College of Education and Human Development

Department of Educational Psychology

Andrew Zieffler

  • Teaching professor

Andrew Zieffler

Areas of interest

  • Teaching and learning of statistics
  • Measurement and assessment of statistics education research
  • Statistical computing and integration of computing into statistics curriculum
  • Data science education

Dr. Zieffler is currently accepting MA students who want to concentrate thesis work in statistics or statistics education.


PhD, University of Minnesota


Educational Psychology Research Colloquium - Andrew Zieffler - February 17, 2022

My research interests are in the teaching and learning of statistics and data science. I am also interested in measurement and assessment as it relates to statistics education and data science research. A further interest if mine is in statistical computing and in thinking about different ways to integrate computing into the statistics curriculum. I am currently a co-PI on a National Science Foundation (NSF) funded project, DSC-WAV, which engages teams of data science students to assist community-based and non-profit organizations tackle their data-based problems. This collaboration helps the organizations and also offers the students valuable hands-on data science experience.

Courses I teach

  • EPSY 3264Basic and Applied Statistics
  • EPSY 8251—Methods in Data Analysis for Educational Research I
  • EPSY 8252—Methods in Data Analysis for Educational Research II
  • EPSY 8264—Advanced Multiple Regression

Awards & Recognition

  • 2022 CEHD Distinguished Teaching Award, College of Education + Human Development, University of Minnesota
  • 2019 MPA Award for Outstanding Graduate Faculty in Psychology, Minnesota Psychological Association
  • 2014 Waller Education Award, for outstanding contributions to and innovations in the teaching of elementary statistics, American Statistical Association, Section on Statistical Education
  • 2013 COGS Outstanding Faculty Award, University of Minnesota, Council of Graduate Students

Research Funding Grants


Collaborative Research: The Data Science WAV: Experiential Learning with Local Community Organiza- tions. National Science Foundation funded October 1, 2019–September 30, 2022, $69,985, Zieffler, A. (PI). HDR DSC-1923700.

  • Defining the quantitative and computational skills of incoming science students. A Science Education Program Award to Macalester College as a lead institution for a collaboration between Bryn Mawr, Oberlin, St. Olaf, Lewis and Clark, Harvey Mudd, Claremont-McKenna Colleges, and the University of Minnesota. Howard Hughes Medical Initiative funded 2013–2016, $250,000, Overvoorde, P. (PI). Grant #520076788.
  • Collaborative Research: Evaluation and assessment of teaching and learning about statistics (e-ATLAS).
  • National Science Foundation funded June 1, 2011–May 31, 2013, $91,970, Garfield, J., Pearl, D., delMas, R., & Zieffler, A. (PIs). DUE-1044812 & 1043141.
  • Collaborative research: The CATALST project, Change Agents for Teaching and Learning STatistics. Na- tional Science Foundation funded August 2008–July 2011, $299,974, Garfield, J., delMas, R., & Zieffler, A. (PIs). DUE-0814433.
  • National statistics teaching practice survey: Instrument development. National Science Foundation funded July 2008–June 2011, $71,887, Garfield, J., delMas, R., & Zieffler, A. (PIs). DUE-0808862.

Legacy, C.*, Zieffler, A., Fry, E., & Le, L. (2022). COMPUTES: An instrument to measure introductory statistics instructors’ emphasis on computational practices. Statistics Education Research Journal, 21(1). doi: 10.52041/serj.v21i1.63

Legacy, C.*, Zieffler, A., Baumer, B. S., Barr, V., & Horton, N. J. (2022). Facilitating team- based data science: Lessons learned from the DSC-WAV project. Foundations in Data Science (Online First). doi: 10.3934/fods.2022003

Horton, N. J., Baumer, Benjamin S., Zieffler, A., & Barr, V. (2021). The Data Science Corps Wrangle- Analyze-Visualize program: Building data acumen for undergraduate students. Harvard Data Science Review, 3(1). doi: 10.1162/99608f92.8233428d

Zieffler, A., Justice, N.*, delMas, R., & Huberty, M.* (2021). The use of algorithmic models to develop secondary teachers’ understanding of the statistical modeling process. Journal of Statistics and Data Science Education, 29(1), 131–147. doi: 10.1080/26939169.2021.1900759

Justice, N.*, Zieffler, A., Huberty, M.*, & delMas, R. (2018). Every rose has it’s thorn: Secondary teachers’ reasoning about statistical models. ZDM—The International Journal on Mathematics Education, 50(7), 1253–1265. doi: 10.1007/s11858-018-0953-1

National Academies of Sciences, Engineering, and Medicine. (2018). [Contributing Author]. Data science for undergraduates: Opportunities and options. Washington, DC: The National Academies Press. doi: 10.17226/25104

Sabbag, A. G.*, Garfield, J., & Zieffler, A. (2018). Assessing statistical literacy and statistical reason- ing: The REALI instrument. Statistics Education Research Journal, 17(2), 141–160. doi: 10.52041/serj.v17i2.163

Garfield, J., Zieffler, A., & Fry, E.* (2017). What is statistics education? In D. Ben-Zvi, K. Makar, & J. Garfield (Eds.), The international handbook of research in statistics education (pp. 37–70). Cham, Switzerland: Springer International Publishing. doi: 10.1007/978-3-319-66195-7_2

* = Current or former graduate student


Legacy, C.*, Rao, V. N. V.*, Zieffler, A., & delMas, R. C. (2022, January). Data to graphs and back: Secondary teachers' reasoning about the aesthetic mappings that link data and visualizations. Presentation at the 12th Statistical Reasoning, Thinking and Literacy (SRTL-12) Research Forum, online.

Baumer, B. S., Horton, N. J., Zieffler, A., & Legacy, C.* (2021, June). Facilitating team-based data science: Lessons learned from the DSC-WAV project. Presentation at the United States Conference on Teaching Statistics, Online.

Zieffler, A., Justice, N.*, delMas, R., & Huberty, M.* (2021, April). The use of algorithmic models to develop secondary teachers’ understanding of the statistical modeling process. Invited presentation for the CAUSE webinar series. Online.

Zieffler, A., Hofelich Mohr, A., Brown, E. C.*, & Bye, J. K. (2020, July). R @ 25 Lunch & Learn: Understanding the landscape of the popular free/open source statistics software. Invited panel presentation for the Research Methodology Consulting Center and Liberal Arts Technologies and Innovation Services, University of Minnesota, Minneapolis, MN.