STEM-R: Modeling STEM Retention and Departure across Physics, Mathematics, and Engineering

STEM-R：跨物理、数学和工程的 STEM 保留和离开建模

• 批准号: 1561517
• 负责人: John Stewart
• 金额: $ 30万
• 依托单位: West Virginia University Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1561517&HistoricalAwards=false
• 关键词: STEM; Modeling; Retention; Departure; across

Researchers at West Virginia University will develop a new theoretical framework for STEM departure that will detail the reasons why students leave STEM majors. The research extends Tinto's university departure model to include the career exploration process where a student leaves STEM but remains in college. The framework will be developed and tested by extensive measurement of demographic, social, academic, affective (self-efficacy, self-esteem, belonging), career exploration/aspirations and psychological variables at four longitudinal points in physics and mathematics introductory class sequences required for many STEM majors. The longitudinal measurement monitors evolution of a student's STEM career commitment and vocational identity, and determines factors that influence a decision to begin the process of STEM departure or lead to a well-investigated resolution to pursue a STEM career. The research will explore crucial questions influencing university STEM departure, including to what extent departure is preventable by modifying advising protocols, to what extent performance in these gate-keeper classes and its effect on self-efficacy influence the decision to change major, what psychological/social factors mark students beginning to explore non-STEM careers, and to what extent belonging influences retention, particularly of underrepresented women and rural students. The research will contribute to a deeper understanding of this important phenomenon. It also will inform the national discussion of STEM retention by producing a theoretical framework of STEM departure validated across a large, economically diverse pool of students. The research design will implement a multi-stage analysis and measurement with the goal of modeling the process of STEM departure and quantifying the reasons for and markers of the departure process. Researchers will investigate in depth: (1) what modifications are needed in theoretical frameworks for college departure to explain intra-university departure from STEM disciplines, (2) the minimum information required to predict STEM departure and where STEM departure originates, (3) what fraction of STEM departure could be prevented by interventions, (4) how STEM departure markers differ for underserved populations, and (5) how to identify subpopulations of students where interventions will be most effective. Researchers will use institutional class-taking and outcome data to build a set of "survival" probabilities for individual classes and class-taking sequences. Linear regression analysis then be used to analyze how these probabilities are affected by (1) the student's intrinsic characteristics (ability measured by ACT/SAT, race and ethnicity, socioeconomic status, first generation status, and personality profile), (2) the student's academic preparation (high school GPA and high school course-taking patterns), (3) the student's academic performance in his or her current class (measured by attendance patterns and assignment grades), (4) the students current social connectedness to both family and university structures and communities, and (5) the student's current affective state, his or her self-efficacy, sense of belonging, self-esteem, and STEM identity. Latent growth modeling will track changes in these factors over time. In parallel, the student's vocational identity status, his or her state of career exploration and career decision making, will be monitored. Using the four-point longitudinal measurement, transitions in the student's vocational identity state (particularly changes that indicate a renewed career exportation process and therefore the threat of STEM departure) will be correlated with previous changes in survival probabilities and with changes in the student's affective, academic, or social state. Signatures to inform advisers of at risk students and general changes to advising protocols will be developed. Campus social/academic structures that promote retention will be identified. At all levels, differential results for underrepresented students will be investigated with the goal of designing interventions that target these populations.This project is supported by NSF's EHR Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in three areas: STEM learning and learning environments, broadening participation, and workforce development.

西弗吉尼亚大学的研究人员将开发一个新的STEM离职理论框架，详细说明学生离开STEM专业的原因。这项研究扩展了Tinto的大学离职模型，将学生离开STEM但仍留在大学的职业探索过程包括在内。该框架将通过对人口统计、社会、学业、情感(自我效能、自尊、归属感)、职业探索/抱负和心理变量的广泛测量，在物理和数学入门课程序列中的四个纵向点进行开发和测试，这是许多STEM专业所需的。纵向测量监测学生STEM职业承诺和职业认同的演变，并确定影响决定开始STEM离开过程或导致充分调查的解决方案追求STEM职业的因素。这项研究将探索影响大学STEM离职的关键问题，包括通过修改建议规程在多大程度上可以防止偏离，这些看门人班级的表现及其对自我效能的影响在多大程度上影响了转学专业的决定，哪些心理/社会因素标志着学生开始探索非STEM职业，以及归属感在多大程度上影响保留，特别是代表不足的女性和农村学生。这项研究将有助于更深入地理解这一重要现象。它还将通过制定STEM离开的理论框架，为全国关于STEM保留的讨论提供信息，该框架在一大批不同经济背景的学生中得到验证。研究设计将实施多阶段分析和测量，目的是对STEM离开过程进行建模，并量化离开过程的原因和标志。研究人员将深入调查：(1)需要对大学内部离开STEM学科的理论框架进行哪些修改，(2)预测STEM离开和STEM离开起源所需的最少信息，(3)干预措施可以防止多大比例的STEM离开，(4)服务不足人群的STEM离开标记有何不同，以及(5)如何确定干预措施将最有效的学生亚群。研究人员将使用机构上课和结果数据来为个别班级和上课序列建立一组“生存”概率。然后用线性回归分析来分析这些概率是如何受以下因素影响的：(1)学生的内在特征(通过ACT/SAT、种族和民族、社会经济地位、第一代地位和人格特征来衡量)，(2)学生的学业准备(高中GPA和高中选课模式)，(3)学生在当前班级的学业表现(通过出勤模式和作业成绩来衡量)，(4)学生目前与家庭和大学结构和社区的社会联系，以及(5)学生目前的情感状态，他或她的自我效能感、归属感、自尊和STEM身份。潜在增长模型将跟踪这些因素随时间的变化。同时，学生的职业身份状态、他或她的职业探索和职业决策状态将受到监控。使用四点纵向测量，学生职业认同状态的转变(特别是表明新的职业输出过程的变化，因此STEM离开的威胁)将与先前生存概率的变化以及学生情感、学业或社会状态的变化相关。将制定通知高危学生顾问的签名和对建议协议的一般修改。将确定促进留住的校园社会/学术结构。在所有层面上，将对代表性不足学生的不同结果进行调查，目的是设计针对这些人群的干预措施。该项目得到了NSF的EHR核心研究(ECR)计划的支持。ECR计划强调基础STEM教育研究，在三个领域产生基础知识：STEM学习和学习环境、扩大参与和劳动力发展。

项目成果

Using machine learning to predict physics course outcomes

使用机器学习来预测物理课程结果

• DOI: 10.1103/physrevphyseducres.15.020120
• 发表时间: 2019
• 期刊: Physical Review Physics Education Research
• 影响因子: 3.1
• 作者: Zabriskie, Cabot;Yang, Jie;DeVore, Seth;Stewart, John
• 通讯作者: Stewart, John

Mediational effect of prior preparation on performance differences of students underrepresented in physics

事先准备对物理学中代表性不足的学生的表现差异的中介作用

• DOI: 10.1103/physrevphyseducres.17.010107
• 发表时间: 2021
• 期刊: Physical Review Physics Education Research
• 影响因子: 3.1
• 作者: Stewart, John;Cochran, Geraldine L.;Henderson, Rachel;Zabriskie, Cabot;DeVore, Seth;Miller, Paul;Stewart, Gay;Michaluk, Lynnette
• 通讯作者: Michaluk, Lynnette

Mediating role of personality in the relation of gender to self-efficacy in physics and mathematics

物理和数学中人格在性别与自我效能关系中的中介作用

• DOI: 10.1103/physrevphyseducres.18.010143
• 发表时间: 2022
• 期刊: Physical Review Physics Education Research
• 影响因子: 3.1
• 作者: Henderson, Rachel;Hewagallage, Dona;Follmer, Jake;Michaluk, Lynnette;Deshler, Jessica;Fuller, Edgar;Stewart, John
• 通讯作者: Stewart, John

Using the Social Cognitive Theory Framework to Chart Gender Differences in the Developmental Trajectory of STEM Self-Efficacy in Science and Engineering Students

利用社会认知理论框架绘制理工科学生STEM自我效能感发展轨迹的性别差异

• DOI: 10.1007/s10956-020-09853-5
• 发表时间: 2020
• 期刊: Journal of Science Education and Technology
• 影响因子: 4.4
• 作者: Stewart, John;Henderson, Rachel;Michaluk, Lynnette;Deshler, Jessica;Fuller, Edgar;Rambo-Hernandez, Karen
• 通讯作者: Rambo-Hernandez, Karen

The Importance of Belonging and Self-Efficacy in Engineering Identity

归属感和自我效能在工程身份中的重要性

• 发表时间: 2018
• 期刊: AERA open
• 影响因子: 2.8
• 作者: Zabriskie, C.;Henderson, R.;Stewart, J.
• 通讯作者: Stewart, J.