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Machine Learning for All!—Introducing Machine Learning in Middle and High School

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Abstract

Although Machine Learning (ML) is found practically everywhere, few understand the technology behind it. This presents new challenges to extend computing education by including ML concepts in order to help students to understand its potential and limits and empowering them to become creators of intelligent solutions. Therefore, we developed an introductory course to teach basic ML concepts, such as fundamentals of neural networks as well as limitations and ethical concerns in alignment with the K-12 Guidelines for Artificial Intelligence. It also teaches the application of these concepts, by guiding the students to develop a first classification model of recycling trash images using Google Teachable Machine. In order to promote ML education, the interactive course is available online in Brazilian Portuguese to be used as an extracurricular course or in an interdisciplinary way as part of science classes covering recycling topics. The course was applied and evaluated through a series of exploratory case studies with a total of 108 middle and high school students. The results of the evaluation show that middle and high school students were able to understand ML concepts and develop an image classification model. No substantial difference with regard to the educational stage, gender and instructional mode was observed. The course was also perceived as an enjoyable learning experience motivating students to learn more about ML.

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Acknowledgements

A special thanks to all participants in this study who took the time to complete the data collection instruments.

Funding

This work was supported by CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico – www.cnpq.br), an entity of the Brazilian government focused on scientific and technological development (Grant no. 303674/2019–9).

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Authors and Affiliations

  1. Graduate Program in Computer Science, Department of Informatics and Statistics, Federal University of Santa Catarina, Florianópolis, Santa Catarina, Brazil

    Ramon Mayor Martins, Christiane Gresse von Wangenheim, Marcelo Fernando Rauber & Jean Carlo Hauck

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  1. Ramon Mayor Martins
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  2. Christiane Gresse von Wangenheim
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  3. Marcelo Fernando Rauber
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  4. Jean Carlo Hauck
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Contributions

All authors contributed to the study's conception and design. Material preparation, data collection and analysis were performed by Ramon Mayor Martins, Christiane Gresse von Wangenheim, Marcelo Fernando Rauber and Jean Carlo Rossa Hauck. The first draft of the manuscript was written by Ramon Mayor Martins and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Ramon Mayor Martins.

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Martins, R.M., von Wangenheim, C.G., Rauber, M.F. et al. Machine Learning for All!—Introducing Machine Learning in Middle and High School. Int J Artif Intell Educ (2023). https://doi.org/10.1007/s40593-022-00325-y

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