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

Teaching Machine Learning in school helps students to be better prepared for a society rapidly changing due to the impact of Artificial Intelligence. This requires age-appropriate tools that allow students to develop a comprehensive understanding of Machine Learning in order to become creators of sm...

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Published in:Education and information technologies 2021-09, Vol.26 (5), p.5733-5778
Main Authors: Gresse von Wangenheim, Christiane, Hauck, Jean C. R., Pacheco, Fernando S., Bertonceli Bueno, Matheus F.
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Computer Appl. in Social and Behavioral Sciences
Computer Science
Computer Science Education
Computers and Education
Education
Educational aspects
Educational Technology
Elementary Secondary Education
Extracurricular activities
Information Systems Applications (incl.Internet)
Instructional Materials
Learning Processes
Machine learning
Mobile applications
Multimedia industry
Students
Teaching
Teaching Machines
User Interfaces and Human Computer Interaction
Visual Aids
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description Teaching Machine Learning in school helps students to be better prepared for a society rapidly changing due to the impact of Artificial Intelligence. This requires age-appropriate tools that allow students to develop a comprehensive understanding of Machine Learning in order to become creators of smart solutions. Following the trend of visual languages for introducing algorithms and programming in K-12, we present a ten-year systematic mapping of emerging visual tools that support the teaching of Machine Learning at this educational stage and analyze the tools concerning their educational characteristics, support for the development of ML models as well as their deployment and how the tools have been developed and evaluated. As a result, we encountered 16 tools targeting students mostly as part of short duration extracurricular activities. Tools mainly support the interactive development of ML models for image recognition tasks using supervised learning covering basic steps of the ML process. Being integrated into popular block-based programming languages (primarily Scratch and App Inventor), they also support the deployment of the created ML models as part of games or mobile applications. Findings indicate that the tools can effectively leverage students’ understanding of Machine Learning, however, further studies regarding the design of the tools concerning educational aspects are required to better guide their effective adoption in schools and their enhancement to support the learning process more comprehensively.
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subjects Algorithms
Artificial Intelligence
Computer Appl. in Social and Behavioral Sciences
Computer Science
Computer Science Education
Computers and Education
Education
Educational aspects
Educational Technology
Elementary Secondary Education
Extracurricular activities
Information Systems Applications (incl.Internet)
Instructional Materials
Learning Processes
Machine learning
Mobile applications
Multimedia industry
Students
Teaching
Teaching Machines
User Interfaces and Human Computer Interaction
Visual Aids
title Visual tools for teaching machine learning in K-12: A ten-year systematic mapping
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