Teaching Machine Learning in School: A Systematic Mapping of the State of the Art

Abstract

Although Machine Learning (ML) is integrated today into various aspects of our lives, few understand the technology behind it. This presents new challenges to extend computing education early to ML concepts helping students to understand its potential and limits. Thus, in order to obtain an overview of the state of the art on teaching Machine Learning concepts in elementary to high school, we carried out a systematic mapping study. We identified 30 instructional units mostly focusing on ML basics and neural networks. Considering the complexity of ML concepts, several instructional units cover only the most accessible processes, such as data management or present model learning and testing on an abstract level black-boxing some of the underlying ML processes. Results demonstrate that teaching ML in school can increase understanding and interest in this knowledge area as well as contextualize ML concepts through their societal impact.

Development of an Artificial Intelligence Education Model of Classification Techniques for Non-computer Majors

“…For AI technology innovation, it is important to secure the data necessary for AI training. However, some companies, such as Google, are paying attention to a transfer learning technology that achieves the desired goal by retraining only part of the data with a pre-trained AI model, even if the amount of data is insufficient [16].…”

Visual tools for teaching machine learning in K-12: A ten-year systematic mapping

“…Although there have been some AI teaching initiatives in K-12 already in the 1970s (Kahn, 1977 ; Papert & Solomon, 1971 ), and involving ML in the 1990s (Bemley, 1999 ), only recently it has become a trend again (Marques et al, 2020 ). In this context, the AI for K-12 Working Group (AI4K12) aims at developing guidelines for teaching K-12 students about Artificial Intelligence.…”

“…Other reviews on visual languages in K-12 focus on teaching computational thinking, not covering ML (Hubwieser et al, 2015;Kraleva et al, 2019;Noone & Mooney, 2018;Weintrop & Wilensky, 2017). Reviews on teaching ML in K-12, such as Marques et al (2020) provide an overview of existing educational units, without analyzing in detail the adopted tools, and Long and Magerko (2020) focus on the definition of AI/ML literacy. On the other hand, reviews on ML tools in general, such as by Dudley and Kristensson (2018) analyze only the user interface design of interactive ML tools.…”

Constructionism and AI: A history and possible futures

“…What is already known about this topic • There exist excellent broad surveys of the current status of teaching machine learning in schools, for example Marques et al (2020). There are historical collections of AI and education research papers that include descriptions of constructionist activities, for example Yazdani (1984).…”