Suppr超能文献

使用主题模型来映射和分析大型课程。

Using a topic model to map and analyze a large curriculum.

机构信息

Department of Cell Biology, Yale University School of Medicine, New Haven, Connecticut, United States of America.

出版信息

PLoS One. 2023 Apr 20;18(4):e0284513. doi: 10.1371/journal.pone.0284513. eCollection 2023.

Abstract

A qualitative and quantitative understanding of curriculum content is critical for knowing whether it's meeting its learning objectives. Curricula for medical education present challenges due to amount of content, the diversity of topics and the large number of contributing faculty. To create a manageable representation of the content in the pre-clerkship curriculum at Yale School of Medicine, a topic model was generated from all educational documents given to students during the pre-clerkship period. The model was used to quantitatively map content to school-wide competencies. The model measured how much of the curriculum addressed each topic and identified a new content area of interest, gender identity, whose coverage could be tracked over four years. The model also allowed quantitative measurement of integration of content within and between courses in the curriculum. The methods described here should be applicable to curricula in which texts can be extracted from materials.

摘要

对课程内容进行定性和定量的理解,对于了解它是否达到学习目标至关重要。由于医学教育课程的内容数量、主题多样性以及众多参与教学的教师,因此给医学教育课程的内容带来了挑战。为了在耶鲁医学院的预科课程中创建一个可管理的课程内容表示形式,从预科期间提供给学生的所有教育文档中生成了一个主题模型。该模型用于将内容定量映射到全校范围内的能力上。该模型测量了课程涵盖了每个主题的程度,并确定了一个新的内容领域,即性别认同,其涵盖范围可以在四年内进行跟踪。该模型还允许对课程内和课程之间的内容整合进行定量测量。此处描述的方法应该适用于可以从材料中提取文本的课程。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eefc/10118121/863a0b369142/pone.0284513.g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验