Engineering/NSM Student Success Program Serving Low-Income Academically Talented Students

在NSF科学，技术，工程和数学奖学金（S-STEM）计划的资助下，该项目将支持休斯顿大学表现出经济需求的高成就，低收入STEM学生的成功。在该奖项的五年中，该项目将为攻读STEM学士学位的学生提供80个奖学金。20名一年级和二年级的学生将参加一系列全面的活动，包括学术工作，补充教学和指导研究。 这些支持旨在支持及时取得毕业进展。通过一种全面的方法，该项目旨在提高低收入，高成就学生的成功，特别是那些来自历史上在STEM中代表性不足的群体的学生。该项目的研究部分旨在确定哪些项目活动对学生的成绩有影响，这些活动的最佳频率和强度，以及哪些参与对提高大学的持久性和提高毕业率最有效。为了估计计划的影响，评估将使用倾向评分技术，以匹配学生学者与对照组的学生谁没有收到奖学金，但谁是类似的奖学金获得者。描述性分析将比较工程学院和自然科学与数学学院各组之间参与率的平均值和分布。还将计算卡方统计数据，以确定不同计划类别的参与情况是否不同。在数据预处理之后，将使用普通最小二乘（OLS）回归来回答与感兴趣的结果相关的研究问题。 参与率的参与度问题将使用重复测量方差分析（ANOVA），其中因变量包括从学生调查中获得的参与度测量。参与度（即无、低、高）将是重复的衡量标准（组内因素），项目活动类别（即学术、行为、社会、情感）将是组间因素。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

With funding from the NSF Scholarships in Science, Technology, Engineering, and Mathematics (S-STEM) program, this project will support the success of high-achieving, low-income STEM students with demonstrated financial need at the University of Houston. Over the five years of the award, the project will fund 80 scholarships for students who are pursuing bachelor's degrees in STEM. Four cohorts of 20 first- and second-year students will participate in a comprehensive set of activities that includes orientation to scholarly work, supplemental instruction, and mentored research. These supports are designed to support timely progress toward graduation. Through a comprehensive approach, the project aims to enhance success of low-income, high-achieving students, especially those from groups that have been historically underrepresented in STEM. The research component of the project aims to determine which program activities make a difference in student outcomes, the optimal frequency and intensity of such activities, and which kinds of engagement are most effective at increasing persistence in college and raising graduation rates. To estimate program impacts, the evaluation will use propensity score techniques to match student scholars with a control group of students who did not receive scholarships but who are similar to the scholarship recipients. Descriptive analyses will compare the means and distributions of participation rates between groups, across the College of Engineering and the College of Natural Sciences and Mathematics. Chi-square statistics will also be calculated to determine whether participation differs across program categories. Following data preprocessing, ordinary least squares (OLS) regression will be used to answer research questions related to the outcomes of interest. The question of engagement by participation rate will use repeated measures analysis of variance (ANOVA), where the dependent variables include measures of engagement obtained from student surveys. Participation (i.e. no-, low-, and high-) will be the repeated measure (within-groups factor) and program activity category (i.e. academic, behavioral, social, affective) will be the between-groups factor.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.