By Audrey Malagon, Lead Editor of DUE Point
Data Science is taking the world by storm. Are we preparing our students for this rapidly growing industry? MAA’s DUE Point blog sat down with StatPREP principal investigator Doug Ensley to discuss this five-year project to help faculty incorporate data-centered components in introductory stats courses.
Q: What are the main goals of the StatPREP project?
First, we want to convince instructors to use modern software tools instead of a graphing calculator for data analysis in order to facilitate use of larger and more realistic datasets in assignments and on exams. Second, we want to help mathematics instructors without statistics training to understand the importance of teaching the introductory course using statistical habits of mind.
Our project creates local professional development programs for (especially community college) faculty at 10 different sites around the country, with the goal of creating regional hubs for innovative teaching of introductory statistics.
Q: Who is involved in the StatPREP project?
StatPREP partners the Mathematical Association of America with the American Statistical Association and the American Mathematical Association of Two-Year Colleges. Danny Kaplan (ASA) and Kathryn Kozak (AMATYC), along with Jenna Carpenter and Mike Brilleslyper from the past MAAPREP program lead the project.
Q: What is your hope for the impact of this project at the end of the five years?
The recent flood of jobs in data science creates a demand for mathematics programs to modernize statistics instruction. Five years from now data science programs will be common, and the first statistics course will need to be aligned with such programs. Intro stats is already a large part of faculty load at four-year and two-year colleges, especially among part-time instructors. We hope professional development in data science methods will be commonplace and StatPREP can provide an evidence-based model for these programs.
Q: What unanticipated challenges have arisen since you wrote the proposal?
The greatest surprise has been the demand for this sort of training. The first workshops had barely finished when people starting inquiring about how the resources can be accessed online. Our proposal focused almost exclusively on the dynamics of regional community hubs, so we will have to think about how to meet the demand beyond the planned sites. It is hard to complain when the success of the initial idea creates the biggest challenge!
Q: NSF grants seem very competitive, so what do you think it was about your project that was unique or innovative enough to get funding?
I believe there were several key reasons. First, the project has formal endorsement from AMATYC and ASA, and it is run by the MAA. These endorsements give reviewers confidence that the project teams will have the resources (like access to top presenters and a robust network for recruiting participants) to execute the program. In this case, we also had the benefit that the MAA ran the NSF-funded PREP programs for ten years, so there is a long history of executing the logistics of distributed professional development workshops. Finally, and most importantly, the project addresses a clear need for math curriculum to adapt to the growing field of data science.
Editor’s note: Q&A responses have been edited for length and clarity.
Learn more about NSF DUE 1626337
Full Project Name: Professional Development Emphasizing Data-Centered Resources and Pedagogies for Instructors of Undergraduate Introductory Statistics (StatPREP)
Project Website: http://www.statprep.org
Project Contact: Jenna Carpenter, firstname.lastname@example.org
For more information on any of these programs, follow the links, and follow these blog posts! This blog is a project of the Mathematical Association of America, produced with financial support of NSF DUE Grant #1626337.
Audrey Malagon is lead editor of DUE Point and a Batten Associate Professor of Mathematics at Virginia Wesleyan University with research interests in inquiry based and active learning, election security, and Lie algebras. Find her on Twitter @malagonmath.