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Multidisciplinary Data Science Education to Prepare STEM Students for Data Science Careers

多学科数据科学教育帮助 STEM 学生为数据科学职业做好准备

基本信息

批准号：
1930532
负责人：
Manuel Rossetti
金额：
$ 100万
依托单位：
University of Arkansas
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：
2019-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1930532&HistoricalAwards=false
关键词：
Multidisciplinary Data Science Education Prepare

项目摘要

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 the University of Arkansas. Over its five-year duration, this project will fund three-year scholarships to 30 students who have an interest in data science and are pursuing Bachelor of Science degrees in the following STEM disciplines: Data Science; Biological Sciences; Biomedical Engineering; Computer Science and Computer Engineering; Geological Science; Industrial Engineering; and Mathematics. The project takes a novel, multidisciplinary approach that focuses on building data science skills among cohorts of students who have different STEM majors. Scholars will participate in project activities designed to develop data science skills and prepare them for careers in the field, including a summer data science boot camp and a data innovation challenge. The project aims to benefit low-income STEM students and contribute to the data science workforce in Northwest Arkansas. Arkansas is currently ranked 45th per capita in Bachelor of Science degrees in scientific and engineering fields and is unable to meet the State's STEM labor needs. This project aims to help fill workforce gaps, as well as to support Arkansas' goal of becoming a new regional STEM technology center for economic development. The overall goal of this project is to increase STEM degree completion of low-income, high-achieving undergraduates with demonstrated financial need. Scholars will have access to peer mentoring, a weekly social event, and a bi-monthly speaker series will provide opportunities for networking, support, and professional development. In addition, they will participate in a two-week summer boot camp to introduce the academic foundations of data science and take a set of courses designed to develop professional data science skills. Scholars will further develop and apply their data science skills in a data innovation competition, in internships or summer research opportunities, and in a knowledge rotation that allows Scholars to shadow faculty who are using data science in their work. The project includes an educational research study that aims to determine the academic, personal, and/or psychological factors that are most related to program enrollment, retention, graduation time-to-completion, and career outcomes in the field of data science. In addition, the project will study the following two research questions: (1) Do students' perceptions of their math-related ability, data science utility value, interest in data science, and their theory of intelligence beliefs predict enrollment, performance, retention, and graduation in the data science coursework series? and (2) Does participation in project activities account for improved retention, course success, graduation rates, and career/advanced study outcomes? To better understand who enrolls in the program and who persists to completion, the project will assess Scholar interest, perceived ability, utility value, and theory of intelligence. The results obtained in this project will be used to develop a model that can account for variability in retention and graduation of low-income Scholars versus other low-income STEM students. These models can then be applied to develop additional retention programs for low-income STEM students. The knowledge generated by this project has the potential to inform the growing number of data science programs in colleges and universities across the nation and world. 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.

该项目将支持阿肯色大学有经济需求的高成就、低收入学生的保留和毕业，从而满足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。该项目为期五年，将为 30 名对数据科学感兴趣并正在攻读以下 STEM 学科理学学士学位的学生提供三年奖学金：数据科学；生物科学；生物医学工程；计算机科学与计算机工程；地质科学；工业工程；和数学。该项目采用新颖的多学科方法，重点培养不同 STEM 专业学生的数据科学技能。学者们将参加旨在培养数据科学技能并为他们在该领域的职业生涯做好准备的项目活动，包括夏季数据科学训练营和数据创新挑战。该项目旨在造福低收入 STEM 学生，并为阿肯色州西北部的数据科学劳动力做出贡献。阿肯色州目前在科学和工程领域的理学学士学位人均排名第 45 位，无法满足该州的 STEM 劳动力需求。该项目旨在帮助填补劳动力缺口，并支持阿肯色州成为新的区域 STEM 技术中心以促进经济发展的目标。该项目的总体目标是提高有经济需求的低收入、成绩优异的本科生完成 STEM 学位的机会。学者们将有机会获得同行指导、每周一次的社交活动和每两个月一次的演讲系列，这将提供交流、支持和专业发展的机会。此外，他们还将参加为期两周的夏季训练营，介绍数据科学的学术基础，并学习一系列旨在培养专业数据科学技能的课程。学者们将在数据创新竞赛、实习或暑期研究机会以及知识轮换中进一步发展和应用他们的数据科学技能，使学者们能够跟随在工作中使用数据科学的教师。该项目包括一项教育研究，旨在确定与数据科学领域的课程注册、保留、毕业完成时间和职业成果最相关的学术、个人和/或心理因素。此外，该项目还将研究以下两个研究问题：（1）学生对其数学相关能力、数据科学实用价值、对数据科学的兴趣以及他们的智力信念理论的看法是否可以预测数据科学系列课程的入学、表现、保留和毕业？ (2) 参与项目活动是否可以提高保留率、课程成功率、毕业率和职业/高级学习成果？为了更好地了解谁参加了该计划以及谁坚持完成了该计划，该项目将评估学者的兴趣、感知能力、效用价值和智力理论。该项目获得的结果将用于开发一个模型，该模型可以解释低收入学者与其他低收入 STEM 学生的保留和毕业差异。然后可以应用这些模型为低收入 STEM 学生制定额外的保留计划。该项目产生的知识有可能为全国和世界各地学院和大学中越来越多的数据科学项目提供信息。该项目由 NSF 的科学、技术、工程和数学奖学金项目资助，该项目旨在增加具有经济需求且获得 STEM 领域学位的低收入学术才华学生的数量。它还旨在改善未来 STEM 工作者的教育，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。