• ANNA FERGUSSON University of Auckland
  • MAXINE PFANNKUCH University of Auckland



Statisitcs education research, Data science education, Predictive modeling, Integrating statistical and computational thinking, Task design, High school teachers, APIs


Tasks for teaching predictive modelling and APIs often require learners to use code-driven tools. Minimal research, however, exists about the design of tasks that support the introduction of high school students and teachers to these new statistical and computational methods. Using a design-based research approach, a web-based task was developed. The task was constructed using our design framework and implemented within a face-to-face professional development workshop involving six high school statistics teachers. The teachers were guided through the process of developing a prediction model using: an informal approach; visual prediction intervals; data about movie ratings from an API; and R code that ran in the browser. Our findings from this exploratory study indicate that the web-based task supported the development of new statistical and computational ideas related to predictive modelling and APIs.


Allaire, J., Xie, Y., McPherson, J., Luraschi, J., Ushey, K., Atkins, A., Wickham, H., Cheng, J., Chang, W., & Iannone, R. (2021). rmarkdown: Dynamic documents for R. RStudio.

Anderson, T., & Shattuck, J. (2012). Design-based research: A decade of progress in education research?. Educational Researcher, 41(1), 16–25.

Bakker, A. (2018). Design research in education: A practical guide for early career researchers. Routledge.

Bakker, A., & van Eerde, D. (2015). An introduction to design-based research with an example from statistics education. In A. Bikner-Ahsbahs, C. Knipping, & N. Presmeg (Eds.), Approaches to qualitative research in mathematics education (pp. 429–466). Springer.

Bargagliotti, A., Franklin, C., Arnold, P., Gould, R., Johnson, S., Perez, L., & Spangler, D. (2020). Pre-K–12 Guidelines for Assessment and Instruction in Statistics Education (GAISE) report II. American Statistical Association.

Ben-Zvi, D. (2000). Toward understanding the role of technological tools in statistical learning. Mathematical Thinking and Learning, 2(1-2), 127–155.

Biehler, R. (2018). Design principles, realizations and uses of software supporting the learning and the doing of statistics: A reflection on developments since the late 1990s. In M. A. Sorto, A. White, & L. Guyot (Eds.), Looking back, looking forward. Proceedings of the Tenth International Conference on Teaching Statistics (ICOTS10), Kyoto, Japan, July 8–13. International Statistical Institute.

Biehler, R., & Schulte, C. (2017). Perspectives for an interdisciplinary data science curriculum at German secondary schools. In R. Biehler, L. Budde, D. Frischemeier, B. Heinemann, S. Podworny, C. Schulte, & T. Wassong (Eds.), Paderborn Symposium on Data Science Education at School Level 2017: The Collected Extended Abstracts (pp. 2–14). Universitätsbibliothek Paderborn.

Burr, W., Chevalier, F., Collins, C., Gibbs, A. L., Ng, R., & Wild, C. J. (2021). Computational skills by stealth in introductory data science teaching. Teaching Statistics, 43, S34–S51.

Casey, S. A., & Wasserman, N. H. (2015). Teachers’ knowledge about informal line of best fit. Statistics Education Research Journal, 14(1), 8–35.

Cetinkaya-Rundel, M., & Rundel, C. (2018). Infrastructure and tools for teaching computing throughout the statistical curriculum. The American Statistician, 72(1), 58–65.

De Veaux, R. D., Agarwal, M., Averett, M., Baumer, B. S., Bray, A., Bressoud, T. C., Bryant, L., Cheng, L. Z., Francis, A., Gould, R., Kim, A. Y., Kretchmar, M., Lu, Q., Moskol, A., Nolan, D., Pelayo, R., Raleigh, S., Sethi, R. J., Sondjaja, M., … Ye, P. (2017). Curriculum guidelines for undergraduate programs in data science. Annual Review of Statistics and Its Application, 4, 15–30.

Edelson, D. C. (2002). Design research: What we learn when we engage in design. The Journal of the Learning sciences, 11(1), 105–121.

Engel, J. (2017). Statistical Literacy for Active Citizenship: A Call for Data Science Education. Statistics Education Research Journal, 16 (1), 44–49.

Erickson, T. (2020). The BART Data Portal. An Introduction to Data Science with CODAP.

Fergusson, A., & Pfannkuch, M. (2020). Development of an informal test for the fit of a probability distribution model for teaching. Journal of Statistics Education, 28(3), 344–357.

Fergusson, A., & Pfannkuch, M. (2021). Introducing teachers who use GUI-driven tools for the randomization test to code-driven tools. Mathematical Thinking and Learning.

Fergusson, A., & Wild, C. J. (2021). On traversing the data landscape: Introducing APIs to data-science students. Teaching Statistics, 43, S71–S83.

Finzer, W. (2013). The data science education dilemma. Technology Innovations in Statistics Education, 7(2).

Gould, R. (2010). Statistics and the modern student. International Statistical Review, 78(2), 297–315.

Gould, R. (2017). Data literacy is statistical literacy. Statistics Education Research Journal, 16(1), 22–25.

Gould, R. (2021). Toward data-scientific thinking. Teaching Statistics, 43, S11–S22.

Hardin, J. (2018). Dynamic data in the statistics classroom. Technology Innovations in Statistics Education, 11(1).

Kaplan, D. (2007). Computing and introductory statistics. Technology Innovations in Statistics Education, 1(1).

Konold, C., & Miller, C. (2015). TinkerPlots™ Version 2.3 [Computer Software]. Learn Troop.

Magana, A. J., Vasileska, D., & Ahmed, S. (2011). Work in progress—a transparency and scaffolding framework for computational simulation tools. 2011 Frontiers in Education Conference (FIE), (pp. S4G–1). IEEE.

Makar, K., & Rubin, A. (2018). Learning about statistical inference. In D. Ben-Zvi, K. Makar, & J. Garfield (Eds.), International handbook of research in statistics education (pp. 261–294). Springer.

McKenney, S., & Reeves, T. C. (2018). Conducting educational design research. Routledge.

National Academies of Sciences, Engineering, and Medicine. (2018). Data science for undergraduates: Opportunities and options. The National Academies of Sciences Engineering Medicine.

Nolan, D., & Temple Lang, D. (2010). Computing in the statistics curricula. The American Statistician, 64(2), 97–107.

New Zealand Qualifications Authority. (2019). Annotated exemplar Level 3 AS91581. Author.

Pfannkuch, M. (2011). The role of context in developing informal statistical inferential reasoning: A classroom study. Mathematical Thinking and Learning, 13(1–2), 27–46.

Pruim, R., Kaplan, D. T., & Horton, N. J. (2017). The mosaic package: Helping students to ‘think with data’ using R. The R Journal, 9(1), 77–102.

R Core Team. (2020). R: A language and environment for statistical computing.

Reeves, T. C. (2007). Design-based research from a technology perspective. In J. Van den Akker, K. Gravemeijer, S. McKenney & N. Nieveen (Eds.), Educational design research, (pp. 52–56). Routledge.

Ridgway, J. (2016). Implications of the data revolution for statistics education. International Statistical Review, 84(3), 528–549.

Sentance, S., Waite, J., & Kallia, M. (2019). Teaching computer programming with PRIMM: a sociocultural perspective. Computer Science Education, 29(2-3), 136–176.

Shaughnessy, J. M. (1997). Missed opportunities in research on the teaching and learning of data and chance. In F. Biddulph & K. Carr (Eds.), People in mathematics education. Proceedings of the Twentieth Annual Conference of the Mathematics Research Group of Australasia (MERGA-20, July, 1990), Rotorua, New Zealand (Vol. 1, pp. 6–22). MERGA.

Schloerke, B., Allaire, J., & Borges, B. (2018). Learnr: Interactive tutorials for R. CRAN.

Son, J. Y., Blake, A. B., Fries, L., & Stigler, J. W. (2021). Modeling first: Applying learning science to the teaching of introductory statistics. Journal of Statistics and Data Science Education, 29(1), 4–21.

Sweller, J., van Merriënboer, J. J. G., & Paas, F. G. W. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251–296.

Van den Akker, J. (1999). Principles and methods of development research. In J. Van den Akker, R. M. Branch, K. Gustafson, N. Nieveen & T. Plomp (Eds.), Design approaches and tools in education and training, (pp. 1–14). Springer.

Van Someren, M. W., Barnard, Y. F., & Sandberg, J. A. C. (1994). The think aloud method: A practical approach to modelling cognitive processes. AcademicPress.

Weiland, T. (2017). The importance of context in task selection. Teaching Statistics, 39(1), 20–25.

Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer-Verlag.

Wickham, H. (2017). Tidyverse: Easily install and load the “tidyverse”. CRAN.

Wiedemann, K., Chao, J., Galluzzo, B., & Simoneau, E. (2020). Mathematical modeling with R: Embedding computational thinking into high school math classes. ACM Inroads, 11(1), 33–42.

Wild, C. J., & Pfannkuch, M. (1999). Statistical thinking in empirical enquiry. International Statistical Review, 67(3), 223–248.

Wild, C. J., Pfannkuch, M., Regan, M., & Parsonage, R. (2017). Accessible conceptions of statistical inference: Pulling ourselves up by the bootstraps. International Statistical Review, 85(1), 84–107.

Wouters, P., Paas, F., & van Merriënboer, J. J. (2008). How to optimize learning from animated models: A review of guidelines based on cognitive load. Review of Educational Research, 78(3), 645–675.

Zieffler, A., Justice, N., delMas, R., & Huberty, M. D. (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.