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

每年都有数千名代表性不足的少数民族（URM）学生参加科学，技术，工程和数学（STEM）课程，并且很大一部分学生未能获得STEM学位。 为了提高获得STEM学位的比例，有关于那些取得学士学位成功的学生的特征以及他们成功的课程属性的更完整的信息将是有帮助的。 通过得克萨斯州A M系统路易斯·斯托克斯少数民族参与联盟（TX LSAMP）和基金会联盟，调查人员直接参与了几年来教育战略的开发，管理，研究和评估，以增加参与，学习经验和URM学生在STEM专业毕业率。 通过这些努力积累的关于学生特征和课程属性的广泛数据库代表了丰富的资源，可以挖掘这些资源，以便更好地了解课程属性与获得STEM学位的学生特征之间的关系。 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、教育研究和评估专业人士合作正在进行TAMU/SAC成功概况比较研究（TAMU/SAC SPCS）。 该项目团队带来了这项研究的纵向数据，机构的见解，以及系统的观点，获得了与TX LSAMP的积极社区学院和大学的合作伙伴关系，其中SAC和TAMU都属于。 整个研究项目分两个阶段进行。 一项初步研究的重点是学生开始在五所大学或九所社区学院，在得克萨斯州LSAMP的合作伙伴之一。 结果变量有七个可能的值：（i）获得BS STEM学位并继续进入研究生院，（ii）获得BS STEM学位，（iii）仍然参加BS STEM学士学位课程，（iv）获得非STEM学士学位并继续进入研究生院，（v）获得非STEM学士学位，（vi）仍然参加非STEM学士学位课程，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）入学行为，（3）起始机构的一般特征，以及（4）学生毕业或目前就读的学士学位机构的类型（即卡内基分类）。由于不可能从所有这些机构获得关于学生和课程、教学策略和精细机构特征的深入、详细的数据，第二阶段的研究，即主要研究，只关注TAMU和SAC。 主要研究丰富和完善了上述自变量类别，并增加了（5）学生大学前的准备和（6）教学策略的经验，以及（7）早期大学的学习成绩的自变量研究。这两项研究都使用了广义线性模型 (e.g.,二元逻辑回归、多元逻辑回归和有序逻辑回归），以及多变量分析（沿着探索性和描述性技术）。这两项研究都为由每个学士学位结果（因变量）类别定义的每个亚群以及由分组变量定义的其他亚群开发了轮廓模型（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.