Introducing Computational Thinking in K-12 Education: Historical, Epistemological, Pedagogical, Cognitive, and Affective Aspects

Introduction of scientific and cultural aspects of Computer Science (CS) (called "Computational Thinking" - CT) in K-12 education is fundamental. We focus on three crucial areas. 1. Historical, philosophical, and pedagogical aspects. What are the big ideas of CS we must teach? What are the historical and pedagogical contexts in which CT emerged, and why are relevant? What is the relationship between learning theories (e.g., constructivism) and teaching approaches (e.g., plugged and unplugged)? 2. Cognitive aspects. What is the sentiment of generalist teachers not trained to teach CS? What misconceptions do they hold about concepts like CT and "coding"? 3. Affective and motivational aspects. What is the impact of personal beliefs about intelligence (mindset) and about CS ability? What the role of teaching approaches? This research has been conducted both through historical and philosophical argumentation, and through quantitative and qualitative studies (both on nationwide samples and small significant ones), in particular through the lens of (often exaggerated) claims about transfer from CS to other skills. Four important claims are substantiated. 1. CS should be introduced in K-12 as a tool to understand and act in our digital world, and to use the power of computation for meaningful learning. CT is the conceptual sediment of that learning. We designed a curriculum proposal in this direction. 2. The expressions CT (useful to distantiate from digital literacy) and "coding" can cause misconceptions among teachers, who focus mainly on transfer to general thinking skills. Both disciplinary and pedagogical teacher training is hence needed. 3. Some plugged and unplugged teaching tools have intrinsic constructivist characteristics that can facilitate CS learning, as shown with proposed activities. 4. Growth mindset is not automatically fostered by CS, while not studying CS can foster fixed beliefs. Growth mindset can be fostered by creative computing, leveraging on its constructivist aspects

Informatical Thinking

International audienceIn this paper, we reviewed many definitions of computational thinking, finding they share a lot of common elements, of very different nature. We classified them in mental processes, methods, practices, and transversal skills. Many of these elements seem to be shared with other disciplines and resonate with the current narrative on the importance of 21st-century skills. Our classification helps on shedding light on the misconceptions related to each of the four categories, showing that, not to dilute the concept, elements of computational thinking should be intended inside the discipline of Informatics, being its "disciplinary way of thinking"

Growth Mindset in Computational Thinking Teaching and Teacher Training

International audienceTeacher training in computational thinking is becoming more and more important, as many countries are introducing it at all K-12 school levels. Introductory programming courses are known to be difficult and some studies suggest they foster a fixed-mindset views of intelligence, reinforcing the idea that only some people have the so called "geek gene". This is particularly dangerous if thought by future school teachers. Interventions to stimulate "CS growth mindset" in students and their teachers are fundamental and worth CS education research

Castle and Stairs to Learn Iteration: Co-Designing a UMC Learning Module with Teachers

This experience report presents a participatory process that involved primary school teachers and computer science education researchers. The objective of the process was to co-design a learning module to teach iteration to second graders using a visual programming environment and based on the Use-Modify-Create methodology. The co-designed learning module was piloted with three second-grade classes. We experienced that sharing and reconciling the different perspectives of researchers and teachers was doubly effective. On the one hand, it improved the quality of the resulting learning module; on the other hand, it constituted a very significant professional development opportunity for both teachers and researchers. We describe the co-designed learning module, discuss the most significant hinges in the process that led to such a product, and reflect on the lessons learned

Davinci Goes to Bebras: A Study on the Problem Solving Ability of GPT-3

In this paper we study the problem-solving ability of the Large Language Model known as GPT-3 (codename DaVinci), by considering its performance in solving tasks proposed in the “Bebras International Challenge on Informatics and Computational Thinking”. In our experiment, GPT-3 was able to answer with a majority of correct answers about one third of the Bebras tasks we submitted to it. The linguistic fluency of GPT-3 is impressive and, at a first reading, its explanations sound coherent, on-topic and authoritative; however the answers it produced are in fact erratic and the explanations often questionable or plainly wrong. The tasks in which the system performs better are those that describe a procedure, asking to execute it on a specific instance of the problem. Tasks solvable with simple, one-step deductive reasoning are more likely to obtain better answers and explanations. Synthesis tasks, or tasks that require a more complex logical consistency get the most incorrect answers

Constructing Computational Thinking Without Using Computers

International audiencePaper type: application.Background(s):computer science; educational research.Approach:Our approach is very practical: we are focusedon pedagogy and improved classroom practices –what Matthews (1997:8) calls “pedagogical constructivism.”Moreover, we discuss the relationships between our work and Papert’s constructionism.Context: The meaning and implications of “computational thinking” (CT) are only now starting to be clarified, and the applications of the Computer Science (CS) Unplugged approach are becoming clearer as research is appearing. Now is a good time to consider how these relate, and what the opportunities and issues are for teachers using this approach.Problem: The goal here is to connect computational thinking explicitly to the CS Unplugged pedagogical approach, and to identify the context where Unplugged can be used effectively. Method: We take a theoretical approach, selecting a representative sample of CS Unplugged activities and mapping them to CT concepts. Results: The CS Unplugged activities map well onto commonly accepted CT concepts, although caution must be taken not to regard CS Unplugged as being a complete approach to CT education. Implications: There is evidence that CS Unplugged activities have a useful role to help students and teachers engage with CT, and to support hands-on activities with digital devices.Constructivist content: A constructivist approach to teaching computer science concepts can be particularly valuable at present because the public (and many teachers who are likely to have to become engaged with the subject) do not see CS as something they are likely to understand. Providing a clear way for anyone to construct this knowledge for themselves gives an opportunity to empower them when it might otherwise have been regarded as a domain that is open to only a select few

Authors’ Response: Keeping the “Computation” in “Computational Thinking” Through Unplugged Activities

International audienceThe commentaries provide useful questions and responses that help us understand better how unplugged activities serve as scaffolding to engage students in computer science. They help us to consider how activities relate to computational thinking, particularly by connecting the scaffolding in the activities to the limits of computation. This in turn helps us to navigate the somewhat disputed boundary between activities that clearly use computation as it occurs on physical devices, and metaphors that could potentially be misleading

Computational Thinking, Between Papert and Wing

International audienceThe pervasiveness of Computer Science (CS) in today’s digital society and the extensive use of computational methods in other sciences call for its introduction in the school curriculum. Hence, Computer Science Education is becoming more and more relevant. In CS K-12 education, computational thinking (CT) is one of the abused buzzwords: different stakeholders (media, educators, politicians) give it different meanings, some more oriented to CS, others more linked to its interdisciplinary value. The expression was introduced by two leading researchers, Jeannette Wing (in 2006) and Seymour Papert (much early, in 1980), each of them stressing different aspects of a common theme. This paper will use a historical approach to review, discuss, and put in context these first two educational and epistemological approaches to CT. We will relate them to today’s context and evaluate what aspects are still relevant for CS K-12 education. Of the two, particular interest is devoted to “Papert’s CT,” which is the lesser-known and the lesser-studied. We will conclude that “Wing’s CT” and “Papert’s CT,” when correctly understood, are both relevant to today’s computer science education. From Wing, we should retain computer science’s centrality, CT being the (scientific and cultural) substratum of the technical competencies. Under this interpretation, CT is a lens and a set of categories for understanding the algorithmic fabric of today’s world. From Papert, we should retain the constructionist idea that only a social and affective involvement of students into the technical content will make programming an interdisciplinary tool for learning (also) other disciplines. We will also discuss the often quoted (and often unverified) claim that CT automatically “transfers” to other broad 21st century skills. Our analysis will be relevant for educators and scholars to recognize and avoid misconceptions and build on the two core roots of CT

Single-Commodity Robust Network Design with Finite and Hose Demand Sets

We study a single-commodity Robust Network Design problem (sRND) defined on an undirected graph. Our goal is to determine minimum cost capacities such that any traffic demand from a given uncertainty set can be satisfied by a feasible single-commodity flow. We consider two ways of representing the uncertainty set, either as a finite list of scenarios or as a polytope. We propose a branch-and- cut algorithm to derive optimal solutions to sRND, built on a capacity-based integer linear programming formulation. It is strenghtened with valid inequalities derived as {0,1/2 }-Chvátal-Gomory cuts. Since the formulation contains exponentially many constraints, we provide practical separation algorithms. Extensive computational experiments show that our approach is effective, in comparison to existing approaches from the literature as well as to solving a flow based formulation by a general purpose solver