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Data Science and Machine Learning in mathematics education: Highschool students working on the Netflix Prize

Schönbrodt, Sarah ORCID iD icon 1; Frank, Martin 1
1 Scientific Computing Center (SCC), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

One goal of contemporary mathematical modeling classes in schools should be to include up-to-date problems or interesting, new technologies from the everyday life of students-especially if these allow the didactical reduction to elementary (school-)mathematical knowledge and thus have the potential to enrich mathematics education. Data Science and Machine Learning is applied in numerous areas of science and technology and used in many applications in our everyday life. Using movie recommender systems and the so-called Netflix Prize as an example, this paper discusses how mathematics education can be enriched by modeling real-world, student-centered problems from the field of Machine Learning in school. For this purpose, we describe tested digital learning material from guided modeling projects and share our experience with giving the problem of developing a recommender system as a completely open problem to upper secondary students.


Preprint §
DOI: 10.5445/IR/1000154051
Veröffentlicht am 22.12.2022
Cover der Publikation
Zugehörige Institution(en) am KIT Scientific Computing Center (SCC)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2022
Sprache Englisch
Identifikator KITopen-ID: 1000154051
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), Feb 2022, Bozen-Bolzano, Italy
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 19.08.2022
Schlagwörter Data Science, Machine Learning, mathematical modeling, recommender system, digital, learning material
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