Increasing Degree-completion for Engineering and Computer Science Scholars

该项目将通过支持贝勒大学有经济需求的高成就、低收入学生的留校和毕业，来促进国家对受过良好教育的科学家、数学家、工程师和技术人员的需求。在为期五年的时间里，该项目将为22名攻读工程、电气和计算机工程、机械工程和计算机科学学士学位的学生提供四年奖学金。为工程和计算机科学(ECS)学者设计的活动包括迎新、每月系列研讨会和必要的教师指导。学者还将参与现有的支持服务和活动，包括同行指导、留学机会、校友指导、本科生研究支持和培训、专业发展研讨会，以及ECS学习资源中心的辅导支持。该项目的一个显著特点是使用了EAB的导航，这是一个基于网络的软件平台，用于跟踪学生进度、协调学生照顾和使用预测性分析。该项目产生的知识预计将确定使用预测性分析的最佳做法，以提供及时的干预措施，并提高STEM学生的保留率和毕业率。该项目的总体目标是增加低收入、高成就、有经济需求的本科生的STEM学位毕业率。该项目将使用导航预测分析平台来跟踪和记录ECS学者在留住、毕业、实习、本科生研究经验和就业安置方面的进展。预测分析的使用具有提高学生成功的巨大潜力，然而，使用基于计算机的预测和工具来支持学生的最佳做法并没有得到很好的发展。该项目将调查学生和教职员工/导师对影响学生成功的因素的看法是否与预测分析仪表板确定的因素一致。该项目还将调查人们对预测性分析在多大程度上有助于识别抑制和促进学生成功的因素的看法。该项目将每年对学生和教师/导师重点小组进行访谈。讨论将被转录和编码，以供定性研究软件分析。由此产生的纵向研究将为使用预测性分析来改善学生成绩提供见解。该项目的成果将在区域会议、国家会议以及导航平台的其他用户中传播。该项目由NSF的科学、技术、工程和数学奖学金项目资助，该项目旨在增加在STEM领域获得学位的低收入学术天才学生的数量。它还旨在改善未来STEM工作者的教育，并产生关于低收入学生的学业成功、留住、转移、毕业和学术/职业道路的知识。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at Baylor University. Over its five-year duration, this project will fund four-year scholarships to 22 students who are pursuing Bachelor of Science degrees in Engineering, Electrical and Computer Engineering, Mechanical Engineering and Computer Science. Activities designed for Engineering and Computer Science (ECS) Scholars include an orientation, a monthly seminar series, and required faculty mentoring. Scholars will also participate in existing support services and activities including peer mentoring, study abroad opportunities, alumni mentoring, support and training for undergraduate research, professional development workshops, and tutoring support from the ECS Learning Resource Center. A distinguishing feature of the project is the use of EAB's Navigate, a web-based software platform for tracking student progress, coordinating student care, and employing predictive analytics. The knowledge generated by this project is expected to identify best practices for using predictive analytics to provide timely interventions and improve retention and graduation rates for STEM students. The overall goal of this project is to increase STEM degree completion of low-income, high-achieving undergraduates with demonstrated financial need. The project will use the Navigate predictive analytics platform to track and document ECS Scholar progress in the areas of retention, graduation, internships, undergraduate research experiences, and job placement. The use of predictive analytics has significant potential to improve student success, however best practices for using computer-based predictions and tools to support students are not well developed. This project will investigate whether student and faculty/mentor perceptions of the factors that influence student success align with those identified by the predictive analytics dashboard. The project will also investigate perceptions about the extent to which predictive analytics are helpful for identifying factors that inhibit and promote student success. The project will conduct yearly interviews with students and faculty/mentor focus groups. The discussions will be transcribed and encoded for analysis by qualitative research software. The resulting longitudinal study will generate insights for using predictive analytics to improve student outcomes. Results of the project will be disseminated at regional conferences, national conferences, and among other users of the Navigate platform. This project is funded by NSF's Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of low-income academically talented students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students.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.