Extending Data-Driven Modelling from School Mathematics to School STEM Education

This study focuses on ‘modellingModelling’ and ‘data’ to ensure the effective implementation of STEM educationSTEM education, specifically emphasising the ‘M’ (Mathematics) at its core. While the modellingModelling process is used to develop real-world interdisciplinary problem-solving and concepts in STEMProblem-solving in STEM subjects, data are inherently interdisciplinary. In this chapter, we illustrate the potential of extending data-driven modellingData-driven modelling (DDM), which involves the construction of mathematical and statistical modelsMathematical and statistical models that describe and explain variability in data to make better predictions about real-world events, from school mathematics to school STEM educationSTEM education. First, we review a framework for describing DDM in school mathematics. Second, given the interdisciplinary nature of data and modellingModelling, we extend the DDM frameworkEducational frameworks to school STEM educationSTEM education and explore a framework of interdisciplinary DDM that can connect knowledge and practice across STEM subjects. Third, we explore the paper helicopter and seed dispersal tasks within the interdisciplinary DDM frameworkInterdisciplinary DDM framework. Finally, we present an example of interdisciplinary DDM practice for Year 4 (9–10-year-olds) students using the seed dispersal task. The extended interdisciplinary DDM frameworkInterdisciplinary DDM framework provides research implications for school STEM educationSTEM education and practical implications for developing STEM tasks and units that highlight the role of the ‘M’.