Ensuring Breadth and Depth of Knowledge on Multiple-Choice Examinations for Board Certification
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| Title: | Ensuring Breadth and Depth of Knowledge on Multiple-Choice Examinations for Board Certification |
|---|---|
| Language: | English |
| Authors: | Heath Kincaid (ORCID |
| Source: | Practical Assessment, Research & Evaluation. 2025 30(1). |
| Availability: | University of Massachusetts Amherst Libraries. 154 Hicks Way, Amherst, MA 01003. e-mail: pare@umass.edu; Web site: https://openpublishing.library.umass.edu/pare/ |
| Peer Reviewed: | Y |
| Page Count: | 16 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Multiple Choice Tests, Certification, Natural Language Processing, Gynecology, Obstetrics, Physicians, Algorithms, Test Content, Content Analysis |
| ISSN: | 1531-7714 |
| Abstract: | Certification organizations aim to assess candidates on their breadth and depth of knowledge to determine eligibility for certification in their field of specialty. Assessments used for certification, when appropriately constructed, should use questions (or items) that assess the entirety of the field. However, comparing the plethora of the content of items to assess content coverage is a lengthy and time-consuming process. In an effort to become more aligned with the purpose of increasing content representativeness, organizations can implement a variety of Natural Language Processing (NLP) techniques with their items to ensure no one concept, medical condition, or scenario presents itself redundantly throughout each of its multiple-choice examinations. We provide an illustrative example from the American Board of Obstetrics and Gynecology (ABOG) of the NLP processes used to increase efficiencies and ensure content representativeness. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1491707 |
| Database: | ERIC |
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