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Updated: Feb 19, 2026

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
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学生をナラティブアセスメントで階層化するために自然言語処理を使用する際の課題 医学教育の大学院でのナラティブアセスメント

Laurah Turner1, Christine Yang Zhou2, Danielle Weber3

  • 1Artificial Intelligence and Educational Informatics and Department of Medical Education, University of Cincinnati College of Medicine, Cincinnati, OH, United States.

Academic medicine : journal of the Association of American Medical Colleges
|February 17, 2026
PubMed
まとめ

自然言語処理 (NLP) モデルは,医学生のパフォーマンスを特定するのに十分なパフォーマンスを示したが,リスクのある学生を特定することに失敗した. NLPトレーニングに"コピー/ペースト"のコメントを加えたことで,高性能と低性能の両方を識別するモデルの精度が向上しました.

キーワード:
評価 評価する 評価する医学部生である学生です.ネラティブアセスメント (ナラティブアセスメント)自然言語処理は自然言語の処理である.

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