Session #11, Part 1: How much mathematical modeling is in AI?
Martin Frank and Sarah Schönbrodt (Germany)
We will try to argue that mathematical modeling, in the sense of a certain kind of mathematical thinking in an interdisciplinary context, is becoming ever more important in the age of digitalization, the data deluge, and AI. It should therefore play a key role in education.
We will try to outline how this could be achieved. We will also raise questions: How mechanical does mathematics education have to be? How much room is there for creativity? How to balance disciplinarity and interdisciplinarity? How to create awareness of the key role of mathematical modeling?
In addition, we present tested, computer-based teaching material for mathematical modeling projects with high-school students. The material was developed within the CAMMP project (www.camp.online) and focuses on the problem-oriented development of the mathematical foundations of AI methods. We try to show that it is indeed possible to embed AI into mathematics education in a meaningful way.
Session #11, Part 2: The Role of Data in School-Based Citizen Science
Dani Ben-Zvi (Israel)
The United Nation’s fourth sustainable development goal calls for inclusive and equitable quality education for all. This includes broad educational goals such as supporting students’ agency to navigate a complex and uncertain world with broad sets of knowledge, skills, attitudes, and values.
Based on our work at the Taking Citizen Science to School (TCSS) research center, we find an opportunity to achieve such goals in school participation in a social phenomenon, known as citizen science, where scientists partner with the public to advance scientific research. Specifically, as citizen science often involves exploration of large and messy sets of data, school-based citizen science is a highly fertile ground for nurturing both scientific and data literacies among students.
For instance, in the TCSS Radon project, implemented in dozens of school in Israel, students learned from scientists about Radon and some unsolved scientific issues regarding its nature and measurement. They collected data by measuring Radon levels in their homes, analyzed a collective dataset from all schools, made informal inferences, and communicated them to their community and to the scientists. Their learning was scaffolded by a sequence of learning activities co-designed by the TCSS community and unique technology-enhanced tools to support data analysis and modeling. Findings indicate mutual benefits for students and scientists. Students developed competences for making sense of large and messy datasets, and for making informed informal inferences based on their explorations. The scientists, based on the data collected by students, developed a technique for identifying buildings with high Radon concentrations.
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