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Abstracts


Purpose: This study explores an integrated approach to identify enemy items in item bank management in a medical licensure examination. Method: The integrated approach utilizes item bank analysis, natural language processing methods by using Cosine Similarity Index, and content analysis and review by subject matter experts. Results: Results from an empirical study indicate that the integrated approach is efficient in identifying enemy items. Content review of the flagged enemy item pairs is a necessary step to confirm enemy items.

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