I’m pretty excited about the talks and workshops I’m doing over the next month or so! Below are the summaries or abstracts for each talk/workshop and when I get a chance I’ll write up some of the ideas presented in separate posts.

** Keynote: Searching for meaningful sampling in apple orchards, YouTube videos, and many other places!** (AMA, Auckland, September 14, 2019)

In this talk, I shared some of my ideas and adventures with developing more meaningful learning tasks for sampling. Using the “Apple orchard” exemplar task, I presented some ideas for “renovating” existing tasks and then introduced some new opportunities for teaching sample-to-population inference in the context of modern data and associated technologies. I shared a simple online version of the apple orchard and also talked about how my binge watching of DIY YouTube videos led to my personal (and meaningful) reason to sample and compare YouTube videos.

** Workshop: Expanding your toolkit for teaching statistics** (AMA, September 14, Auckland, 2019)

In this workshop, we explored some tools and apps that I’ve developed to support student’s statistical understanding. Examples were: an interactive dot plot for building understanding of mean and standard deviation, a modelling tool for building understanding of distributional variation, tools for carrying out experiments online and some new tools for collecting data through sampling.

The slides for both the keynote and workshop are embedded below:

*Talk***:**** Introducing high school statistics teachers to code-driven tools for statistical modelling** (VUW/NZCER, Wellington, September 30, Auckland, 2019)

**Abstract: **The advent of data science has led to statistics education researchers re-thinking and expanding their ideas about tools for teaching and learning statistical modelling. Algorithmic methods for statistical inference, such as the randomisation test, are typically taught within NZ high school classrooms using GUI-driven tools such as VIT. A teaching experiment was conducted over three five-hour workshops with six high school statistics teachers, using new tasks designed to blend the use of both GUI-driven and code-driven tools for learning statistical modelling. Our findings from this exploratory study indicate that teachers began to enrich and expand their ideas about statistical modelling through the complementary experiences of using both GUI-driven and code-driven tools.

** Keynote: Follow the data** (NZAMT, Wellington, October 3, 2019)

**Abstract: **Data science is transforming the statistics curriculum. The amount, availability, diversity and complexity of data that are now available in our modern world requires us to broaden our definitions and understandings of what data is, how we can get data, how data can be structured and what it means to teach students how to learn from data. In particular, students will need to integrate statistical and computational thinking and to develop a broader awareness of, and practical skills with, digital technologies. In this talk I will demonstrate how we can* follow the data* to develop new learning tasks for data science that are inclusive, engaging, effective, and build on existing statistics pedagogy.

**Workshop: ****Just hit like! Data science for everyone, including cats (and maybe dogs)** (NZAMT, Wellington, October 2, 2019)

**Abstract: **Data science is all about integrating statistical and computational thinking with data. In this hands-on workshop we will explore a collection of learning tasks I have designed to introduce students to the exciting world of image data, measures of popularity on the web, machine learning, algorithms, and APIs. We’ll explore questions such as “Are photos of cats or dogs more popular on the web?”, “What makes a good black and white photo?”, “How can we sort photos into a particular order?”, “How can I make a cat selfie?” and many more. We’ll use familiar statistics tools and approaches, such as data cards, collaborative group tasks and sampling activities, and also try out some new computational tools for learning from data. Statistical concepts covered include features of data distributions, informal inference, exploratory data analysis and predictive modelling. We’ll also discuss how each task can also be extended or adapted to focus on specific aspects and levels of the statistics curriculum. Please bring along a laptop to the workshop.

I’m also presenting a workshop at NZAMT with Christine Franklin on what makes a good statistical task. I’ve been assisting Maxine Pfannkuch and members of the NZSA education committee to set up a new teaching journal, which we will be launching at the workshop!!