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Erschienen in:
Introduction—What is This Guide About? Kapitel lesen Erstes Kapitel lesen

2023 | OriginalPaper | Buchkapitel

13. The Pedagogical Challenge of Machine Learning Education

verfasst von : Orit Hazzan, Koby Mike

Erschienen in: Guide to Teaching Data Science

Verlag: Springer International Publishing

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Abstract

Machine learning (ML) is the essence of the modeling phase of the data science workflow. In this chapter, we focus on the pedagogical challenges of teaching ML to various populations. We first describe the terms white box and black box in the context of ML education (Sect. 13.2). Next, we describe the pedagogical challenge with respect to different learner populations including data science major students as well as non-major students (Sect. 13.3). Then, we present three framework remarks for teaching ML (regarding statistical thinking, interdisciplinary projects, and the application domain knowledge), which, despite not being mentioned frequently in this part of the book, are important to be kept in mind in ML teaching processes (Sect. 13.4). We conclude this chapter by highlighting the importance of ML education in the context of the application domain (Sect. 13.5).

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Vorheriges Kapitel Social and Ethical Issues of Data Science
Nächstes Kapitel Core Concepts of Machine Learning
Fußnoten
1
This section is based on Mike and Hazzan (2022). Machine learning for non-major data science students: A white box approach, special issue on Research on Data Science Education, The Statistics Education Research Journal (SERJ) 21(2), Article 10. Reprint is allowed by SERJ journal’s copyright policy.
 
2
This section is based on Mike and Hazzan (2022). Machine learning for non-major data science students: A white box approach, special issue on Research on Data Science Education, The Statistics Education Research Journal (SERJ) 21(2), Article 10. Reprint is allowed by SERJ journal’s copyright policy.
 
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Metadaten
Titel
The Pedagogical Challenge of Machine Learning Education
verfasst von
Orit Hazzan
Koby Mike
Copyright-Jahr
2023
Buch
Guide to Teaching Data Science

Print ISBN: 978-3-031-24757-6

Electronic ISBN: 978-3-031-24758-3

Copyright-Jahr: 2023

DOI
https://doi.org/10.1007/978-3-031-24758-3_13

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