The development of computational thinking is a major topic in K-12 education. Many of these experiences focus on teaching programming using block-based languages. As part of these activities, it is important for students to receive feedback on their assignments. Yet, in practice it may be difficult to provide personalized, objective and consistent feedback. In this context, automatic assessment and grading has become important. While there exist diverse graders for text-based languages, support for block-based programming languages is still scarce. This article presents CodeMaster, a free web application that in a problem-based learning context allows to automatically assess and grade projects programmed with App Inventor and Snap!. It uses a rubric measuring computational thinking based on a static code analysis. Students can use the tool to get feedback to encourage them to improve their programming competencies. It can also be used by teachers for assessing whole classes easing their workload.

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As computing has become an integral part of our world, demand for teaching computational thinking in K-12 has increased. One of its basic competences is programming, often taught by learning activities without a predefined solution using block-based visual programming languages. Automatic assessment tools can support teachers with their assessment and grading as well as guide students throughout their learning process. Although being already widely used in higher education, it remains unclear if such approaches exist for K-12 computing education. Thus, in order to obtain an overview, we performed a systematic mapping study. We identified 14 approaches, focusing on the analysis of the code created by the students inferring computational thinking competencies related to algorithms and programming. However, an evident lack of consensus on the assessment criteria and instructional feedback indicates the need for further research to support a wide application of computing education in K-12 schools.

Resumo Apesar da computação estar presente em praticamente todos os setores da sociedade, ainda existe uma carência de conhecimento e interesse no aprendizado dessa área, em grande parte devido à ausência do ensino da computação no Ensino

Although Machine Learning (ML) is used already in our daily lives, few are familiar with the technology. This poses new challenges for students to understand ML, its potential, and limitations as well as to empower them to become creators of intelligent solutions. To effectively guide the learning of ML, this article proposes a scoring rubric for the performance-based assessment of the learning of concepts and practices regarding image classification with artificial neural networks in K-12. The assessment is based on the examination of student-created artifacts as a part of open-ended applications on the use stage of the Use-Modify-Create cycle. An initial evaluation of the scoring rubric through an expert panel demonstrates its internal consistency as well as its correctness and relevance. Providing a first step for the assessment of concepts on image recognition, the results may support the progress of learning ML by providing feedback to students and teachers.