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Teaching data science in school: Digital learning material on predictive text systems

Hofmann, Stephanie 1; Frank, Martin 1
1 Scientific Computing Center (SCC), Karlsruher Institut für Technologie (KIT)

Abstract:

Data science and especially machine learning issues are currently the subject of lively discussions in society. Many research areas now use machine learning methods, which, especially in combination with increased computer power, has led to major advances in recent years. One example is natural language processing. A large number of technologies and applications that we use every day are based on methods from this area. For example, students encounter these technologies in everyday life through the use of Siri and Alexa but also when chatting with friends they are supported by assistance systems such as predictive text systems that give suggestions for the next word. This proximity to everyday life is used to give students a motivating approach to data science concepts. In this paper we will show how mathematical modeling of data science problems can be addressed with students from tenth grade or higher using digital learning material on predictive text systems.


Preprint §
DOI: 10.5445/IR/1000154062
Veröffentlicht am 22.12.2022
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte und Numerische Mathematik (IANM)
Scientific Computing Center (SCC)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2022
Sprache Englisch
Identifikator KITopen-ID: 1000154062
HGF-Programm 46.21.02 (POF IV, LK 01) Cross-Domain ATMLs and Research Groups
Erschienen in Twelfth Congress of the European Society for Research in Mathematics Education (CERME12)
Veranstaltung 12th Congress of the European Society for Research in Mathematics Education (CERME 2022), Bozen, Italien, 02.02.2022 – 05.02.2022
Vorab online veröffentlicht am 15.08.2022
Externe Relationen Abstract/Volltext
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