Computers in Science and Math Undergraduate Education

计算机在科学和数学本科教育中的应用

• 批准号: 1834159
• 负责人: Joyce Stamm
• 金额: $ 99.99万
• 依托单位: University of Evansville
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1834159&HistoricalAwards=false
• 关键词: Computers; Science; Math; Undergraduate; Education

The ability to extract knowledge and insights from large datasets is increasingly important in science and the workforce. This project intends to address this need by educating scientists and mathematicians with computational skills needed to use large datasets to solve complex problems. Over five years, this project will provide four-year scholarships to fifteen low-income, high-achieving STEM undergraduate students with demonstrated financial need. The selected Scholars will be majors in biochemistry, biology, chemistry, environmental science, mathematics, physics, or statistics and data science. The project will develop four new courses to help students develop strong computational thinking and data visualization skills. Three new courses will introduce students early in their college career to interesting scientific problems that require analysis of complex data. A fourth new course will pair local businesses or non-profit clients with teams of third-year students and their faculty mentors. The teams will work to solve a client problem that requires computational approaches. Although only some students will receive scholarships, all math and science majors will have access to the new courses and support activities, including a career colloquium, undergraduate research, and a new Math and Science Problem Solving Lab. This access broadens the potential impact of this work to all STEM undergraduates at the University of Evansville. The project's research goal is to assess the impact of an interdisciplinary, computation-focused learning community on low-income students. Data will be collected from formative and summative assessments of Scholars' academic performance, retention in STEM, and progression to STEM careers. This assessment will use an embedded, mixed-methods study design to understand participant views throughout their college years. The summative data from Scholars will be compared to that of non-participating STEM majors. This analysis will contribute to understanding how participation in a combination of activities including career events, undergraduate research, and computational problem-solving affect students' career goals and self-efficacy. 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 high-achieving students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future scientists, engineers, and technicians, 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.

从大型数据集中提取知识和见解的能力在科学和劳动力中变得越来越重要。 该项目旨在通过教育科学家和数学家掌握使用大型数据集解决复杂问题所需的计算技能来满足这一需求。 在五年内，该项目将为 15 名表现出经济需求的低收入、成绩优异的 STEM 本科生提供四年期奖学金。 入选的学者将主修生物化学、生物学、化学、环境科学、数学、物理或统计学和数据科学。 该项目将开发四门新课程，帮助学生培养强大的计算思维和数据可视化技能。三门新课程将向学生在大学生涯的早期介绍需要分析复杂数据的有趣科学问题。 第四个新课程将把当地企业或非营利客户与三年级学生及其导师团队配对。 这些团队将致力于解决需要计算方法的客户问题。尽管只有部分学生将获得奖学金，但所有数学和科学专业的学生都将有机会参加新课程和支持活动，包括职业研讨会、本科生研究以及新的数学和科学问题解决实验室。 这种访问扩大了这项工作对埃文斯维尔大学所有 STEM 本科生的潜在影响。该项目的研究目标是评估跨学科、以计算为中心的学习社区对低收入学生的影响。 数据将从对学者学术表现、STEM 保留率以及 STEM 职业发展的形成性和总结性评估中收集。 该评估将使用嵌入式混合方法研究设计来了解参与者在整个大学期间的观点。 学者们的总结数据将与未参与的 STEM 专业的总结数据进行比较。 该分析将有助于理解参与职业活动、本科生研究和计算问题解决等活动组合如何影响学生的职业目标和自我效能。 该项目由 NSF 的科学、技术、工程和数学奖学金项目资助，该项目旨在增加获得 STEM 领域学位的低收入、学业成绩优异、有经济需要的学生数量。它还旨在改善未来科学家、工程师和技术人员的教育，并产生有关低收入学生的学业成功、保留、转学、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价值和更广泛的影响审查标准进行评估，被认为值得支持。