Expanding STEM Talent Through Upward Transfer: Factors Influencing Transfer in STEM Fields of Study from Two-Year to Four-Year Institutions

通过向上转移扩大 STEM 人才：影响 STEM 学习领域从两年制到四年制机构转移的因素

• 批准号: 1430642
• 负责人: Xueli Wang
• 金额: $ 140万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1430642&HistoricalAwards=false
• 关键词: Expanding; STEM; Talent; Through; Upward

Two-year institutions are essential components of the nation's education system and increasingly recognized as critically important in established and emerging areas of science, technology, engineering, and mathematics (STEM). However, there is a limited body of research examining what influences student transfer from two-year colleges into STEM fields of study at four-year institutions. Drawing upon social cognitive career theory, the project will develop and refine a STEM transfer model that hypothesizes that STEM transfer is influenced by demographic factors, contextual factors, learning experiences in STEM, and motivational beliefs related to STEM learning and transfer. The four-year longitudinal mixed methods research project will investigate STEM transfer and will advance and test a model of postsecondary STEM pathways to the baccalaureate and careers in STEM or to alternative educational and occupational attainment in STEM. The research team will follow a cohort of 3,000 students beginning in STEM programs or courses required for STEM programs at two-year community and technical colleges in Wisconsin and study factors that facilitate or hinder their transfer into a STEM major at 4-year colleges and universities during the study period. The research adopts an explanatory sequential mixed methods design in which the quantitative approach is dominant and is followed by a complementary qualitative approach. The quantitative phase will consist of collection and analysis of students' administrative records from participating institutions integrated with students' data from a new longitudinal survey on STEM transfer. To establish construct validity, the research team will perform exploratory factor analysis to identify the factor structure. This will be followed by a confirmatory factor analysis. Additional factor analyses will be ongoing as each wave of the survey data becomes available. Three waves of data collection will be conducted. Structural equation modeling (SEM) will be used to analyze the multivariate data and to test the theoretical framework. SEM defines latent and observed variables while testing the direct and indirect relationships among variables that are hypothesized. The interview and focus group sample will be purposively selected from survey participants. While specific selection criteria will depend on the analysis of the survey data, it is anticipated that a combination of intensity sampling, maximum variation sampling and/or homogenous sampling will be the most viable strategies to provide more depth and insight into the factors influencing STEM transfer as identified by quantitative findings. Deductively guided and inductively grounded data analyses will be utilized to discover common themes across interview and focus group qualitative data. The project will advance the knowledge base on STEM transfer and contribute to theory building in this important area of study.

两年制院校是国家教育体系的重要组成部分，并且越来越被认为在科学、技术、工程和数学 (STEM) 等成熟和新兴领域中至关重要。 然而，关于影响学生从两年制大学转入四年制大学 STEM 学习领域的研究数量有限。 该项目将利用社会认知职业理论，开发和完善一个 STEM 转移模型，该模型假设 STEM 转移受到人口因素、背景因素、STEM 学习经历以及与 STEM 学习和转移相关的动机信念的影响。 这个为期四年的纵向混合方法研究项目将调查 STEM 转移，并将推进和测试中学后 STEM 通往 STEM 学士学位和职业或 STEM 替代教育和职业成就的途径模型。 研究小组将跟踪 3,000 名在威斯康星州两年制社区和技术学院开始 STEM 课程或 STEM 课程所需课程的学生，并研究促进或阻碍他们转入四年制学院和大学 STEM 专业的因素学习期间。 该研究采用解释性序贯混合方法设计，其中定量方法占主导地位，定性方法为辅。 定量阶段将包括收集和分析参与机构的学生行政记录，并结合新的 STEM 转学纵向调查的学生数据。 为了建立结构效度，研究团队将进行探索性因素分析来确定因素结构。 随后将进行验证性因素分析。 随着每一波调查数据的出现，其他因素分析将持续进行。 将进行三波数据收集。 结构方程模型（SEM）将用于分析多变量数据并测试理论框架。 SEM 定义潜在变量和观察到的变量，同时测试假设的变量之间的直接和间接关系。 访谈和焦点小组样本将有目的地从调查参与者中挑选。 虽然具体的选择标准将取决于调查数据的分析，但预计强度抽样、最大变异抽样和/或同质抽样的组合将是最可行的策略，可以更深入地了解影响 STEM 转移的因素通过定量研究结果确定。 将利用演绎引导和归纳基础的数据分析来发现访谈和焦点小组定性数据中的共同主题。 该项目将推进 STEM 转移的知识基础，并有助于这一重要研究领域的理论建设。