Suppr超能文献

谁在我们的STEM课程中,我们又如何得知?学生的自我描述、交叉性与全纳教育。

Who is in Our STEM Courses and How do We Know? Student Self-Descriptions, Intersectionality and Inclusive Education.

作者信息

Hanauer David I, Zhang Tong, Graham Mark, Hatfull Graham

机构信息

Indiana University of Pennsylvania, Indiana, PA 15206.

Duke Kunshan University, Suzhou, China 215316.

出版信息

CBE Life Sci Educ. 2025 Mar 1;24(1):ar9. doi: 10.1187/cbe.24-02-0078.

Abstract

The aim of inclusive education is to provide a supportive space for students from every background. The theory of intersectionality suggests that multiple identities intersect within social spaces to construct specific positionalities. To support the heterogeneity of all students, there is a need to understand who is in our Science, Technology, Engineering and Mathematics (STEM) courses and how we would go about assessing this. This article problematizes the traditional approach to demographic data collection and presents the beginnings of an alternative approach. The study utilized qualitative and quantitative data in order to examine the way students self-describe within a large multi-institutional program. There were 2,082 students presented with 12 identity categories and asked to specify which of these identities were important to them for their own self-definition and then write an open self-description. The data was analyzed using descriptive statistics, comparative proportional usage analyses of identity categories by traditional demographic groupings, and hierarchical cluster analysis of identity variables. The results showed that the majority of students use multiple categories of identity in combination, that these identity preferences differ in relation to traditional demographic categories, and that there were four underpinning identity orientations consisting of a focus on heritage, health, self-expression, and career.

摘要

全纳教育的目标是为来自各种背景的学生提供一个支持性的空间。交叉性理论表明,多种身份在社会空间中相互交织,构建出特定的位置。为了支持所有学生的多样性,有必要了解哪些学生选修了我们的科学、技术、工程和数学(STEM)课程,以及我们将如何进行评估。本文对传统的人口数据收集方法提出了质疑,并提出了一种替代方法的初步设想。该研究利用定性和定量数据,以考察学生在一个大型多机构项目中自我描述的方式。共有2082名学生面对12种身份类别,并被要求指明其中哪些身份对他们自身的自我定义很重要,然后撰写一篇开放式的自我描述。数据分析采用了描述性统计、按传统人口分组对身份类别的比较比例使用分析,以及身份变量的层次聚类分析。结果表明,大多数学生综合使用多种身份类别,这些身份偏好因传统人口类别而异,并且有四种基本的身份取向,分别侧重于传统、健康、自我表达和职业。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4038/11974525/0e4ccd4cc7b3/cbe-24-ar9-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