
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.
bepress|Education|Secondary Education, bepress|Education|Elementary Education, Elementary Education, bepress|Education|Science and Mathematics Education, SocArXiv|Education|Secondary Education, SocArXiv|Education|Science and Mathematics Education, Education, SocArXiv|Education, SocArXiv|Education|Elementary Education, bepress|Education, Science and Mathematics Education, Secondary Education
bepress|Education|Secondary Education, bepress|Education|Elementary Education, Elementary Education, bepress|Education|Science and Mathematics Education, SocArXiv|Education|Secondary Education, SocArXiv|Education|Science and Mathematics Education, Education, SocArXiv|Education, SocArXiv|Education|Elementary Education, bepress|Education, Science and Mathematics Education, Secondary Education
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 9 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
