Theme: Exploring worlds through data

2021 Programme and Resources

8:30am - 8:40am: Welcome

8:40am - 9:30am: Keynote 1

10:00am - 11:00am: Workshop session one (CL6A part one, CL6B part one)

11:30am - 12:30pm: Workshop session two (CL6A part two, CL6C part one)

1:00pm - 2:00pm: Workshop session three (CL6B part two, CL6C part two)

2:30pm - 3:20pm: Keynote 2

3:20pm - 3:30pm: Farewell

Keynotes

Simon Harris

Simon joined the University of Auckland in April 2018 as an Associate Professor of Probability in the Department of Statistics. Prior to moving to New Zealand, he spent many years at the University of Bath, United Kingdom. In Bath, he was one of the founding members of ProbL@B which is internationally recognised as a world class probability centre. He contributed to the great success of the lab over many years and his leadership was a major factor in ProbL@B becoming a centre of excellence in branching processes.

He often aims to understand fundamental stochastic population models by making intuitive ideas and heuristic arguments about their behaviour into rigorous, and ideally elegant, mathematics. Typical questions involving branching processes might concern the growth rate of a population, how fast new territory is colonised, the probability a population survives, the effects of selection, or genealogies of certain individuals.

Trees, branching, and random walks...

We will take a bit of a random walk through some probability theory - revealing how random trees branch along the way! Some of the stochastic branching processes and their properties that we will encounter on our journey will be motivated by questions in epidemiology, population genetics, conservation, and even pest control. For example, in epidemiology, what vaccination level is required to prevent an epidemic, and what if vaccine efficacy reduces over time? In conservation, what might knowing the family tree of a sample of individuals reveal about the health of the entire population? We will discuss how theory might help us tackle such questions.

Michael Shadbolt

Michael Shadbolt (no relation) is not a doctor, although he used to play one on TV. He obtained a Scholarship result in Bursary Statistics in 1993, but only got 59% in Bursary Calculus, which explains why he is not doing the keynote at the maths day. Before becoming a teacher, he worked as a writer for TV and radio, data warehousing consultant for Oracle Corporation, magazine editor, kiwifruit grader, voiceover artist, election returning officer, The Edge Roadrunner, marketing manager and live billboard model. Not in that order.

He has since put all of that experience into practice at Otumoetai College and Bethlehem College in Tauranga over the last 15 years. Regular attendees at the AMA Statistics day may have attended his workshops on Adding Awesomesauce to their lessons, Why Statistics Killed the Radio Star, and How to be an Emotional Statistician. He also took the last mini-quiche at lunchtime just before you got there. Sorry about that.

Last period relief with Mr Shadbolt

Thinking of truanting last period? Don’t! Mr Shadbolt is relieving, and he’s ditched the lesson plan and gone rogue! Join us for a grab-bag of fun ideas, random ‘reckons’ and actual activities for your class. Activities include: "When you slay the dragon, who wins the loot?", "Why are there so many songs about rainbows?" and “How come I always get the Cherry Ripes?”

PDF of slides | Powerpoint with notes

To download, use the button in the top right hand corner of the viewing screen.

Workshops - Exploring worlds through data at CL6

Please head to https://www.stat.auckland.ac.nz/~fergusson/exploring_worlds/ for recordings and resources from the workshops.

There’s nothing like a big review of assessment or curriculum to send us into a spin. We invite you to take a spin with us as we re-imagine, review, and refresh data and statistical approaches at Curriculum Level 6 (CL6).

Come along and explore one of the worlds of data. Will you virtually tour the prediction world, the probability modelling world or the inference world? In these workshops we will:

Re-imagine the world from a teaching and learning perspective
Review and share conceptual development ideas based on local and international research and good old kiwi ingenuity (AKA what have we learnt from our experiences to date)
Refresh how we think about data and possibilities for learning new things about the [student’s] world

All of these workshop sessions are designed to be interactive, so please “ZOOM” in using a computer or laptop where possible. Each “world of data” focus has two workshops and have been designed based on teachers attending both. Summaries and the times for the workshops are given below.

CL6A: Sample-to-population inference

Over the last 12 years we have been developing our understanding of sample-to-population inference. Along the way there have been diversions and road blocks. In this workshop we will remind ourselves about the big ideas in sample-to-population inference, explore how we can revitalise teaching and learning of sample-to-population inference and re-imagine ways to engage our students in statistical enquiry with a purpose.

The team for this session is: Pip Arnold, Robyn Headifen, Maxine Pfannkuch, Sophie Wright, Rachael Ouwejan, Zac Rutledge, Marina McFarland, Mark Hooper, Lucy Edmonds, Michelle Dalrymple

CL6B: Probability modelling

It’s time to put ideas about probability and uncertainty at the centre of learning about patterns and relationships in data. We’ll explore a range of activities where students create and explore chance situations. You will think about structuring randomness, stability in distributions, your modelling assumptions, fairness, “what if?” scenarios and good old proportional reasoning. We will use technology to aid our understanding about how random events behave and help us see the chance in data. We have the technology and it’s time to realise its full potential in the classroom.

The team for this session is: Anne Patel, Stephanie Budgett, Katalina Ma, Amy Renelle, Lorraine O’Carroll, Helen Teal, Aaron Webb, Liam Smyth

CL6C: Prediction

A key statistical modelling approach is to use the data you have to predict outcomes/data you don’t (yet) have. The data that we can learn from is rapidly changing (including data sourced from a variety of digital technologies) and can provide exciting opportunities to broaden student awareness and support their personal connection to data. In this workshop, we’ll engage in a range of activities that demonstrate how exploring and investigating relationships between variables can support student engagement with informal approaches to prediction and provide a foundation for predictive modelling at the higher curriculum levels.

The team for this session is: Anna Fergusson, Chris Wild, Jim Davis, Clare Nelson, Lisa Mulvey, Jacqui Hammond, Ash Rambhai, Amy Hooper, Hanna Reid, Marion Steel

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