Texas A&M/San Antonio College Success Profile Comparison Study

每年有数千名代表不足的少数族裔(URM)学生注册参加科学、技术、工程和数学(STEM)课程，其中很大一部分人未能获得STEM学位。为了提高获得STEM学位的比例，更完整地了解那些获得学士学位成功的学生的特征以及他们成功参加的项目(S)的特点将是有帮助的。通过德克萨斯州农工系统路易斯·斯托克斯少数族裔参与联盟(TX LSAMP)和基金会联盟，调查人员多年来一直直接参与教育战略的制定、管理、研究和评估，以提高STEM专业URM学生的参与度、学习体验和毕业率。通过这些努力积累的关于学生特征和项目属性的广泛数据库是一个丰富的来源，可以挖掘这些来源，以更好地了解项目属性与获得STEM学位的学生的特征之间的关系。为了开始从大量积累的数据中提取知识，德克萨斯农工大学(TAMU)和圣安东尼奥学院(SAC)的教育工作者和教育研究和评估专业人员组成的合作伙伴正在进行TAMU/SAC成功概况比较研究(TAMU/SAC SPC)。项目团队为这项研究带来了与德克萨斯州LSAMP积极的社区学院和大学合作伙伴关系相关的纵向数据、机构洞察力和系统观点，SAC和TAMU都属于该伙伴关系。整个研究项目分两个阶段进行。初步研究的重点是从德克萨斯州LSAMP合作伙伴的五所大学或九所社区学院之一开始的学生。结果变量有七个可能的值：(I)获得理工学院硕士学位并继续攻读研究生院，(Ii)获得理工科理工学院学位，(Iii)仍就读于理工学院理工学院学士学位课程，(Iv)获得非理工科理工学院学士学位并继续就读研究生院，(V)获得非理工科理工学院学士学位，(Vi)仍就读于非理工学院理工学院学士学位课程，以及(Vii)离开德克萨斯州的公立高等教育。独立的输入变量包括(1)学生人口统计，(2)招生行为，(3)启动院校的一般特征，以及(4)学生毕业或目前入学的学士学位院校的类型(即卡内基分类)。由于不可能从所有这些机构获得关于学生的深入、详细的数据，以及关于项目、教学战略和细粒度的机构特征的数据，第二阶段的研究，即校长研究，只专注于TAMU和SAC。校长研究丰富和提炼了上述自变量类别，并将(5)学生大学前准备和(6)所经历的教学策略，以及(7)大学早期的学习成绩添加到所研究的自变量集合中。这两项研究都使用了广义线性模型(例如，二元Logistic回归、多分类和有序Logistic回归)、多变量分析以及探索性和描述性技术。这两项研究都为每个文凭结果(因变量)类别定义的每个亚群以及通过分组变量定义的其他亚群开发了概况模型(PM)。

Each year thousands of under-represented minority (URM) students enroll in science, technology, engineering, and mathematics (STEM) programs and to a large percentage fail to graduate with a STEM degree. In order to increase the percentage that earn a STEM degree, it would be helpful to have more complete information about the characteristics of those students who have achieved baccalaureate success, and the attributes of the program(s) in which they succeeded. Through the Texas A&M system Louis Stokes Alliance for Minority Participation (TX LSAMP) and the Foundation Coalition, investigators have been directly involved, for several years, in the development, management, study and evaluation of educational strategies to increase participation, learning experiences, and graduation rates of URM students in STEM majors. Extensive databases on student characteristics and program attributes, accumulated through these efforts, represent a rich source, which can be mined for a greater understanding of relationships between program attributes and the characteristics of students who earned STEM degrees. To begin extracting knowledge from the extensive accumulated data, a partnership of educators and educational research and evaluation professionals at Texas A&M University (TAMU) and San Antonio College (SAC) are undertaking the TAMU/SAC Success Profile Comparison Study (TAMU/SAC SPCS). The project team brings to this study longitudinal data, institutional insights, and systemic perspectives acquired in connection with the active community college and university partnerships of the TX LSAMP, to which both SAC and TAMU belong. The entire research project is being conducted in two phases. A Preliminary Study focuses on students that started in one of the five universities or nine community colleges that were partners in TX LSAMP. The outcome variables have seven possible values: (i) earned BS STEM degree and continued onto graduate school, (ii) earned BS STEM degree, (iii) still enrolled in BS STEM baccalaureate degree program, (iv) earned non-STEM baccalaureate degree and continued on to graduate school, (v) earned non-STEM baccalaureate degree, (vi) still enrolled in non-STEM baccalaureate degree program, and (vii) left public higher education in the state of Texas. Independent, input variables include (1) student demographics, (2) enrollment behaviors, (3) general characteristics of the starting institutions, and (4) the types (i.e. Carnegie classification) of baccalaureate institution from which students graduated or are currently enrolled. Because it would not be possible to obtain, from all those institutions, the deep, detailed data about students and about programs, pedagogical strategies and fine-grained institutional characteristics, the second phase research, the Principal Study, focuses only on TAMU and SAC. The Principal Study enriches and refines the aforementioned independent variable categories, and also adds (5) student pre-college preparation and (6) pedagogical strategies experienced, as well as (7) early collegiate academic performance to the sets of independent variables studied. Both studies utilize Generalized Linear Models (e.g., binary logistic regression, polychotomous and ordinal logistic regression), and Multivariate Analyses along with exploratory and descriptive techniques. Both studies develop Profile Models (PMs) for each sub-population defined by each baccalaureate outcome (dependent variable) category, as well as for other sub-populations defined by grouping variables.