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

该项目将有助于国家需要受过良好教育的科学家，数学家，工程师和技术人员，通过支持高成就，低收入学生的保留和毕业证明在阿肯色州的经济需要。在为期五年的时间里，该项目将为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;计算机科学和计算机工程;地质科学;工业工程;和数学。该项目采用了一种新颖的多学科方法，专注于在拥有不同STEM专业的学生群体中培养数据科学技能。学者将参加旨在发展数据科学技能的项目活动，并为他们在该领域的职业生涯做好准备，包括夏季数据科学靴子营和数据创新挑战。该项目旨在使低收入的STEM学生受益，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经济发展的新区域STEM技术中心的目标。该项目的总体目标是提高低收入，高成就的本科生与证明财政需要完成STEM学位。学者将有机会获得同行指导，每周一次的社交活动，以及双月演讲系列将提供网络，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靴子营，介绍数据科学的学术基础，并参加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，知识轮换使学者们能够跟踪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入学，保留，毕业时间和职业成果最相关的学术，个人和/或心理因素。此外，该项目将研究以下两个研究问题：（1）学生对他们的数学相关能力，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预测数据科学课程系列的入学，表现，保留和毕业？以及（2）参与项目活动是否能提高学生保留率、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，谁坚持完成，该项目将评估学者的兴趣，感知能力，实用价值和智力理论。在这个项目中获得的结果将被用来开发一个模型，可以解释低收入学者与其他低收入STEM学生的保留和毕业的变化。然后，这些模型可以应用于为低收入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的学院和大学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，工程和数学奖学金计划资助，该计划旨在增加低收入学术人才的数量，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STEM工作者的教育，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。

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.