Towards the end of last year, I made a post on LinkedIn in response to the announcement by the Minister of Education regarding new secondary school subjects. I’ve copied the text of this post below:

A few months back, Minister of Education Erica Stanford announced a new set of future focused secondary school subjects, including a specialist Year 13 subject called “Statistics and Data Science”. In her statement, she says, “When students get to high school, we want them to have access to innovative and dynamic subjects that will help grow New Zealand’s future and take on the world.”

I couldn’t agree more – statistics and data science are both innovative and dynamic subjects that are important for our ākonga to engage with in order to prepare them for an uncertain future. However, I would go further and say that both statistics and data science are not just subjects to specialise in at high school, but subjects that are vitally important for all students to “generalise in”, right from the start of schooling.

I am an academic and researcher based at the University of Auckland who specialises in data science and statistics education research. Although I have been known to incorporate “silly“ things like cats, memes and YouTube videos, I actually take my job very seriously and have dedicated my entire professional career to advancing the teaching and learning of statistics and data science.

That’s why I know that there is an established, internationally-recognised, and research-informed body of knowledge that also supports the position that in order to empower students to grow New Zealand’s future and take on the world, we need to include statistics and data science across all levels of school curricula.

Furthermore, research and practice-based reports consistently reinforce that statistics and data science need to be implemented using teaching approaches that develop and support humanistic, statistical, and computational thinking, including knowledge and practices such as recognising the importance of the data context and using statistical enquiry cycles.

In practical terms, this means that students need to learn how to conduct purposeful and critical exploration and investigation of data using statistical and computational tools and models, grounded in a humanistic approach that recognises the role of culture, society, and context.

Students need to be taught that in statistics and data science, we value diverse perspectives and knowledge, cultural and ethical reflection, and that we seek to understand not just the “numbers”, but also the motivations, decisions, and communities embedded in the data and data-based products. These are the key attributes of learning statistics and data science that make them innovative and dynamic subjects, and why I love teaching data science and statistics! 😺📊🤖

I am fortunate to be on research and study leave for the first half of this year, and during this time have planned to write a multi-part series of blog posts sharing my views and research on the potential for teaching data science at the high school level in Aotearoa New Zealand. I’ve been working in this research space since 2015 when I joined the University of Auckland | Waipapa Taumata Rau, although my application of data science practices within education started in 2003 when I first begun teaching Mathematics and Statistics at Avondale College. Currently, I am working on a few different data science and statistics education research projects, including an externally funded collaborative project with researchers from Paderborn University, Germany, which I wrote about here: Preparing high school teachers and students for a data science future.

So, if you’re interested in following along for the next few months, feel free to subscribe to this blog so you get email notifications when I post, or just bookmark this page for future reference. Additionally, for all my Aotearoa NZ teachers, as a result of my NZAMT 2023 Bevan Werry Speaker award, you can arrange for me to give talks and/or run workshops about data science/statistics with teachers through your regional mathematics associations. These sessions will combine my research and design of practical teaching tasks, with a focus on integrating statistical and computational thinking, data science practice, and data technologies. Below are the related “Bevan Werry” talks/workshops I’ve given so far:

  • Whangārei (New Zealand Association of Mathematics Teachers conference) [2023]
    Exploring data landscapes: Towards more personalised learning journeys
    Sounds like data science: A practical introduction to integrating statistical and computational thinking using music
  • Auckland (Statistics Teachers’ Day) [2023]
    Fake it until you make it! Using probabilty models to create animated monsters that can fake it as humans
    Exploring text data and more with Google sheets
  • Whangārei (Northland Mathematics Association) [2024]
    Data Science Unplugged: Integrating statistical and computational thinking to learn from data, without computers
  • Tauranga (Bay of Plenty Mathematics Association) [2024]
    Data Science Unplugged: Integrating statistical and computational thinking to learn from data, without computers
  • Christchurch (Canterbury Mathematics Association) [2025]
    Explorations in variation: Supporting and developing curiosity and creativity through data exploration
    From sketchy intuitions to imperfect rules: Introducing informal classification models at the school level
  • Timaru (Aoraki Mathematics Association) [2025]
    Preparing for an uncertain future: Tools and skills to cover in Phase 4 to prepare ākonga for Phase 5 Statistics

I’m not sure at this stage how many parts there will be to this series, or how often I will make posts, but I would like to cover key aspects of curriculum, assessment, and task design, as well as review existing AotearoaNZ-specific resources as part of considering where to next for future-focused statistics and data science education in Aoteroa New Zealand. I’m hoping what I share will be helpful for teachers to inform their practice, as well as for other researchers and educators exploring how to support effective and inclusive teaching and learning of data science.