IASE 2023 Satellite Conference
Fostering Learning of
Statistics and Data Science
Hybrid Conference
11 – 13 July 2023,   Toronto, Canada
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Keynote Talks

Walking Backwards into the Future

Chris Wild - University of Auckland, New Zealand

New Zealand Māori have an expression “Ka mua, ka muri”, which translates as “Walking backwards into the future.” A longer version of the proverb, “Kia whakatōmuri te haere whakamua”, translates as “I walk backwards into the future with my eyes fixed on my past.” The aptness of the metaphor is clear. It is precisely how statisticians do forecasting. So, in statistics education, how do we chart our way into an uncertain future inevitably informed only by the past. To do this in a reasoned way we need to ask and answer some questions? There are questions of identity and purpose: Who are we? Who we want to serve and how? There are questions about environment: Where do we live? Who are our closest neighbours? What are the major strengths and limitations of humans, and of machines, relevant to our mission? We need forecasts of where we are heading: In the environment in which we will be operating, what’s changing and how fast? And what will remain much the same? How can we exploit foreseeable opportunities and combat foreseeable hazards? But we also know that the future is largely unpredictable. We know that major, unseen, disruptors are inevitable. So, how can we future-proof our children/students and build resilience in anticipation of a rapidly and unpredictably changing landscape? What will they need to know, or no longer need to know? What habits of mind, dispositions and soft skills will they need? And if all that is not enough, how can we further democratise data and analytics so that significant capabilities for learning from data are no longer confined just to the well-resourced in the richest societies?
This talk will at least skim across most of these questions. Everything else will be set for homework!

Lessons Learned from Five Years of Data Technologies Course Projects at Utah State University

Jürgen Symanzik - Utah State University, USA

Teaching an annual Data Technologies course for graduate and undergraduate students at Utah State University and finding a new suitable course project each year for the graduate students can be a daunting task. Only a few examples of such projects have been outlined in the literature. In this presentation, we look at the course content and its student audience in general and then focus on the course projects: Which topics have been selected (e.g., the Kennedy files, data related to the Marvel Cinematic Universe, and the Billboard Hot 100 charts)? What were particular challenges for the students in these projects? What worked well – and what not? What does an instructor have to do to make these course projects a successful learning experience for the students? Templates for the project descriptions and multi-point grading criteria will be provided.

Preparing the Next Generation for an AI-Enabled World:
A Case Study on Developing a Data Science Curriculum for High School Students and a Roadmap for Success in an Increasingly AI-Enabled world.

Shingai Manjengwa - Vector Institute for Artificial Intelligence, Canada

This talk shares a case study on developing a data science curriculum for high school students in Ontario. We'll cover challenges, lessons learned, and effective implementation strategies, such as collaboration and a multidisciplinary approach.
We'll also explore key skills, such as critical thinking, problem-solving, mathematics, foundational knowledge, and prompt engineering, that will be essential for success in an AI-enabled world.

Panel Discussion

Putting Research into Practice: Applying Evidence-based Principles to Foster Student Learning in Statistics and Data Science

Bruno de Sousa - Universidade de Coimbra, Portugal
Anna Fergusson - University of Auckland, New Zealand
Laura Le - University of Minnesota, USA
Samantha-Jo Caetano - University of Toronto, Canada

How can we apply evidence from research to best foster learning in our students? Inspired by the conference theme, this panel session will give insight into the practical application of research-based learning principles to the teaching of Statistics and Data Science, guided by the eight principles described in Lovett et al. (2023). These principles are grounded in learning theory and pedagogy and backed by empirical evidence. Panellists will share illustrative examples of how they are enacted in their teaching and participants will be encouraged to reflect on their own strategies for supporting learning through following these principles.

Lovett, MC, Bridges, MW, DiPietro, M, Ambrose, SA, & Norman, MK. (2023). How Learning Works: Eight Research-based Principles for Smart Teaching. John Wiley & Sons.