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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.

Computing education in schools faces several problems, such as a lack of computing teachers and time in an already overloaded curriculum. A solution can be a multidisciplinary approach, integrating the teaching of computing within other subjects, creating the need to motivate teachers from other disciplines to teach computing in middle school. Therefore, the motivation and training of in-service teachers becomes crucial, as they need to have computing content and technological knowledge as well as pedagogical content knowledge. Yet, so far there exist very few training programs. Thus, as part of a comprehensive outreach program, we present a study on a one-day taster workshop for middle school teachers on physical computing education. Participants learn computer programming practice and computational thinking by programming an interactive robot. The workshop also approaches pedagogical aspects for teaching computing and technical issues regarding the installation and preparation of the required hardware/software. Preliminary results of its application with public school teachers in Florianopolis/Brazil are positive, motivating the majority of participants to introduce computing into their classes. However, our results also highlight that in order to enable teacher to apply the workshops effectively, longer training courses and ongoing support is required.

The objective of this article is to present the development and evaluation of dETECT (Evaluating TEaching CompuTing), a model for the evaluation of the quality of instructional units for teaching computing in middle school based on the students' perception collected through a measurement instrument. The dETECT model was systematically developed and evaluated based on data collected from 16 case studies in 13 different middle school institutions with responses from 477 students. Our results indicate that the dETECT model is acceptable in terms of reliability (Cronbach's alpha a = 0.787) and construct validity, demonstrating an acceptable degree of correlation found between almost all items of the dETECT measurement instrument. These results allow researchers and instructors to rely on the dETECT model in order to evaluate instructional units and, thus, contribute to their improvement and to direct an effective and efficient adoption of teaching computing in middle school.