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.
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 on 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, learning 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 image recognition model of recycling trash 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.
Uma vez que a Machine Learning (ML) está presente em vários aspectos de nossas vidas, novos desafios são apresentados à educação em ajudar estudantes a entender os potenciais e limites dessa tecnologia. Para obter um panorama do estado da arte do ensino de ML, foi realizado um mapeamento sistemático. Foram identificadas 39 unidades instrucionais focadas em conceitos básicos e redes neurais. Muitas unidades abordam apenas de forma parcial o processo de ML, como gerenciamento de dados, ou apresentam alguns processos apenas de forma abstrata. Os resultados obtidos indicam que o ensino de ML na educação básica pode aumentar o entendimento e interesse, bem como contextualizar conceitos de ML na vida dos estudantes.
O presente artigo aponta os resultados percebidos por alunos na utilização de um fliperama cujo jogo é destinado à preparação para o Exame Nacional do Ensino Médio (ENEM). Estruturado por uma pesquisa inicial acerca dos impactos da tecnologia na educação e do real interesse dos alunos em utilizar um jogo como o proposto, o FlipENEM foi desenvolvido com base em conhecimentos de hardware, programação, banco de dados e redes e teve os resultados confirmados por meio de outra pesquisa. Além disso, foi desenvolvido em uma estrutura de madeira, com botões e joystick formando um fliperama. Através da utilização do FlipENEM pelos alunos, obteve-se resultados positivos sobre a plataforma e também, em relação à sua contribuição em estimular os estudantes à preparação para o ENEM.
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