Session #13, Part 1: Evaluating media claims about sustainability through the use of large data sets
Vince Geiger (Australia)
In an era marked by disruption, both global and digital, the application of mathematics and statistics is crucial for understanding, making predictions about, and addressing challenges associated with public health, the environment, and national and global social cohesion.
The COVID-19 pandemic highlighted how mathematics and statistics can be used to present sometimes contradictory and misleading claims by various groups – amplifying the need for citizens be capable of critically evaluating claims made by both expert and non-expert commentators, and the decisions of government. This means that informed and responsible citizens must be capable of understanding the role of mathematics and statistics in underpinning evidence and have the capacity to employ evidentiary practices in forming relevant judgements. In this presentation I report on the aims and approaches that frame an international collaborative project being conducted by Australian Catholic University and Wurzburg University, entitled Strengthening Teachers’ Instructional Capabilities with Big Data. The project is designing and implementing tasks that require middle years students to evaluate differing claims in the media about issues related to sustainability by making use of relevant publicly available large data sets. This includes students’ selection of databases, approaches to modelling data, and their decision-making processes. Participant teachers will engage with professional learning activities based on students’ approaches to resolving differences in media reports.
Session #13, Part 2: A learner-centered approach to teaching computational modeling, data analysis, and programming
Devin W. Silvia (USA)
One of the core missions of Michigan State University’s Department of Computational Mathematics, Science and Engineering (CMSE) is to provide education in computational modeling and data science to MSU’s undergraduate and graduate students.
In this presentation, I will describe our creation of CMSE 201, „Introduction to Computational Modeling and Data Analysis,“ which is intended to be a standalone course teaching undergraduate students (both STEM and non-STEM) core concepts in data analysis, data visualization, and computational modeling. I will discuss the rationale behind the „flipped classroom“ instructional model that we have been using and explain the course’s design principles and implementation. The concepts and skills students learn in this course can be used by other disciplines as the foundation for integrating computing across the curricula in undergraduate degree programs.
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