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.
Machine Learning (ML) is becoming increasingly present in our lives. Thus, it is important to introduce ML already in High School, enabling young people to become conscious users and creators of intelligent solutions. Yet, as typically ML is taught only in higher education, there is still a lack of knowledge on how to properly teach younger students. Therefore, in this systematic literature review, we analyze findings on teaching ML in High School with regard to content, pedagogical strategy, and technology. Results show that High School students were able to understand and apply basic ML concepts, algorithms and tasks. Pedagogical strategies focusing on active problem/project-based hands-on approaches were successful in engaging students and demonstrated positive learning effects. Visual as well as text-based programming environments supported students to build ML models in an effective way. Yet, the review also identified the need for more rigorous evaluations on how to teach ML.
In today’s society, creativity plays a key role, emphasizing the importance of its development in K-12 education. Computing education may be an alternative for students to extend their creativity by solving problems and creating computational artifacts. Yet, there is little systematic evidence available to support this claim, also due to the lack of assessment models. This article presents SCORE, a model for the assessment of creativity in the context of computing education in K-12. Based on a mapping study, the model and a self-assessment questionnaire are systematically developed. The evaluation, based on 76 responses from K-12 students, indicates a high internal reliability (Cronbach’s alpha = 0.961) and confirmed the validity of the instrument suggesting only the exclusion of 3 items that do not seem to be measuring the concept. As such, the model represents a first step aiming at the systematic improvement of teaching creativity as part of computing education.
Although Machine Learning (ML) has already become part of our daily lives, few are familiar with this technology. Thus, in order to help students to understand ML, its potential, and limitations and to empower them to become creators of intelligent solutions, diverse courses for teaching ML in K-12 have emerged. Yet, a question less considered is how to assess the learning of ML. Therefore, we performed a systematic mapping identifying 27 instructional units, which also present a quantitative assessment of the students’ learning. The simplest assessments range from quizzes to performance-based assessments assessing the learning of basic ML concepts, approaches, and in some cases ethical issues and the impact of ML on lower cognitive levels. Feedback is mostly limited to the indication of the correctness of the answers and only a few assessments are automated. These results indicate a need for more rigorous and comprehensive research in this area.
Creativity has emerged as an important 21st-century competency. Although it is traditionally associated with arts and literature, it can also be developed as part of computing education. Therefore, this article -presents a systematic mapping of approaches for assessing creativity based on the analysis of computer programs created by the students. As result, only ten approaches reported in eleven articles have been encountered. These reveal the absence of a commonly accepted definition of product creativity customized to computer education, confirming only originality as one of the well-established characteristics. Several approaches seem to lack clearly defined criteria for effective, efficient and useful creativity assessment. Diverse techniques are used including rubrics, mathematical models and machine learning, supporting manual and automated approaches. Few performed a comprehensive evaluation of the proposed approach regarding their reliability and validity. These results can help instructors to choose and adopt assessment approaches and guide researchers by pointing out shortcomings.
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.
Teaching computational thinking in K-12 as a 21th century skill is becoming increasingly important. Computational thinking describes a specific way of reasoning building on concepts and processes derived from algorithms and programming. One way to teach these concepts is games as an effective and efficient alternative. This article presents SplashCode, a low-cost board game to reinforce basic algorithms and programming concepts. The game was developed in a systematic way following an instructional design process, and applied and evaluated in a Brazilian public school with a total of 65 students (grade 5 to 9). First results indicate that the game can have a positive impact on motivation, learning experience, and students' learning, as well as contribute positively to social interaction, relevance, and fun. Results of this study may assist in the selection of games as an instructional strategy and/or in the development of new games for teaching computational thinking.
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.
Diverse initiatives have emerged to popularize the teaching of computing in K-12 mainly through programming. This, however, may not cover other important core computing competencies, such as Software Engineering (SE). Thus, in order to obtain an overview of the state of the art and practice of teaching SE competences in K-12, we carried out a systematic mapping study. We identified 17 instructional units mostly adopting the waterfall model or agile methodologies focusing on the main phases of the software process. However, there seems to be a lack of details hindering large-scope adoption of these instructional units. Many articles also do not report how the units have been developed and/or evaluated. However, results demonstrating both the viability and the positive contribution of initiating SE education already in K-12, indicate a need for further research in order to improve computing education in schools contributing to the popularization of SE competencies.
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.