computational thinkingcomputational thinking

BiblioMap

Diese Grafik fensterfüllend anzeigen als Pixelgrafik (PNG) Vektorgrafik (SVG)

Definitionen

Computational thinking is loosely defined as the habits of mind developed from designing programs, software packages, and computations performed by machines.

Von Peter Denning im Text Remaining Trouble Spots with Computational Thinking (2017)

Computational Thinking (CT) is a thought process (or a human thinking skill) that uses analytic and algorithmic approaches to formulate, analyse and solve problems. I

Von Stefania Bocconi, Augusto Chioccariello, Giuliana Dettori, Anusca Ferrari, Katja Engelhardt im Buch Developing Computational Thinking in Compulsory Education (2016)

This term has been defined in many ways and encompasses a broad and somewhat debated range of analytic and problem-solving skills, dispositions, habits, and approaches used in computer science

Von Marina Umaschi Bers, Louise P. Flannery, lizabeth R. Kazakoff im Text Computational thinking and tinkering (2014)

Dies meint die Fähigkeit, Probleme und Prozesse so zu formulieren, dass sie sich mithilfe von Computertechnologien lösen bzw. Bearbeiten lassen. Dies umfasst einerseits die Auswahl und Nutzung und andererseits auch die Programmierung geeigneter Software.

Von Dominik Petko im Text Die Schule der Zukunft und der Sprung ins digitale Zeitalter (2017)

Computational thinking is thinking in terms of prevention, protection, and recovery from worst-case scenarios through redundancy, damage containment, and error correction. It is calling gridlock deadlock and contracts interfaces. It is learning to avoid race conditions when synchronizing meetings with one another.

Von Jeannette M. Wing im Text Computational Thinking (2006)

we have created the following working definition of CT: The conceptual foundation required to solve problems effectively and efficiently (i.e., algorithmically, with or without the assistance of computers) with solutions that are reusable in different contexts. This definition highlights that CT is primarily a way of thinking and acting, which can be exhibited through the use particular skills, which then can become the basis for performance-based assessments of CT skills.

Von Valerie J. Shute, Chen Sun, Jodi Asbell-Clarke im Text Demystifying computational thinking (2017)

Computational thinking is thinking recursively. It is parallel processing. It is interpreting code as data and data as code. It is type checking as the generalization of dimensional analysis. It is recognizing both the virtues and the dangers of aliasing, or giving someone or something more than one name. It is recognizing both the cost and power of indirect addressing and procedure call. It is judging a program not just for correctness and efficiency but for aesthetics, and a system’s design for simplicity and elegance.

Von Jeannette M. Wing im Text Computational Thinking (2006)

Very briefly, the key points of Computational Thinking are that

it is a way of solving problems and designing systems that draws on concepts fundamental to computer science
it means creating and making use of different levels of abstraction, to understand and solve problems more effectively;
it means thinking algorithmically and with the ability to apply mathematical concepts to develop more efficient, fair, and secure solutions; and
it means understanding the consequences of scale, not only for reasons of efficiency but also for economic and social reasons.

Von James J. Lu, George H. L. Fletcher im Text Thinking about computational thinking (2009)

Computational thinking is using abstraction and decomposition when attacking a large complex task or designing a large complex system. It is separation of concerns. It is choosing an appropriate representation for a problem or modeling the relevant aspects of a problem to make it tractable. It is using invariants to describe a system’s behavior succinctly and declaratively. It is having the confidence we can safely use, modify, and influence a large complex system without understanding its every detail. It is modularizing something in anticipation of multiple users or prefetching and caching in anticipation of future use.

Von Jeannette M. Wing im Text Computational Thinking (2006)

Papert and Harel (1991) were the first who coined the term “computational thinking” in their 1991 paper on constructionism. They proposed that computational thinking was a shift on students’ thinking by contributing to their mental growth and become producers of knowledge using computing. Computational thinking received broader recognition from Wing (2006) who suggested that computational thinking was a critical twenty-first-century skill comparable to reading or math. Wing described computational thinking as “the thought processes involved in formulating a problem and expressing its solution in a way that a computer—human or machine—can effectively carry out” (p. 33).

Von Olgun Sadik, Anne-Ottenbreit Leftwich, Hamid Nadiruzzaman im Buch Emerging Research, Practice, and Policy on Computational Thinking (2017) im Text Computational Thinking Conceptions and Misconceptions auf Seite 222

Ever since Jeannette Wing (2006) coined the term “computational thinking,” there has been a debate over providing a single definition. Despite the lack of a consistent definition, within the Computer Science community, a common understanding of the term emerged related to abstraction as a means for solving problems. According to P.J. Denning (2009), computational thinking is just a new term for a core concept, algorithmic thinking. Computer Science is not just about programming, but an entire way of thinking. The Computer Science community believes that learning to think in this way, to think computationally, could promote new perspectives and solutions to problems in many other disciplines (Wenger, 1998).

Von Andri Ioannidou, Vicki E. Bennett, Alexander Repenning, Kyu Han Koh, Ashok R. Basawapatna im Text Computational Thinking Patterns (2011)

Computational Thinking (vgl. GAS 2014) betont den Stellenwert des Nachdenkens und Analysierens von Problemen und Problemlösungsstrategien, die der anschließenden Umsetzung mit ei nem Computer vorausgehen. Hierzu gehören die Anwendung verschiedener In der Informatik zentraler Konzepte wie Logik (analysieren und Voraussagen treffen), Abstraktion (Unwichtiges weglassen), Dekomposition (Komplexität in Teilprobleme zergliedern) und Algorithmisieren (Prozesse automatisieren und nachvollziehen) sowie Arbeitsweisen, die in der Informatik und bei der Nutzung und Gestaltung digitaler Medien gefördert werden. Hierzu zählen Kreativität (Gestalten und Umsetzen von Ideen), Debuggen (Fehler finden und korrigieren). Durchhalten (Probleme meistern lernen) und Kollaboration (zusammenarbeiten).

Von Ralf Romeike im Buch Software takes command im Text Wie informatische Bildung hilft, die digitale Gesellschaft zu verstehen und mitzugestalten (2017)

Wing’s original paper did not offer a succinct definition for computational thinking, but offered many examples of how computer scientists tackle common problems: “When your daughter goes to school in the morning, she puts in her backpack the things she needs for the day; that’s prefetching and caching. When your son loses his mittens, you suggest he retrace his steps; that’s back-tracking. [...] Which line do you stand in at the supermarket?; that’s performance modeling for multi-server systems.” [12]. Extrapolating from these examples, the overall message is that computer scientists have a toolbox of methods for matching problem situations to standard types of solution, drawn from various parts of the computer science curriculum, and, perhaps just as importantly, a standard terminology to describe these abstract problemsolution patterns.

Von Steve Easterbrook im Text From Computational Thinking to Systems Thinking (2014)

Bemerkungen

Endlich wird in vielen Fächern durch das Lösen von Problemen ("problem solving approach") das informatische Denken gefördert.

Von Raymond Morel, Pierre Banderet, Fritz Egger, Erich Hui, André Jaquenod, René Jeanneret, Jürg Nievergelt, Edo Poglia, Marcel Sutter in der Broschüre Die Einführung der Informatik an den Mittelschulen (1978) auf Seite 19

CT is not about getting humans to think like computers, but rather about developing the full set of mental tools necessary to effectively use computing to solve complex human problems [8].

Von James J. Lu, George H. L. Fletcher im Text Thinking about computational thinking (2009)

Ubiquitous computing is to today as computational thinking is to tomorrow. Ubiquitous computing was yesterday’s dream that became today’s reality; computational thinking is tomorrow’s reality.

Von Jeannette M. Wing im Text Computational Thinking (2006)

Just as the printing press facilitated the spread of the three Rs, what is appropriately incestuous about this vision is that computing and computers facilitate the spread of computational thinking.

Von Jeannette M. Wing im Text Computational Thinking (2006)

Computational thinking is a fundamental skill for everyone, not just for computer scientists. To reading, writing, and arithmetic, we should add computational thinking to every child’s analytical ability.

Von Jeannette M. Wing im Text Computational Thinking (2006)

Computational Thinking ist ein Ansatz, der bewusst auf Konzepte und Problemlösungsstrategien allgemeiner Relevanz fokussiert. Es geht unter anderem darum, den Zusammenhang zwischen sequenziellen und parallelen Prozessen zu verstehen.

Von Alexander Repenning im Text Computational Thinking in der Lehrerbildung (2015) auf Seite 4

Computational thinking involves solving problems, designing systems, and understanding human behavior, by drawing on the concepts fundamental to computer science. Computational thinking includes a range of mental tools that reflect the breadth of the field of computer science.

Von Jeannette M. Wing im Text Computational Thinking (2006)

Computational thinking confronts the riddle of machine intelligence: What can humans do better than computers? and What can computers do better than humans? Most fundamentally it addresses the question: What is computable? Today, we know only parts of the answers to such questions.

Von Jeannette M. Wing im Text Computational Thinking (2006)

Computational thinking will have become ingrained in everyone’s lives when words like algorithm and precondition are part of everyone’s vocabulary vocabulary; when nondeterminism and garbage collection take on the meanings used by computer scientists; and when trees are drawn upside down.

Von Jeannette M. Wing im Text Computational Thinking (2006)

Dieses Computerdenken kann man lernen, ohne den konkreten Programmcode zu kennen, sagt auch Ulrich Kortenkamp, der an der Universität Potsdam Didaktik der Informatik lehrt. Er ergänzt aber, dass es viel einfacher und schöner sei, wenn man auch programmieren darf. "Das ist die naheliegende praktische Umsetzung, die man sich nicht nehmen lassen sollte."

Von Christoph Drösser im Text Kids & Codes (2017)

Computational Thinking ist ein Denken mit und in numerischen Modellen. Im Altertum wurden Theorien aufgestellt – Astronomie mit Kugeln auf Kreisen –, in der frühen Neuzeit wurde gemessen und experimentiert – Kugeln auf Ellipsen, Bahnen mit Abweichungen –, und heute wird zusätzlich numerisch modelliert. Ohne Computer wäre schon der Mondflug unmöglich gewesen.

Von Carl August Zehnder im Text «Die Informatik verändert, was unter Allgemeinbildung zu verstehen ist» (2013)

True, an algorithm is a series of steps—but the steps are not arbitrary, they must control some computational model. A step that requires human judgment has never been considered to be an algorithmic step. Let us correct our computational thinking guidelines to accurately reflect the definition of an algorithm. Otherwise, we will mis-educate our children on this most basic idea.

Von Peter Denning im Text Remaining Trouble Spots with Computational Thinking (2017)

Computational thinking provides an ontology of computational concepts, and a set of terms for talking about them. For example, procedural and data abstractions provide the building blocks of computational solutions, and sequential and parallel composition provide a way of putting them together. Hierarchical decomposition is used to reduce complex problems, and encapsulation is used to create re-usable solutions.

Von Steve Easterbrook im Text From Computational Thinking to Systems Thinking (2014)

If computational thinking is the central tool of computer scientists, then we ought to consider whether computational thinking becomes just another instance of Maslow’s Hammer [16]: “If all you have is a hammer, then everything looks like a nail”. In other words, computer professionals may attempt to solve all problems through algorithmic means, while failing to perceive those that cannot be expressed using the abstractions of CT.

Von Steve Easterbrook im Text From Computational Thinking to Systems Thinking (2014)

Because computation has invaded so many fields, and because people who do computational design in those fields have made many new discoveries, some have hypothesized that CT is the most fundamental kind of thinking, trumping all the others such as systems thinking, design thinking, logical thinking, scientific thinking, etc. This is computational chauvinism. There is no basis to claim that CT is more fundamental than other kinds of thinking.

Von Peter J. Denning, Matti Tedre, Pat Yongpradit im Text Misconceptions About Computer Science (2017)

Computational thinking is using heuristic reasoning to discover a solution. It is planning, learning, and scheduling in the presence of uncertainty. It is search, search, and more search, resulting in a list of Web pages, a strategy for winning a game, or a counterexample. Computational thinking is using massive amounts of data to speed up computation. It is making trade-offs between time and space and between processing power and storage capacity.

Von Jeannette M. Wing im Text Computational Thinking (2006)

Finally, it is worth noting that educators have long promoted a large number of different kinds of thinking: engineering thinking, science thinking, economics thinking, systems thinking, logical thinking, rational thinking, network thinking, ethical thinking, design thinking, critical thinking, and more. Each academic field claims its own way of thinking. What makes computational thinking better than the multitude of other kinds of thinking? I do not have an answer.

Von Peter Denning im Text Remaining Trouble Spots with Computational Thinking (2017)

Programming should not, however, be essential in the teaching of computational thinking, nor should knowledge of programming be necessary to proclaim literacy in basic computer science. Just as math students come to proofs after 12 or more years of experience with basic math, and English students come to literary analysis after an even longer period of reading and writing, programming should begin for all students only after they have had substantial practice thinking computationally.

Von James J. Lu, George H. L. Fletcher im Text Thinking about computational thinking (2009)

Encouraged by funding programs from the NSF, the US computer science community has readily adopted the term computational thinking, using it as a slogan to re-design existing computer science curricula to make them more attractive to students, and to develop new courses aimed at audiences who would not otherwise be exposed to computer science. For example, the Computer Science Teachers Association (CSTA) set up a task force to “explore and disseminate teaching and learning resources related to computational thinking”.

Von Steve Easterbrook im Text From Computational Thinking to Systems Thinking (2014)

Computational thinking (CT) stems back to the constructionist work of Seymour Papert (Papert, 1980, 1991) and was first coined as a term in a seminal article by Wing (2006). She explained that CT entails “solving problems, designing systems, and understanding human behavior, by drawing on the concepts fundamental to computer science” (Wing, 2006, p. 33). As such, it represents an ability to analyze and then solve various problems. Her arguments provided a fresh perspective on the relationship( s) between humans and computers, and gave rise to a wave of research on CT.

Von Valerie J. Shute, Chen Sun, Jodi Asbell-Clarke im Text Demystifying computational thinking (2017)

The computational thinker looks for problems that can be tackled with computers. Immediately, this provides a selective lens through which to view the world. Problems that are unlikely to have computational solutions (e.g. ethical dilemmas, value judgements, societal change, etc) are ignored. Others are reduced to a simpler, computational proxy. It is no coincidence that computer science students tend to be less morally mature than students from other disciplines [17]. Ethical dilemmas have no computational solutions, and so are overlooked when peering through a CT lens.

Von Steve Easterbrook im Text From Computational Thinking to Systems Thinking (2014)

Computational thinking (CT) is an old idea in CS, first discussed by pioneers such as Alan Perlis in the late 1950s.8 Perlis thought “algorithmizing” would become part of every field as computing moved in to automate processes. Dijkstra recognized he had learned new mental skills while programming (1974). In his 1980 book Mindstorms, Papert was the first to mention the term CT explicitly when discussing the mental skills children developed while programming in Logo. Jeannette Wing catalyzed a discussion about how people outside CS could benefit from learning computing.

Von Peter J. Denning, Matti Tedre, Pat Yongpradit im Text Misconceptions About Computer Science (2017)

At heart, CT is inherently reductionist. Computational problems are tackled by reducing them to a set of discrete variables that can be mapped onto abstract data types, and a set of algorithmic steps for manipulating these data types. In the process, multiple perspectives on the nature of the problem are lost, as is any local, contingent knowledge about the problem situation [18]. Computational thinking thus ignores the fact that any particular expression of the “the problem to be solved” is the result of an ongoing negotiation between the competing needs of a variety of stakeholders [19], [20].

Von Steve Easterbrook im Text From Computational Thinking to Systems Thinking (2014)

Wing (2006) argued that CT does not mean to think like a computer; but rather to engage in five cognitive processes with the goal of solving problems efficiently and creatively. These include:

Problem reformulation - Reframe a problem into a solvable and familiar one.
Recursion - Construct a system incrementally based on preceding information.
Problem decomposition - Break the problem down into manageable units.
Abstraction - Model the core aspects of complex problems or systems.
Systematic testing - Take purposeful actions to derive solutions.

Von Valerie J. Shute, Chen Sun, Jodi Asbell-Clarke im Text Demystifying computational thinking (2017)

Although there are different efforts to define the term and there is no consensus on different definitions, there is a general acceptance that CT skills cover the concepts of “abstraction, algorithmic thinking, problem-solving, decomposition, generalization, and debugging” (Sarıtepeci & Durak, 2017). In support of this, Kalelioglu, Gülbahar and Kukul (2016) have formed a word cloud in relation to the explanations about computational thinking in their work and have found that the data words that are most used in terms of defining the process of computation thinking in the literature are “abstraction, problem, solving, algorithmic and thinking.

Von Hatice Yildiz Durak, Mustafa Saritepeci im Text Analysis of the relation between computational thinking skills and various variables with the structural equation model (2017)

Relevante Personen

Alexander
Repenning

Yasmin B.
Kafai

Häufig erwähnende Personen

Alexander
Repenning

Kenny A.
Hunt

David D.
Riley

Yasmin B.
Kafai

Quinn
Burke

Mark
Guzdial

Häufig co-zitierte Personen

Jeannette M.
Wing

Alexander
Repenning

Andri
Ioannidou

Kyu Han
Koh

Ashok R.
Basawapatna

Vicki E.
Bennett

Yasmin B.
Kafai

Hilarie
Nickerson

Seymour
Papert

Brian
Silverman

Evelyn
Eastmond

Jay
Silver

Amon
Millner

Eric
Rosenbaum

Caitlin
Kelleher

Karen
Brennan

Andrés
Monroy-Hernández

Randy
Pausch

Catharine
Brand

James
Ambach

Mitchel
Resnick

Natalie
Rusk

David
Webb

Ingolf
Ståhl

John
Maloney

Statistisches Begriffsnetz

Blahfasel-Generator (Beta)

Das Themenfeld computational thinking retrospektiv angegangen
Praktisch nie eine politische Talkshow, in der nicht das Themenfeld "computational thinking" beschrieben wird. Im folgenden wird mit dieser Überlegung versucht, das Spannungsfeld vertiefter zu formulieren.
Bereits 1978 wurde der Begriff ein erstes Mal diskutiert. Eine lange Zeit, in der viel geschehen ist. Allgemein gilt Alexander Repenning als häufig zitierter Experte für dieses Thema. Yasmin B. Kafai wird aber ebenfalls oft genannt.
Hilfreich ist ein Blick auf die zahlreichen Definitionen des Begriffs. Die älteste einigermassen bekannte Definition des Begriffs lautet: "Computational thinking is thinking recursively. It is parallel processing. It is interpreting code as data and data as code. It is type checking as the generalization of dimensional analysis. It is recognizing both the virtues and the dangers of aliasing, or giving someone or something more than one name. It is recognizing both the cost and power of indirect addressing and procedure call. It is judging a program not just for correctness and efficiency but for aesthetics, and a system’s design for simplicity and elegance." (Jeannette M. Wing, 2006). Verwandt klingt die Erklärung von Jeannette M. Wing (2006) : "Computational thinking is using abstraction and decomposition when attacking a large complex task or designing a large complex system. It is separation of concerns. It is choosing an appropriate representation for a problem or modeling the relevant aspects of a problem to make it tractable. It is using invariants to describe a system’s behavior succinctly and declaratively. It is having the confidence we can safely use, modify, and influence a large complex system without understanding its every detail. It is modularizing something in anticipation of multiple users or prefetching and caching in anticipation of future use.". Hier zeigen sich nun sowohl Unterschiede und Gemeinsamkeiten. ...