From school mathematics to artificial neural networks: Developing a mathematical model to predict life expectancy

Stephan Kindler (1) , Sarah Schönbrodt (1) , Martin Frank (1)

1 KIT - Karlsruhe Institute of Technology

In this paper, we show that it is possible to develop artificial neural networks building on school mathematical knowledge – initially avoiding AI terminology and comparisons with biological neurons since both are unnecessary to understand the underlying mathematical concepts. We present a didactical reduction of the mathematical foundations of a simple artificial neural network using the example of regression problems. It becomes clear that numerous connections to school mathematical content exist, not only from statistics but also from the area of analysis and linear algebra. As part of a design-based research project we developed digital teaching and learning material that builds on the presented didactical reduction. The material allows upper secondary students to develop the mathematical ideas of artificial neural networks in a problem-oriented way. The central building blocks of the material and first experiences with students are described.

Mots clés: Artificial intelligence; machine learning; mathematics education; artificial neural networks; Jupyter Notebooks

Domaines: Mathématiques [math]; Education

Stephan Kindler, Sarah Schönbrodt, Martin Frank. From school mathematics to artificial neural networks: Developing a mathematical model to predict life expectancy. Thirteenth Congress of the European Society for Research in Mathematics Education (CERME13), Alfréd Rényi Institute of Mathematics; Eötvös Loránd University of Budapest, Jul 2023, Budapest, Hungary. ⟨hal-04410971⟩