Implementation Project: Transforming Computational STEM Education at Claflin University

历史黑人学院和大学-本科课程中的实施项目为设计、实施、研究和评估全面的机构努力提供支持，以通过加强科学、技术、工程和数学(STEM)教育和研究来增加学生数量和提高他们的准备质量。克拉夫林大学的这一实施项目侧重于生物、化学、计算机科学和数学等学科，以提供一系列计算技术，以支持和教育能够在多学科环境中工作的下一代数据科学家。克拉夫林大学在STEM领域有相当数量的非裔美国学生毕业的可靠记录，通过这一实施项目，这一数字预计将增加。该项目由持续的评估指导和提供信息。拟议项目的目标是提高学生在STEM劳动力中的毕业率和就业率；在以STEM为基础的计算课程中注入研究和教育；促进计算科学研究以提高体验质量并为STEM学生在全球社会中的竞争做好准备；以及利用核心合作伙伴的优势和资产推动学生进入STEM职业生涯。该项目将与联邦和国家实验室、工业和其他大学建立关系。该项目涉及教师和本科生，使用分析仪器和方法在代谢物和生物分子领域进行研究；在检索和分析生物数据的方法方面；以及在优化、计算复杂性、统计建模和机器学习方面进行研究。

Implementation Projects in the Historically Black Colleges and Universities - Undergraduate program provide support to design, implement, study and assess comprehensive institutional efforts to increase the numbers of students and the quality of their preparation by strengthening science, technology, engineering and mathematics (STEM) education and research. This implementation project at Claflin University focuses on the disciplines of biology, chemistry, computer science and mathematics to provide an array of computational technologies in order to support and educate the next-generation of data scientists who can work in a multi-disciplinary environment. Claflin University has a proven record of graduating a significant number of African-American students in STEM fields and through this implementation project that number is projected to increase. The project is guided and informed by an on-going evaluation.The goals of the proposed project are to increase graduation and placement rates of students in the STEM workforce; to infuse research and education in a STEM-based computational curriculum; to promote computational science research to improve the quality of experiences and prepare STEM students to compete in a global society; and to use the strengths and assets of core partners to propel students towards a STEM career. The project will create relationships with federal and national laboratories, industry and other universities. The project involves faculty and undergraduate students to conduct research in the areas of metabolites and biomolecules using analytical instrumentation and methodology; in methods for retrieving and analyzing biological data; as well as in optimization, computational complexity, statistical modeling and machine learning.