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Conference Papers Year : 2022

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

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.
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Dates and versions

hal-03751829 , version 1 (15-08-2022)

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  • HAL Id : hal-03751829 , version 1

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Stephanie Hofmann, Martin Frank. Teaching data science in school: Digital learning material on predictive text systems. Twelfth Congress of the European Society for Research in Mathematics Education (CERME12), Feb 2022, Bozen-Bolzano, Italy. ⟨hal-03751829⟩

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