Computers in Science and Math Undergraduate Education

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越来越重要。 该项目旨在通过教育科学家和数学家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名低收入，高成就的STEM本科生提供四年奖学金，并证明他们有经济需要。 入选的学者将主修生物化学，生物学，化学，环境科学，数学，物理学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。三门新课程将在大学生涯的早期向学生介绍需要分析复杂数据的有趣科学问题。 第四个新课程将使当地企业或非营利客户与三年级学生及其教师导师团队配对。 这些团队将致力于解决需要计算方法的客户端问题。虽然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的课程和支持活动，包括职业座谈会，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一个跨学科的、以计算为重点的学习社区对低收入家庭学生的影响。 数据将从学者的学业成绩，保留在干，并进展到干职业的形成性和总结性评估收集。 该评估将使用嵌入式混合方法研究设计，以了解参与者在大学期间的观点。 学者的总结性数据将与未参与STEM专业的数据进行比较。 这项分析将有助于了解如何参与活动的组合，包括职业活动，本科生的研究和计算问题解决影响学生的职业目标和自我效能。 该项目由NSF的科学，技术，工程和数学奖学金计划资助，该计划旨在增加低收入，学术成就高的学生的数量，这些学生表现出经济需求，并获得STEM领域的学位。它还旨在改善未来科学家、工程师和技术人员的教育，并产生关于低收入学生的学术成功、保留、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价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.