC. Three options are optimal for multiple-choice items: A meta-analysis of 80 years of research. Educ Meas. 2005;24(2):3-13.

3. Haladyna TM. Developing and validating multiple-choice test items (2015) 3rd edition, Routledge.

4. Royal KD, Hedgpeth MW. The prevalence of item construction flaws in medical school examinations and innovative recommendations for improvement. EMJ Innov. 2017;1(1):61-6.

5. Rudner L, “Implementing the graduate management admission test computerised adaptive test,” WJ van der Linden, CAW Glas (eds.), Elements of Adaptive Testing (2010). New York: Springer, pp.151-65.

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