In computer science education at school, computational thinking has been an emerging topic over the last decade. Even though, computational thinking is interpreted and integrated in classrooms in different ways, an identification process about what computational thinking is about has been in progress among computer science school-teachers and computer science education researchers since Wing's initial paper on the characteristics of computational thinking. On the other hand, the constructionist learning theory by Papert, based on constructivism and Piaget, has a long tradition in computer science education for describing the students' learning process by hands-on activities. Our contribution, in this paper, is to present a new mapping tool which can be used to review classroom activities in terms of both computational thinking and constructionist learning. For the tool, we have reused existing definitions of computer science concepts and computational thinking concepts and combined these with our new constructionism matrix. The matrix's most notable feature is its scale of learners' autonomy. This scale represents the degree of choices learners have at each stage of development of their artefact. To develop the scale definitions, we trialed the mapping tool, coding twenty-one popular international computing activities for pupils aged 5 to 11 (K-5). From our trial, we have shown that we can use the mapping tool, with a moderate to high degree of reliability across coders, to analyse classroom activities with regard to computational thinking and constructionism, however, further validation is needed to establish its usefulness. Despite a small number of activities (n = 21) being analysed with our mapping tool, our preliminary results showed several interesting findings. Firstly, that learner autonomy was low for defining the problem and developing their own design. Secondly that the activity type (such as lesson plan rather than online activity) or artefact created (such as physical artefact rather than onscreen activity or unplugged activity), rather than the computational thinking or computer science concept being taught was related to learner autonomy. This provides some tentative evidence, which may seem obvious, that the learning context rather than the learning content is related to degree of constructionism of an activity and that computational thinking per se may not be related to constructionism. However, further work is needed on a larger number of activities to verify and validate this suggestion.
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.
During the last decade, coding has come to the foreground of educational trends as a strong mean for developing students' Computational Thinking (or CT). However, there is still limited research that looks at coding and Computational Thinking activities through the lens of constructionism. In this paper, we discuss how the knowledge we already have from other thinking paradigms and pedagogical theories, such as constructionism and mathematical thinking, can inform new integrated designs for the cultivation of Computational Thinking. In this context, we explore students' engagement with MaLT (Machine Lab Turtle-sphere), an online environment of our design that integrates Logo textual programming with the affordances of dynamic manipulation, 3D graphics and camera navigation. We also present a study on how the integration of the above affordances can promote constructionist learning and lead to the development of CT skills along with the generation of meanings about programming concepts.
The European Commission Science Hub has been promoting Computational Thinking (CT) as an important 21st century skill or competence. However, "despite the high interest in developing computational thinking among schoolchildren and the large public and private investment in CT initiatives, there are a number of issues and challenges for the integration of CT in the school curricula". On the other hand, the Digital Competence (DC) Framework 2.0 (DigCom) is promoted in the same European Commission Science Hub portal. It shows that both topics have many things in common. Thus, there is the need of research on the relationship between CT and digital competence.
The goal of this paper is to analyse and discuss the relationship between DC and CT, and to help educators as well as educational policy makers to make informed decisions about how CT and DC can be included in their local institutions. We begin by defining DC and CT and then discuss the current state of both phenomena in education in multiple countries in Europe. By analysing official documents, we try to find the underlying commonness in both DC and CT, and discover all possible connections between them. Possible interconnections between the component groups of approaches are presented in Fig.
Coding and computational thinking have recently become compulsory skills in many school systems globally. Teaching these new skills presents a challenge for many teachers. A notable example of professional development designed using Constructionist principles to address this challenge is ScratchEd. Upon reflecting on her experiences designing and running ScratchEd, Karen Brennan identified five tensions faced by professional development providers, and proposed that these tensions could be used for scrutinising and critiquing professional development. In this paper we analyse, through the lens of Brennan's tensions, the process we have followed to design, evaluate and improve professional development. We argue that while we have experienced the same tensions, the extent to which we assess learning is a new tension that extends those identified by Brennan. There are strong reasons to assess teachers' knowledge, however, quantitative measures of learning could be at odds with Constructionism: as Papert argued in Mindstorms, constructionist educators should study their learning environments as anthropologists. Consequently, we have called this new tension the tension between anthropology and assessment.
Computational thinking is becoming common in K-12 curricula, and at the same time there is interest in how STEM subjects can be integrated with the Arts (referred to as STEAM). There are some obvious connections between music and computation, but the idea of engaging with genuine computational thinking while also having authentic music learning experiences for students provides new opportunities.
In this paper we consider ways to explore computational thinking ideas such as decomposition, patterns, abstraction and algorithms in a meaningful way while also exploring key concepts that a music educator would expect to work with. We review some existing ideas for doing this, and also provide novel approaches that connect computational thinking and music. This is done through a series of vignettes that describe creative ways to connect the two subjects using approaches that have been used successfully with school students.
The first approach is based on the use of comparisons in sorting, which can be used to have students physically compare musical elements such as note pitches. The second uses simple programming on physical devices to represent music notation. Further examples include exploring binary representations using sound, writing programs for musical scales, understanding musical phrases in the context of programming, and using programming for music composition.
Integrating the opportunity to learn about computational thinking and music at the same time has the benefit that some efficiency can be gained in teaching, but more importantly, students are able to experience the relevance of these two subjects to each other, when they might otherwise pigeonhole them into separate areas of their lives.
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.
The Computer Science Unplugged activities and project has been an influential STEM (Science, Technology, Engineering & Mathematics) initiative, providing enrichment and teaching activities supporting computational thinking. Many of its activities are suitable for children. One of the most popular Unplugged activities is "Kid Krypto", invented by Mike Fellows and Neal Koblitz. Kid Krypto demonstrates the mathematics underlying public-key cryptography without using advanced mathematics. The paper gives an example of a Kid Krypto-style encryption system that is based on disjoint cycles in a graph or network and which is accessible to a very young audience. Also described is the original Kid Krypto system which is based on a version of dominating set called perfect code. The paper urges research scientists to participate in mathematical sciences communication and outreach.
Despite a growing effort to implement computational thinking (CT) skills in primary schools, little research is reported about what CT skills to teach at what age. Therefore, the research questions that guide this study read: (1) How is age related to students' success in computational thinking tasks? (2) How are computational thinking tasks perceived by students? (3) How do students' experience learning with respect to computational thinking? 200 primary school students between the age of 6 and 12 participated in this study. These students got introduced to two CT subjects: abstraction and decomposition. We found that age seems to be related with these concepts, with an interaction effect for gender in the abstraction task. No differences found between young and older students in the constructs perceived difficulty, cognitive load, and flow indicate that young primary school students can engage in learning these CT skills.
Although there is no universal agreement that students should learn programming, many countries have reached a consensus on the need to expose K-12 students to Computational Thinking (CT). When, what and how to teach CT in schools are open questions and we attempt to address them by examining how well students around the world solved problems in recent Bebras challenges. We collected and analyzed performance data on Bebras tasks from 115,400 students in grades 3-12 in seven countries. Our study provides further insight into a range of questions addressed in smaller-scale inquiries, in particular about the possible impact of schools systems and gender on students' success rate.
In addition to analyzing performance data of a large population, we have classified the considered tasks in terms of CT categories, which should account for the learning implications of the challenge. Algorithms and data representation dominate the challenge, accounting for 75-90% of the tasks, while other categories such as abstraction, parallelization and problem decomposition are sometimes represented by one or two questions at various age groups. This classification can be a starting point for using online Bebras tasks to support the effective learning of CT concepts in the classroom.