A Mathematics Pipeline to Student Success in Data Analytics through Course-Based Undergraduate Research

IF 0.3 Q4 MATHEMATICS Mathematics Enthusiast Pub Date : 2022-01-01 DOI:10.54870/1551-3440.1573
Kristin P. Bennett, John S. Erickson, Amy Svirsky, Josephine C. Seddon
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Abstract

This paper reports on Data Analytics Research (DAR), a course-based undergraduate research experience (CURE) in which undergraduate students conduct data analysis research on open real-world problems for industry, university, and community clients. We describe how DAR, offered by the Mathematical Sciences Department at Rensselaer Polytechnic Institute (RPI), is an essential part of an early low-barrier pipeline into data analytics studies and careers for diverse students. Students first take a foundational course, typically Introduction to Data Mathematics, that teaches linear algebra, data analytics, and R programming simultaneously using a project-based learning (PBL) approach. Then in DAR, students work in teams on open applied data analytics research problems provided by the clients. We describe the DAR organization which is inspired in part by agile software development practices. Students meet for coaching sessions with instructors multiple times a week and present to clients frequently. In a fully remote format during the pandemic, the students continued to be highly successful and engaged in COVID-19 research producing significant results as indicated by deployed online applications, refereed papers, and conference presentations. Formal evaluation shows that the pipeline of the single on-ramp course followed by DAR addressing real-world problems with societal benefits is highly effective at developing students' data analytics skills, advancing creative problem solvers who can work both independently and in teams, and attracting students to further studies and careers in data science.
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通过基于课程的本科生研究,帮助学生在数据分析方面取得成功的数学管道
本文报告了数据分析研究(DAR),这是一种基于课程的本科生研究体验(CURE),在这种体验中,本科生为行业、大学和社区客户对开放的现实世界问题进行数据分析研究。我们描述了由伦斯勒理工学院(RPI)数学科学系提供的DAR如何成为早期低门槛管道进入数据分析研究和职业生涯的重要组成部分。学生首先要学习基础课程,典型的是数据数学导论,该课程使用基于项目的学习(PBL)方法同时教授线性代数、数据分析和R编程。然后在DAR中,学生们以小组的形式对客户提供的开放式应用数据分析研究问题进行研究。我们描述了部分受到敏捷软件开发实践启发的DAR组织。学生们每周与导师进行多次培训,并经常向客户介绍。在大流行期间,学生们以完全远程的形式继续取得巨大成功,并参与了COVID-19研究,从部署的在线应用程序、评审论文和会议报告中可以看出,这些研究取得了重大成果。正式评估表明,通过DAR解决具有社会效益的现实问题的单一入门课程的管道在培养学生的数据分析技能,提高能够独立和团队合作的创造性问题解决者方面非常有效,并吸引学生进一步学习和从事数据科学方面的职业。
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来源期刊
Mathematics Enthusiast
Mathematics Enthusiast MATHEMATICS-
CiteScore
1.40
自引率
0.00%
发文量
43
期刊介绍: The Mathematics Enthusiast (TME) is an eclectic internationally circulated peer reviewed journal which focuses on mathematics content, mathematics education research, innovation, interdisciplinary issues and pedagogy. The journal exists as an independent entity. The electronic version is hosted by the Department of Mathematical Sciences- University of Montana. The journal is NOT affiliated to nor subsidized by any professional organizations but supports PMENA [Psychology of Mathematics Education- North America] through special issues on various research topics. TME strives to promote equity internationally by adopting an open access policy, as well as allowing authors to retain full copyright of their scholarship contingent on the journals’ publication ethics guidelines. Authors do not need to be affiliated with the University of Montana in order to publish in this journal. Journal articles cover a wide spectrum of topics such as mathematics content (including advanced mathematics), educational studies related to mathematics, and reports of innovative pedagogical practices with the hope of stimulating dialogue between pre-service and practicing teachers, university educators and mathematicians. The journal is interested in research based articles as well as historical, philosophical, political, cross-cultural and systems perspectives on mathematics content, its teaching and learning. The journal also includes a monograph series on special topics of interest to the community of readers.
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