Abstract
The role of data is increasing in a society plagued by elements of uncertainty such as the COVID-19 pandemic, climate change and natural disasters. On the one hand, mathematical and statistical representations (models) can be seen in the media and in public data to make informed predictions and decisions about such uncertain events. On the other hand, in the field of educational research, more and more researchers are working “on or at the border” between the mathematical modelling education community and the statistics/data science education community with respect to mathematical modelling with data and data/statistical modelling. These backgrounds imply that we need to organise data-rich modelling in school mathematics including statistics education, on the one hand for a data-driven society in which students survive, and on the other hand for stimulating conversation and collaborative research between the two scientific communities.
In my completed PhD thesis, I developed the Data-Driven Modelling (DDM) framework in school mathematics, which integrates mathematical and data/statistical modelling and provides a platform and authentic need for both modelling. This framework aims to describe students’ DDM activities and design tasks so that they can flexibly use both deterministic reasoning based on mathematical models and non-deterministic/stochastic reasoning based on statistical models to make well-informed predictions and decisions. Furthermore, I have extended the DDM framework into interdisciplinary and socio-critical contexts in school and teacher education.
In this talk, I will first briefly report on the recent joint discourses between the mathematical modelling and the statistics/data science communities. I will then present three strands of the DDM approach in line with these discourses:
- DDM with mathematics and statistics at its core
- Interdisciplinary DDM with not only mathematics and statistics, but also other disciplines/subjects
- Societal DDM with a focus on social decision making.
In addition, I will present empirical examples of interdisciplinary DDM with Grade 4 students using a seed dispersal task and societal DDM with pre-service teachers using a COVID-19 task.
Takashi Kawakami
Dr Takashi Kawakami is an Associate Professor of Mathematics Education at the Cooperative Faculty of Education, Utsunomiya University, Japan, after five years as a primary school teacher. His research interests include the teaching and learning of mathematical modelling, data/statistical modelling, and integrated STEM in school and teacher education. He has won many awards in the fields of mathematics, statistics and science education, such as the Highly Commended Award for an Early Career Researcher in the 10th International Conference on Teaching Statistics (ICOTS-10) and the Excellent Paper Awards in the Japan Society for Science Education and the Mathematics Education Society of Japan. He was a member of the Local Organising Committee of the 21st International Conference on the Teaching of Mathematical Modelling and Applications (ICTMA-21). He was also a member of the Survey Team on Statistics and Data Science Education as a Vehicle for Empowering Citizens in the 15th International Congress on Mathematical Education (ICME-15).
