Bio
Anna Fergusson is passionate about teaching, data technologies, and developing ways to introduce people to learning statistics and data science that are inclusive, engaging, accessible, effective and fun. She has led several statistics and data science curriculum design projects, and supports and advances her teaching, research and data analysis activities by creating new software tools and educational technologies. Anna’s research specialty is data science and statistics education, with a focus on technology-based and technology-informed pedagogy, including but not limited to: large-scale teaching and assessment practices and tools; introduction of computer programming for data science and associated design principles for tool and task design; tool-mediated development of statistical concepts and reasoning, such as graphical and visual inference; frameworks for observable integrated statistical and computational thinking practices; and designing professional development opportunities for high school data science teachers.
Talks/workshops
2025
Upcoming
- A task design framework for introducing code-driven tools through statistical modelling, February 2025, Data Science Education K-12: Research to Practice Conference
- “It’s a different D … a different way of thinking about PPDAC”: Examining teachers’ perspectives on using computational approaches to engage learners with complex data, June 2025, SRTL conference
- From sketchy intuitions to imperfect rules: Using digital image data from drawings to introduce informal classification models (workshop), July 2025, USCOTS
- USCOTS research satellite (keynote), July 2025, USCOTS
2024
- Embracing creativity through explorations in probability and modelling, June 2024, AMA online
- Data science doesn’t happen in a vaccuum: An initial exploration of high school statistic teachers’ data practices with messy data, July 2024, IASE 2024 Roundtable Conference
- Towards a framework for assessing integrated statistical and computational thinking, July 2024, ICME
- From sketchy intuitions to imperfect rules: Using digital image data from drawings (sketches) to introduce informal classification models, July 2024, MWM/ASA
- Go big or go home: Innovations in large scale assessment practice, August 2024, JSM
- Data Science Unplugged: Integrating statistical and computational thinking to learn from data, without computers (mostly), August 2024, NMA
- The data science of drawings!, August 2024, UoA Computer Science outreach event
- Designing positive first experiences with coding for introductory level statistics and data science students, October 2024, IASE webinar
- Developing tools for “real time” formative assessment of writing within large introductory statistics and data science courses, October 2024, WOMBAT 2024
- Data Science Unplugged: Integrating statistical and computational thinking to learn from data, without computers (mostly), November 2024, BOPMA
- Getting the best of both worlds: Integrating human and automated assistance to support student learning via an online question-answering platform, November 2024, STELA, Faculty of Science, UoA
- Lost (and found) in translation: Examining the diversity and impact of languages selected on student responses to a statistical investigation of automated language translation, November/December 2024, NZARE/NZSA conferences
- Engaging with data through education research, November 2024, UoA/AMA Statistics Teachers’ Day
- Using grayscale photos to introduce high school statistics teachers to reasoning with digital image data, December 2024, JSDSE/CAUSE webinar
- Introducing a data science perspective on predictive modelling within a large introductory statistics course: Connecting research with practice, December 2024, ProDaBi Colloqium
- Designing positive first experiences with coding for introductory level statistics and data science students, December 2024, PISTAR webinar
Publications
Fergusson, A. & Pfannkuch, M. (2025). A task design framework for introducing code-driven tools through statistical modelling. Preprint DSEK12 conference paper.
Fergusson, A., Pfannkuch, M. & Budgett, S. (2025). Data cleaning doesn’t happen in a vacuum: An initial exploration of high school statistics teachers’ data practices with messy data. In: Kaplan, J. & Luebke, K. (Ed.) (2024). Connecting Data and People for Inclusive Statistics and Data Science Education. Proceedings of the Roundtable conference of the International Association for Statistics Education (IASE), July 2024, Auckland, New Zealand. IASE. doi.org/10.52041/iase24.301
Ferguson, A. & Pfannkuch, M. (2024). Towards a framework for assessing integrated statistical and computational thinking. Preprint ICME conference paper.
Fergusson, A. (2024). Designing positive first experiences with coding for introductory-level data science students. In: EM Jones (Ed.), Fostering Learning of Statistics and Data Science. Proceedings of the Satellite conference of the International Association for Statistical Education (IASE), July 2023, Toronto, Canada. IASE. doi.org/10.52041/iase2023.503
Caetano, S-J., de Sousa, B., Fergusson, A., Le, L., Gibbs, A. L., White, B., & Damouras, S. (2024). Putting research into practice: Applying evidence-based principles to foster student learning in statistics and data science. In: EM Jones (Ed.), Fostering Learning of Statistics and Data Science. Proceedings of the Satellite conference of the International Association for Statistical Education (IASE), July 2023, Toronto, Canada. IASE. doi.org/10.52041/iase2023.701
Fergusson, A., & Pfannkuch, M. (2024). Using grayscale photos to introduce high school statistics teachers to reasoning with digital image data. Journal of Statistics and Data Science Education, 32(4), 345–360. doi.org/10.1080/26939169.2024.2351570
Fergusson, A., & Pfannkuch, M. (2022). Introducing high school statistics teachers to predictive modelling and APIs using code-driven tools. Statistics Education Research Journal, 21(2). doi.org/10.52041/serj.v21i2.49
Fergusson, A., & Pfannkuch, M. (2022). Introducing teachers who use GUI-driven tools for the randomization test to code-driven tools. Mathematical Thinking and Learning, 24(4), 336-356. doi.org/10.1080/10986065.2021.1922856
Fergusson, A. (2022). Towards an integration of statistical and computational thinking: Development of a task design framework for introducing code-driven tools through statistical modelling (Doctoral dissertation, University of Auckland). hdl.handle.net/2292/64664
Fergusson, A., & Wild, C. J. (2021). On traversing the data landscape: Introducing APIs to data‐science students. Teaching Statistics, 43, S71-S83. doi.org/10.1111/test.12266
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. doi.org/10.1080/10691898.2020.1837039
Fergusson, A., & Bolton, E. L. (2018). Exploring modern data in a large introductory statistics course. In M. A. Sorto, L. White, & L. Guyot (Eds.), Looking Back, Looking Forward. Proceedings of the Tenth International Conference on Teaching Statistics (ICOTS10, July 2018), Kyoto, Japan. International Statistical Institute. iase-web.org/icots/10/proceedings/pdfs/ICOTS10_3C1.pdf?1532045286
Fergusson, A. M. (2017). Informally testing the fit of a probability distribution model. Masters dissertation, University of Auckland, New Zealand. ResearchSpace@Auckland. hdl.handle.net/2292/36909
Starnes, D., & Martin, A. (2015). The AP Statistics Exam: An Insider’s Guide to its Distinctive Features. CHANCE, 28(3), 28-37. doi.org/10.1080/09332480.2015.1099363