Analysing artificial neural networks in high school mathematics education
Kindler, Stephan
1; Schönbrodt, Sarah
1; Frank, Martin
1
1 Scientific Computing Center (SCC), Karlsruher Institut für Technologie (KIT)
1; Schönbrodt, Sarah
1; Frank, Martin
11 Scientific Computing Center (SCC), Karlsruher Institut für Technologie (KIT)
Abstract:
This article presents an intended learning path for high school students, through which they can be able to grasp and categorise the central properties of artificial neural networks. This will be achieved by applying classical methods of analysis to the mathematical building blocks of a simple network. The aim is to enable students to have an informed opinion about the inherent limitations of such networks, thereby demystifying this new technology.
| Zugehörige Institution(en) am KIT | Scientific Computing Center (SCC) |
| Publikationstyp | Proceedingsbeitrag |
| Publikationsdatum | 01.07.2025 |
| Sprache | Englisch |
| Identifikator | KITopen-ID: 1000188809 |
| HGF-Programm | 46.21.02 (POF IV, LK 01) Cross-Domain ATMLs and Research Groups |
| Erschienen in | Towards Fostering AI and Data Science Literacy in Schools Across Disciplines: Proceedings of the 1st Symposium on Integrating AI and Data Science into School Education Across Disciplines (AIDEA1 2025). Ed.: S. Podworny & S. Schönbrodt |
| Veranstaltung | 1st Symposium on Integrating AI and Data Science into School Education Across Disciplines (AIDEA1 2025), Salzburg, Österreich, 24.02.2025 – 28.02.2025 |
| Externe Relationen | Konferenz |
| Schlagwörter | Learning materials, mathematics education, artificial neural networks, artificial intelligence |