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Acad Radiol. 2019 Jun;26(6):851-859. doi: 10.1016/j.acra.2018.09.017. Epub 2018 Oct 10.

Automated Test-Item Generation System for Retrieval Practice in Radiology Education.

Author information

1
Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 333 Cedar St, P O Box 208042, New Haven, Connecticut 06520-8042. Electronic address: gowthaman7@gmail.com.
2
Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 333 Cedar St, P O Box 208042, New Haven, Connecticut 06520-8042. Electronic address: caroline.taylor@yale.edu.
3
Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 333 Cedar St, P O Box 208042, New Haven, Connecticut 06520-8042. Electronic address: mahan.mathur@yale.edu.
4
Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 333 Cedar St, P O Box 208042, New Haven, Connecticut 06520-8042. Electronic address: jamal.bokhari@yale.edu.
5
Department of Radiology and Biomedical Imaging, Vice-Chair of Education, Yale University School of Medicine, New Haven, Connecticut. Electronic address: leslie.scoutt@yale.edu.

Abstract

OBJECTIVE:

To develop and disseminate an automated item generation (AIG) system for retrieval practice (self-testing) in radiology and to obtain trainee feedback on its educational utility.

MATERIALS AND METHODS:

An AIG software program (Radmatic) that is capable of generating large numbers of distinct multiple-choice self-testing items from a given "item-model" was created. Instead of writing multiple individual self-testing items, an educator creates an "item-model" for one of two distinct item styles: true/false knowledge based items and image-based items. The software program then uses the item model to generate self-testing items upon trainee request. This internet-based system was made available to all radiology residents at our institution in conjunction with our didactic conferences. After obtaining institutional review board approval and informed consent, a written survey was conducted to obtain trainee feedback.

RESULTS:

Two faculty members with no computer programming experience were able to create item-models using a standard template. Twenty five of 54 (46%) radiology residents at our institution participated in the study. Twelve of these 25 (48%) study participants reported using the self-testing items regularly, which correlated well with the anonymous website usage statistics. The residents' overall impression and satisfaction with the self-testing items was quite positive, with a score of 7.89 ± 1.91 (mean ± SD) out of 10. Lack of time and email overload were the main reasons provided by residents for not using self-testing items.

CONCLUSION:

AIG enabled self-testing is technically feasible, and is perceived positively by radiology residents as useful to their education.

KEYWORDS:

Automated item-generation; Radiology education; Retrieval practice

PMID:
30316703
DOI:
10.1016/j.acra.2018.09.017

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