Rebecca Nugent
Teaching Professor and Department Head | Carnegie Mellon University
Pittsburgh, PA, UNITED STATES
Rebecca Nugent's research interests lie in clustering, record linkage, educational data mining/psychometrics and public health.
Biography
Rebecca Nugent's research interests lie in clustering, record linkage, educational data mining/psychometrics, public health, tech/innovation/entrepreneurship and semantic organization. She is a national leader in data science education, sports analytics and promoting opportunities for women in data science and technical fields more generally. Nugent is the founding director of the department's Corporate Capstone Program, an experiential learning initiative that matches groups of faculty and students with data science research problems in corporate, nonprofit and government organizations. She has provided leadership for a number of other initiatives, including the Carnegie Mellon Sports Analytics Center and Women in Data Science Pittsburgh.
Areas of Expertise (9)
Future of Education
Public Health
Clustering
Semantic Organization
Educational Data Mining/Psychometric
Record Linkage
Social Sciences
Tech Innovation
Enterpreneurship
Media Appearances (3)
As omicron spreads, public health advocates urge states to reinstate mask mandates
NPR online
2021-12-15
So Rebecca Nugent, a data scientist at Carnegie Mellon who's been tracking the impact of COVID-19 mitigation policies, recently looked at how delta spread through the summer and fall. And she says among states with similar vaccination rates, those with statewide mask mandates did better.
Mona Chalabi discusses illustrations that allow people to both connect and engage with data
The Tartan online
2017-11-19
The Department of Statistics and Data Science at the Dietrich College of Humanities and Social Sciences is one of the fastest growing departments at Carnegie Mellon University, with the number of undergraduate statistics majors being more than 20 times what it was in 2003. This uptick was attributed to the fact that the department focuses on “problems that are real,” as stated by Professor Rebecca Nugent, director of undergraduate studies.
TEDxCMU showcases talent of Carnegie Mellon professors and performers
The Tartan online
2017-04-02
Rebecca Nugent, a teaching professor at Carnegie Mellon’s department of statistics, raised the underestimated issue of being “innumerate” and discussed how the concept is perceived in western culture. She noted that, while illiteracy is often regarded as the more obvious problem to solve, people’s attitude towards the subject of mathematics, statistics, and data science are often steered by the Western culture that labels mathematics as a “cold, hard, masculine,” and intimidating subjects.
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Industry Expertise (2)
Education/Learning
Public Policy
Education (3)
Rice University: B.A., Mathematics, Statistics, and Spanish
University of Washington:: Ph.D., Statistics
Stanford University: M.S., Statistics
Links (3)
Event Appearances (5)
Panelist on Institutional Transformations
(2020) NSF/Berkeley National Workshop on Data Science Education
Keynote
(2020) The Fort AI and Data Summit
Invited Panel, Invited Poster
2020 Joint Statistical Meetings (JSM2020)
Breakout Session on ISLE, May 2020
(2020) electronic Conference on Teaching Statistics (eCOTS 2020),
Keynote
(2020) GOTO Chicago
Articles (2)
Think-Aloud Interviews: A Tool for Exploring Student Statistical Reasoning
Journal of Statistics and Data Science Education2022 Think-aloud interviews have been a valuable but underused tool in statistics education research. Think-alouds, in which students narrate their reasoning in real time while solving problems, differ in important ways from other types of cognitive interviews and related education research methods. Beyond the uses already found in the statistics literature—mostly validating the wording of statistical concept inventory questions and studying student misconceptions—we suggest other possible use cases for think-alouds and summarize best-practice guidelines for designing think-aloud interview studies. Using examples from our own experiences studying the local student body for our introductory statistics courses, we illustrate how research goals should inform study-design decisions and what kinds of insights think-alouds can provide. We hope that our overview of think-alouds encourages more statistics educators and researchers to begin using this method. Supplementary materials for this article are available online.
Teaching Statistical Concepts and Modern Data Analysis With a Computing-Integrated Learning Environment
Journal of Statistics Education2020 Revisiting the seminal 2010 Nolan and Temple Lang paper on the role of computing in the statistics curricula, we discuss several trends that have emerged over the last ten years. The rise of data science has coincided with a broadening audience for learning statistics and using computational packages and tools. It has also increased the need for communication skills. We argue that, for most of this audience, instruction should focus on foundational concepts and the early introduction of different types of data and modern methods through the use of interactive learning environments without programming prerequisites. We then describe ISLE, a web-based e-learning platform and lesson authoring framework for teaching statistics. Built on top of computing and peer-to-peer technology, the platform allows collaborative data analysis and real-time interactions in the classroom. We describe how ISLE contributes to the three key Nolan and Temple Lang components: broadening the statistical computing curriculum, deepening computational reasoning and literacy, and expanding computing with data in the practice of statistics. We then present several advantages of using a computing-integrated environment such as ISLE, including promoting (cross-disciplinary) communication, supporting classroom-focused integrated development environments, and advancing the science of data science.
