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Even though countries from all over the world are modifying their national educational curriculum in order to include computational thinking skills, there is not an agreement in the definition of this ability. This is partly caused by the myriad of definitions that has been proposed by the scholar community. In fact, there are multiple examples in educational scenarios in which coding and even robotics are considered as synonymous of computational thinking. This paper presents a text network analysis of the main definitions of this skill that have been found in the literature, aiming to offer insights on the common characteristics they share and on their relationship with computer programming. As a result, a new definition of computational thinking is proposed, which emerge from the analysed data.
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Revista Interuniversitaria de Investigación en Tecnología Educativa (RIITE)
Nº 7 Diciembre 2019 pp. 26-35 ISSN: 2529-9638 DOI:
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Not the same: a text network analysis on computational
thinking definitions to study its relationship with computer
No es lo mismo: un análisis de red de texto sobre definiciones de
pensamiento computacional para estudiar su relación con la
programación informática
Jesús Moreno-León
Programamos (España)
Gregorio Robles
Universidad Rey Juan Carlos (España)
Marcos Román-González
Universidad Nacional de Educación a Distancia (UNED) (España)
Juan David Rodríguez García
Instituto Nacional de Tecnologías Educativas y de Formación del Profesorado (España)
Recibido: 26/09/2019
Aceptado: 9/12/2019
Publicado: 26/12/2019
Even though countries from all over the world are modifying their national educational curriculum in
order to include computational thinking skills, there is not an agreement in the definition of this ability. This
is partly caused by the myriad of definitions that has been proposed by the scholar community. In fact, there
are multiple examples in educational scenarios in which coding and even robotics are considered as
synonymous of computational thinking. This paper presents a text network analysis of the main definitions
of this skill that have been found in the literature, aiming to offer insights on the common characteristics they
share and on their relationship with computer programming. As a result, a new definition of computational
thinking is proposed, which emerge from the analysed data.
Computer Science Education; Programming; Text Structure
RIITE, Núm.7 (2019), 26-35 Not the same: a text network analysis on computational thinking
definitions to study its relationship with computer programming
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A pesar de que países de todo el mundo están modificando su plan de estudios nacional para incluir
habilidades de pensamiento computacional, no hay un acuerdo en la definición de esta capacidad. Esto se
debe en parte a la gran cantidad de definiciones propuestas por la comunidad académica. De hecho, hay
múltiples ejemplos en escenarios educativos en los que la programación e incluso la robótica se consideran
sinónimos del pensamiento computacional. Este artículo presenta un análisis de la red de texto de las
principales definiciones de esta habilidad que se han encontrado en la literatura, con el objetivo de ofrecer
información sobre las características comunes que comparten y sobre su relación con la programación
informática. Como resultado, se propone una nueva definición de pensamiento computacional que emerge
de los datos analizados.
Educación informática; Programación; Estructura de Texto
Moreno-León, J., Robles, G., Román-González, M. y Rodríguez, J.D. (2019). Not the same: a
text network analysis on computational thinking definitions to study its relationship with computer
programming. RIITE. Revista Interuniversitaria de Investigación en Tecnología Educativa, 7, 26-
35. Doi:
All over the world, governments have started to modify their national curriculum at both
primary and secondary educational levels to incorporate Computational Thinking (CT), since this
ability is considered a key set of problem-solving skills that must be developed by all learners
(Bocconi et al., 2016). Still, there seems to be a lack of consensus on a formal definition of CT
(Grover, 2015; Kalelioglu, Gülbahar, & Kukul, 2016; Román-González, Moreno-Leon & Robles,
2017) and, consequently, a myriad of CT definitions has been proposed in the last few years.
This diversity of theoretical approaches to CT, and the resulting lack of standardization, is
problematic from the educational point of view. This is evidenced by the fact that in many
educational contexts CT and programming (or coding) are used almost as synonymous
(Balanskat & Engelhardt, 2015). However, what is the relationship between programming and
CT, based on the definitions of the latter? Does programming arise as a fundamental core of CT?
And what about the relationship between CT and robotics?
In addition, how different are the definitions of CT proposed during the last years? Do they
share some common characteristics? Or are they focused on distinct dimensions of this
In order to address these questions, we have collected the main CT definitions published in
the literature, which are presented in Section 2. We have studied these definitions using a text
network analysis (Paranyushkin, 2011), which is described in Section 3. Section 4 summarizes
the main results, with a special focus on the most influential elements of the CT definitions, the
main themes or topics of words, and the structure of the discourse. Finally, in Section 6 we discuss
these findings and their implications, and conclude the paper with a new “data-driven” definition
of CT.
Principales aportaciones del artículo y futuras líneas de investigación:
Las diferentes definiciones del concepto de pensamiento computacional coinciden en los elementos
Es posible aunar nodos comunes que evidencian que la dispersión conceptual es solo aparente.
Jesús Moreno, Gregorio Robles, Marcos Román y Juan David Rodríguez RIITE, Núm. 7 (2019), 26-35
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In order to review the main definitions of CT that can be found in both academic and grey
literature, we have reused the literature review performed in the recently published doctoral
dissertation by one of the authors of this work (Moreno-León, 2018).
The first appearance of the term CT, although without elaboration, was in Seymour Papert’s
Mindstorms when discussing about the idea of creating samba schools for mathematics:
There have already been attempts in this direction by people engaged in computer hobbyist clubs and
in running computer drop-in centers. In most cases, although the experiments have been interesting
and exciting, they have failed to make it because they were too primitive. Their computers simply did
not have the power needed for the most engaging and shareable kinds of activities. Their visions of
how to integrate computational thinking into everyday life was insufficiently developed. But there will
be more tries, and more and more. And eventually, somewhere, all the pieces will come together and
it will catch. (Papert, 1980, p. 182).
But the term CT did not become popular until 2006, when Wing published her seminal paper
on CT with the following definition:
CT involves solving problems, designing systems, and understanding human behavior, by drawing on
the concepts fundamental to computer science. CT includes a range of mental tools that reflect the
breadth of the field of computer science [...]. It represents a universally applicable attitude and skill set
everyone, not just computer scientists, would be eager to learn and use. (Wing, 2006, p. 33).
The timing was more opportune at that moment, and the term quickly gained popularity and
raised the interest of both the scholar and educational communities. Since then, other influential
authors and organizations have proposed new definitions for CT from different perspectives.
One of these new, alternative definitions is provided by Lu and Fletcher, who defend that
being proficient in CT “helps us to systematically and efficiently process information and tasks”
(Lu & Fletcher, 2009, p. 261).
Aiming to support educators in the introduction of CT in K-12, the Computer Science
Teachers Association and the International Society for Technology in Education developed the
following operational definition of CT:
CT is a problem-solving process that includes (but is not limited to) the following characteristics:
formulating problems in a way that enables us to use a computer and other tools to help solve them;
logically organizing and analyzing data; representing data through abstractions such as models and
simulations; automating solutions through algorithmic thinking (a series of ordered steps); identifying,
analyzing, and implementing possible solutions with the goal of achieving the most efficient and
effective combination of steps and resources; and generalizing and transferring this problem-solving
process to a wide variety of problems. (ISTE & CSTA, 2011, p. 1).
The vision of CT proposed by Wing has also received criticism, though. Hence, Denning
argues that CT is equivalent to algorithmic thinking, a concept well known since the 1950s that
could be defined as “a mental orientation to formulating problems as conversions of some input
to an output and looking for algorithms to perform the conversions.” (Denning, 2009, p. 28).
Most of the complaints that the term received were in terms of ambiguity and vagueness. As
a result, in 2011 Wing proposed a new definition of CT aiming to clarify certain aspects of her
initial proposal: “CT is the thought processes involved in formulating problems and their solutions
so that the solutions are represented in a form that can be effectively carried out by an information-
processing agent. (Wing, 2011, p. 1).
A similar definition is introduced by Aho, who defines CT as the “thought processes involved
in formulating problems so their solutions can be represented as computational steps and
algorithms.” (Aho, 2011, p. 2).
RIITE, Núm.7 (2019), 26-35 Not the same: a text network analysis on computational thinking
definitions to study its relationship with computer programming
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Placing the focus on the educational community, Barr & Stephenson define CT as:
[…] an approach to solving problems in a way that can be implemented with a computer. Students
become not merely tool users but tool builders. They use a set of concepts, such as abstraction,
recursion, and iteration, to process and analyze data, and to create real and virtual artifacts. CT is a
problem-solving methodology that can be automated and transferred and applied across subjects.
(Barr & Stephenson, 2011, p. 49).
In a report advocating for computing education in UK schools, the British Royal Society goes
one step forward by highlighting the presence of computation in nature as another reason to teach
CT, which was defined as “the process of recognizing aspects of computation in the world that
surrounds us, and applying tools and techniques from Computer Science to understand and
reason about both natural and artificial systems and processes.” (Furber, 2012, p. 29).
Following this approach, in the context of an intervention to integrate CT with K-12 science
education, Sengupta et al. propose a theoretical framework where authors state that
CT draws on concepts and practices that are fundamental to computing and computer science. It
includes epistemic and representational practices, such as problem representation, abstraction,
decomposition, simulation, verification, and prediction. However, these practices are also central to the
development of expertise in scientific and mathematical disciplines. (Sengupta et al., 2013, p. 351).
Aiming to gather the elements that are accepted as comprising CT in most CT definitions in
educational environments, Grover & Pea review aforementioned definitions and propose the
following elements as the basis of curricula that aim to support CT learning and assessment:
[…] abstractions and pattern generalizations (including models and simulations); systematic
processing of information; symbol systems and representations; algorithmic notions of flow of control;
structured problem decomposition (modularizing); iterative, recursive, and parallel thinking; conditional
logic; efficiency and performance constraints; and debugging and systematic error detection. (Grover
& Pea, 2013, p. 39).
Also with the aim of supporting educators, Computing at School proposed a framework that
states that, when working in the classroom, CT involves both concepts (logic, algorithms,
decomposition, patterns, abstraction, and evaluation) and approaches (tinkering, creating,
debugging, persevering, and collaborating), thus pointing to some non-cognitive skills being part
of CT (Csizmadia et al., 2015).
As we can see, the computer science educational community has had difficulties in finding
a definition of CT that everyone agrees upon. This is a view shared by Mannila et al., who wrote
a report on the current status of the coverage of computer science in K-9 education in several
countries (Mannila et al., 2014). In this report, the authors define CT as a set of concepts and
thinking processes from computer science that help in formulating problems and their solutions
in different disciplines.
Besides multiple contributions to dene CT, we also nd authors and organizations that
modify their initial proposals over time. Hence, in the [Interim] CSTA K-12 Computer Science
we nd yet another denition of CT by the CSTA Standards Task Force:
We believe that CT is a problem-solving methodology that expands the realm of computer science into
all disciplines, providing a distinct means of analyzing and developing solutions to problems that can
be solved computationally. With its focus on abstraction, automation, and analysis, CT is a core
element of the broader discipline of computer science. (CSTA, 2016, p. 6)
More recently, two new definitions for CT have been published. Tedre & Denning describe
CT as “a popular phrase that refers to a collection of computational ideas and habits of mind that
people in computing disciplines acquire through their work in designing programs, software,
simulations, and computations performed by machinery.” (Tedre & Denning, 2016, p. 120). Lastly,
in a more informal approach, Wolfram states that CT:
Jesús Moreno, Gregorio Robles, Marcos Román y Juan David Rodríguez RIITE, Núm. 7 (2019), 26-35
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“intellectual core is about formulating things with enough clarity, and in a systematic enough way, that
one can tell a computer how to do them [...] CT is a broad story, because there are just a lot more
things that can be handled computationally [...] But how does one tell a computer anything? One has
to have a language” (Wolfram, 2016).
As a summary, Table 1 shows the reviewed publications that include a definition of CT
ordered by date of publication, and also shows if each contribution was published in a book,
journal, magazine, conference proceedings or official report, among others. As can be seen, a
majority of the proposals following Wings’ definition were published in computer science
environments, while since 2011 most of the definitions were proposed in scenarios closer to the
educational community.
Table 1. Reviewed publications that propose a CT denition
(Papert, 1980)
Book - Mindstorms
(Wing, 2006)
Magazine - Communications of the ACM
(Lu & Fletcher, 2009)
Newsletter SIGCSE Bulletin
(Denning, 2009)
Magazine - Communications of the ACM
(Wing, 2011)
Magazine The LINK
(Aho, 2011)
Symposioum Ubiquity
(Barr & Stephenson, 2011)
Journal Inroads
(ISTE & CSTA, 2011)
Report - ISTE & CSTA
(Furber, 2012)
Report - Royal Society
(Sengupta et al., 2013)
Journal Education and information technologies
(Grover & Pea, 2013)
Journal - Educational researcher
(Mannila et al., 2014)
Report Working group ITiCSE
(Csizmadia et al., 2015)
Report - CAS
CSTA K-12 CS Standards, 2016
Report - CSTA
(Tedre & Denning, 2016)
Proceedings - Koli calling
(Wolfram, 2016)
Opinion column
In order to detect the central concepts of CT that emerge from the myriad of CT definitions
that have been reviewed, a text network analysis (Paranyushkin, 2011) was performed on a
document containing all these definitions. Especifically, we used InfraNodus, which is an open-
source tool used by the academic community to perform text-related studies and to make sense
of pieces of disjointed textual data (Paranyushkin, 2019). The solution automates the visualization
of a text as a network; shows the most relevant topics, their relations, and the structural gaps
between them; and enables the analysis of the discourse structure and the assessment of its
diversity based on the community structure of the graph (Paranyushkin, 2019).
As a first step, the tool removes the syntax information (such as commas and dots) and
converts the words into their morphemes to reduce redundancy (Paranyushkin, 2019). For
instance, “computers” becomes “computer” or “programmed” becomes “program”. In addition, the
tool removes articles, conjunctions, auxiliary verbs and some other frequently used words, such
as ’is’ or ’the’. Thus, a sentence that reads “the process of recognizing aspects of computation in
the world that surrounds us” is turned into “process recognize aspect computation world
The resulting sequence is then converted by Infranodus into a directed network graph, where
the nodes are the different words while the edges represent their co-occurrences. The tool
RIITE, Núm.7 (2019), 26-35 Not the same: a text network analysis on computational thinking
definitions to study its relationship with computer programming
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identifies the nodes that appear most often on the shortest paths between any two randomly
chosen nodes in the network -i.e., betweenness centrality- and detects the groups of nodes that
tend to appear more often together -topical groups-. The result is a visual network representation
of the text that, based on colors and sizes, enables a clear vision of its structure and topics.
Finally, the tool also identifies the structure of the discourse, which can be categorized as
dispersed, diversified, focused or biased (Paranyushkin, 2019).
The text network analysis generates 148 nodes (words) and 658 edges (co-occurrences).
The average degree, which represents the number of nodes every node is connected to, is 4.45.
Figure 1 is a graph image that can be used to get a clear visual representation of the main
topics and influential keywords of the reviewed CT definitions. Colors in Figure 3.1 indicate the
distinct contextual clusters, or themes, which are communities of words that are closely related.
On the contrary, words that appear in different contexts are shown far away from each other. The
size of the nodes reflects their betweenness centrality, which is the number of different themes or
contexts each node connects.
Figure 1. Visual representation of the main topics and inuential keywords in CT denitions.
Jesús Moreno, Gregorio Robles, Marcos Román y Juan David Rodríguez RIITE, Núm. 7 (2019), 26-35
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As can be seen in Figure 1, the most influential elements of the network, since they link
different topics together, are “problem”, “computer”, “solution” and “process”. These nodes are
shown bigger on the graph.
Table 2 presents the main words within the most inuential contextual clusters. These words
are the nodes that have more connections within each group, being in consequence the most
inuential words of the themes. However, connections to the other clusters in the network are not
considered in this case. Color column in Table 2 refers to the colors in Figure 1.
Table 2. Most inuential communities of words in CT denitions.
Words in the context
computer, science, tool
problem, solve, solution
abstraction, simulation, decomposition
system, information, algorithmic
logic, debug, performance
1 Refers to the colors used in Figure 1
In terms of network structure, the analysis indicates that it is “focused” (modularity -which
measures how pronounced is the community structure- is 0.49, 18% of words are in the top topic
and its influence dispersal is 40%). This means that the most influential words are concentrated
around one topic and the discourse is focused on a certain perspective.
The results of the text network analysis show that neither programming nor coding emerge
among the most inuential words of the main CT definitions. Why are, then, CT and programming
considered almost synonymous in many contexts?
As discussed by Voogt et al.:
the concepts of CT and the practice of programming are difficult to delineate in the literature because
many CT studies or discussions of theory use programming as their context [...]. This can be confusing
to the reader and often lead to the impression that CT is the same as programming or at the very least
that CT requires the use of programming. (Voogt et al., 2015, p. 716)
So even though, as stated by our text network analysis, scholars do not claim that
programming must be the required context to develop CT skills, a vast majority of interventions
in which these skills are trained make use of different types of programming tasks (Kalelioglu et
al., 2016; Lye and Koh, 2014).
However, although programming makes CT concepts concrete and nowadays is therefore a
de facto method for the learning and teaching of these skills (Bocconi et al., 2016) this situation
might change in the near future due to several factors. On the one hand, educators and
researchers may find other strategies to develop CT skills, as it is already the case with the use
of unplugged activities (Brackmann et al., 2017). On the other hand, the intense development of
artificial intelligence solutions, especially those based on machine learning, may alter dramatically
the way computer programming is performed (Rodríguez-García, Moreno-León, Román-
González & Robles, 2019).
In other words, just like we distinguish between verbal aptitude -which is in the order of
human cognitive abilities, with an important innate base- and literacy skill -which is an instrumental
competence that requires a relatively formal teaching and learning process- we could similarly
establish a distinction between CT -human cognitive ability- and programming skills -instrumental
competence- (Román-González, Pérez-González & Jiménez-Fernández, 2017). However, if CT
is, first and foremost, a human cognitive ability, it is very striking that "cognition" or "cognitive
ability" do not appear as key terms of the analysis. Perhaps this is a result of the fact that there
RIITE, Núm.7 (2019), 26-35 Not the same: a text network analysis on computational thinking
definitions to study its relationship with computer programming
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are more definitions of CT proposed by computer scientists than by psychologists or pedagogues,
as shown in Table 1?
It is also worth noting that robotics, which is sometimes used in school scenarios as a context
to develop CT skills (Balanskat & Engelhardt, 2015) does not even appear in the 148 nodes of
the analysis.
As mentioned earlier, the analysis indicates that the network structure is “focused”. Such
discourse structure is characteristic “for newspaper articles, essays, reports, which are designed
to provide a clear and concise representation of a certain idea” (Paranyushkin, 2019). This result
is quite interesting, since one might expect that a list of definitions could have a more dispersed
or diversified structure. Consequently, this result shows that, even with some differences -since
the structure is not biased-, the definitions have lots of elements in common.
Finally, taking into account both the most influential elements and the communities of words
highlighted by the text network analysis, we could almost propose (yet) a new definition of CT.
Based on this data, CT would be the ability to formulate and represent problems to solve them by
making use of tools, concepts and practices from the computer science discipline, such as
abstraction, decomposition or the use of simulations. Such data-driven definition could be of
interest for the educational community, since it clarifies the relationship between CT and
programming (or robotics, for that matter), being the former a cognitive ability of the subject, and
the latter just one of the means to develop it.
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Jesús Moreno-León
Jesús Moreno-León is a researcher at Programamos, a non-profit organization promoting computational
thinking skills in education. He participates as an advisor in multiple international committees and expert
groups regarding the use of computer programming in education. As an example, since 2013 he has
collaborated with different roles -at this moment as a national ambassador- with EU Code Week, an initiative
promoted by the European Commission that reached over 2.7 million people during the last edition. His main
lines of research are related to the inclusion of computational thinking in schools, the assessment of the
development of this ability, and the evaluation of its educational impact.
Gregorio Robles
Universidad Rey Juan Carlos
Gregorio Robles is Associate Professor at the Universidad Rey Juan Carlos, in Madrid, Spain. He mainly
does research in following two fields: a) Software engineering: he is specialized in software analytics of
Free/Libre/Open Source Software systems. His primary focus is on mining software repositories, socio-
technical issues such as community metrics, software evolution, and development effort estimation. And b)
Computational thinking (CT): he investigates the effect of using coding as a way to help students learn
beyond coding. I also work on how the development of CT skills can be assessed.
Marcos Román-González
Universidad Nacional de Educación a Distancia (UNED)
Marcos Román-González is Associate Professor at the Department of Methods of Research and Diagnosis
in Education I (Faculty of Education, UNED). His research lines are related to code-literacy (teaching-
learning processes with/through computer programming languages) and computational thinking (cognitive
problem-solving ability that underlies computer programming tasks, among others). He is the author of the
Computational Thinking Test (CTt), which has been endorsed by the research community through several
Juan David Rodríguez García
Instituto Nacional de Tecnologías Educativas y de Formación del Profesorado
Juan David Rodríguez García is Teaching Technical Advisor at INTEF, the unit of the Spanish Ministry of
Education and Vocational Training responsible for the integration of ICT and Teacher Training in the non-
university educational stages. He is currently working toward his PhD Thesis in the field of Computational
Thinking (CT) development and the use of machine learning contents as a means to develop CT.
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... STEAM-EDU develops computational thinking skills at an early age [39], understood as the ability to formulate and represent problems to solve them through the use of tools, concepts and practices of the computer science discipline, such as abstraction, decomposition or use of simulations [40]. There are multiple fields that allow STEAM-EDU to work through computational thinking, such as robotics [41,42], block programing or artificial intelligence, which favor student problem solving [43,44] with benefits for the development of creative thinking [45,46]. ...
... The data from the pre-test study showed different results in the control and experimental groups. We agree with Refs [21][22][23]33,34,40,41] that TRANS_THINK had a better statistical evaluation in this study among the students of the experimental group that used STEAM and computational thinking as a discipline that allows them to solve and approach a problem, not necessarily math, and work cooperatively in person. This fact confirms that discussing how to approach a task, activity or problem and co-creating projects across multiple disciplines contribute to the development of transversal and critical thinking. ...
... In reference to the main objective of the study, "Evaluate the STEAM dimensions in sixth grade of primary education in times of pandemic", the post-test results of the experimental group support the lines of research of Refs [19,20] in relation to the fact that there is little research on STEAM-EDU teaching methodologies and resources among teachers, who encountered an added difficulty during the pandemic, that is, the restrictions implemented in educational centers by the government of Spain [69]. Finally, in the experimental group, the possibility of creating, researching, interacting, exploring, developing and presenting, which are areas of the Classroom of the Future, and these actions are associated with the pyramid of Bloom's taxonomy, are enriching cognitive processes that are achieved through computational thinking, following the lines of research in Refs [40,43,44]. In this study, the pre-test data show that they are viable but not as a result of the pandemic. ...
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The demand for professionals entering the labor market requires knowledge and disciplines in the areas of Science, Technology, Engineering, Art and Mathematics (STEAM). Schools are the first link to train competent students for today’s society. However, the pandemic has conditioned the teaching–learning methodologies based on promoting STEAM in educational centers, which is the reason that leads us to carry out this study. The main objective of the research is to evaluate the STEAM dimensions in the sixth grade of primary education in times of pandemic. The study method is based on a quasi-experimental, descriptive and correlational design with an experimental group and a control group. The data are collected through a validated questionnaire, pre-test and post-test, which develops an assessment of student collaboration in STEAM activities. The sample is made up of 142 Spanish students, of which 68 belong to the control group and 74 to the experimental group. The conclusions of the study highlight that the active methodologies, based on computational thinking and on makerspaces of the future classroom, influenced the STEAM dimensions of the experimental group before the pandemic. However, the pandemic and the health restrictions in face-to-face classes led to a negative assessment of the experimental group in the STEAM dimensions.
... Using the analysis of scientific research, we can conclude that computational thinking is a system of mental methods of actions, techniques, methods and corresponding mental tactics, the result of which is an algorithm. There are more and more publications devoted to the development of computational thinking in children of preschool and primary school age with an emphasis on the fact that the formation of computational thinking is the basis of such important skills as coding and programming (Bers, Flannery, Kazakoff, & Sullivan, 2014;Maya, Pearson, Tapia, Wherfel, & Reese, 2015;González-González, 2019;Moreno, Robles, Román, & Rodríguez, 2019). ...
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The article examines the problem of forming the information and digital competence of elementary school students by means of robotics. Our research work is aimed at analyzing the modern state of educational robotics, the readiness of educators for its teaching and experimental testing the possibilities of robotics as a means of forming information and digital competence of primary education seekers. The organization of pedagogical experiment meant diagnosis and correction of such components of information and digital competence as motivational, cognitive, active, reflexive, the formation of which took place in the course of pupils’ learning robotics constructors and interactive manuals devoted to the history and development of robots. To measure the level of information and digital competence of primary school pupils the system of expert assessments of its components was used. As a result of observing behavior, accuracy, speed and independence during the fulfillment of special tasks connected with the search and processing of information, computational thinking, work on the Internet, understanding the ethics of working with information etc. the level of information and digital competence of every pupil is defined as the sum of all its indexes. The generalization of the results allowed the conclusions: the use of robotics constructors and interactive manuals, online resources that imitate actions with robots help to increase the level of information and digital competence of education seekers.
... For Wing [12,13], the first person to introduce the term CT into the scientific field in 2006, CT is the development and knowledge that people acquire by thinking like a computer programmer. CT is a fundamental and analytical skill that children of the 21st century should develop [14] because it allows students to abstract [15] from a problem solving situation [16] and break it down into simpler ones until a solution is found [17]. ...
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Scratch is an educational software based on visual programming blocks. It was created in 2003 by the Massachusetts Institute of Technology Media Lab (MIT) and it develops computational thinking (CT) skills from an early age in schools and allows STEM (science, technology, engineering and mathematics) projects to be carried out. The aim of this research is to know the development of the scientific production of the Scratch programme in the educational field in scientific articles in WoS and its link with the STEM field. The methodology used in this study is of a bibliometric nature with an analysis of the development in the scientific literature and co-words. The Scratch in Education (Scratch-EDU) programme has been studied using the Web of Science (WoS) database. WoS, Vosviewer and SciMAT were used to extract the results and a total of 579 manuscripts were analysed. The results of the study show that the first scientific article on Scratch published in WoS dates back to 2004, although it is from 2011 when a considerable volume of studies began to appear in the scientific literature, and moreover, in recent years the scientific literature relates Scratch-EDU with topics and keywords related to the STEM field. The conclusions of the study are that the Scratch programme has had a progressive evolution in the scientific field related to education from 2012 to 2020, mainly in proceedings papers, with a decrease in manuscripts in the last two years. The emerging themes and keywords that have most influenced Scratch-EDU manuscripts in recent years are related to the terms “Implementation” and “Curriculum”, connected in turn, with terms such as “pedagogy”, “public school” or “students”. Another term that stands out in the development of scientific evolution is “Computational Thinking”, associated with topics such as “Primary Education”, “Learning” or “Problem Solving”. Finally, a discussion and conclusion of the results has been carried out, which can serve as a turning point for future lines of research on programming and CT in the STEM field from an early age in education.
... Si bien desde entonces se han sucedido largas discusiones, en ocasiones agrias, acerca de una mejor enunciación sobre qué es el pensamiento computacional, hoy ya podemos afirmar que se ha llegado a un mínimo consenso acerca de una definición (cuasi) definitiva que podría dictarse así:Bien sabemos que un constructo no es sólido ni empíricamente productivo hasta que se convierte en una variable susceptible de medición y evaluación, siendo para ello necesario concretar las definiciones conceptuales anteriores en otras operativas. estudiado la validez convergente entre algunos de dichos instrumentos(Román- González et al., 2019). Desde un punto de vista longitudinal, ya disponemos de un arsenal de test y pruebas, fiables y válidos, que permiten estimar el nivel de desarrollo del pensamiento computacional del sujeto a lo largo de todas las etapas educativas.Más concretamente, en el entorno de nuestro grupo y colaboradores cercanos se ha podido diseñar la siguiente terna: el "Beginners Computational Thinking Test" (5-10 años; ver Figura 1)(Zapata-Cáceres et al., 2020), el "Computational Thinking Test"(10-16 años; ver Figura 2)(Román-González et al., 2017), y el "Algorithmic Thinking Test for Adults" (>16 años)(Lafuente-Martínez et al., 2022). ...
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A diferencia de las variables físicas o biofisiológicas, como el peso o el grupo sanguíneo, que tienen existencia por sí mismas y admiten una medición relativamente directa e independiente del observador; las variables psicopedagógicas son “constructos”. Los constructos no tienen existencia propia e independiente, sino que exigen ser definidos y consensuados de manera particular por una comunidad académica. En otras palabras, los constructos tienen una historia y un desarrollo, desde que son inicial y tentativamente definidos hasta cuando han acumulado una suficiente evidencia teórica y empírica que nos permiten afirmar su utilidad y validez. Piénsese por ejemplo en el largo camino seguido por algunos constructos, hoy ya consolidados, como la “inteligencia emocional” o el “clima escolar”. Dicho lo anterior, este artículo se propone dos objetivos. Por un lado, pretende exponer y justificar la afirmación de que el “pensamiento computacional” es un constructo psicopedagógico que ha llegado a un momento o punto de madurez. Por otro lado, visibilizar el importante papel que en ese camino ha jugado la Revista de Educación a Distancia (RED), en particular a través de los tres números monográficos que ha dedicado al tema hasta el momento (VV. AA., 2015, 2020, 2021). Desde nuestro punto de vista, hay cinco razones fundamentales que en conjunto sustentan y apoyan el título de este artículo, a saber: i) que ya se ha avanzado suficientemente en las definiciones teóricas acerca de qué es el pensamiento computacional; ii) que dichas definiciones teóricas han podido ser operacionalizadas dando lugar al subsiguiente diseño de instrumentos que permiten medir el pensamiento computacional de manera fiable y válida (y a lo largo de las distintas edades); iii) que ya se han llevado a cabo implantaciones educativas masivas, a nivel nacional e internacional, con el objetivo de desarrollar el pensamiento computacional de los estudiantes y cuyo impacto ha podido ser medido e interpretado; iv) que el término “pensamiento computacional” ha sido explícita y oficialmente reconocido en la legislación educativa española recientemente promulgada; y v) que el constructo que nos ocupa tiene una naturaleza intrínsecamente dinámica y, con ello, lejos de conformarse con llegar a un estadio maduro, posee un gran potencial para seguir creciendo en el futuro.
... Son muchas las voces a favor de su potencial educativo (Moreno et al., 2019) ya que, la resolución de problemas reales y cotidianos requiere de un gran grupo de habilidades y actitudes transversales (González, 2019). Para esta investigación hemos optado por las indicadas por la Sociedad Internacional de Tecnología en Educación (ISTE) y la Asociación de Maestros de Ciencias de la Computación (CSTA), por ser organizaciones de reconocido prestigio internacional al servicio del profesorado interesado en el uso de la tecnología en la educación (ISTE, & CSTA, 2011, p. 1): ...
En la actual sociedad digitalizada el pensamiento computacional se ha convertido en una competencia imprescindible para la resolución de problemas cotidianos. Del mismo modo, la programación emerge con un gran potencial para el desarrollo de dicha competencia. Por ello, resulta urgente incluir la código-alfabetización en la formación inicial del futuro profesorado. En este estudio, se pretende valorar la experimentación basada en Scratch llevada a cabo con el alumnado de Grado de Educación Primaria de la Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU). Asimismo, se ha recogido la percepción del alumnado en relación con las posibilidades de uso que ofrece Scratch para el desarrollo de habilidades y actitudes del pensamiento computacional y a las expectativas de utilización de este lenguaje de programación en su futuro profesional. Los resultados indican que el alumnado considera evidente la relación de Scratch con la enseñanza de la programación. La experiencia ha sido valorada positivamente y, a su vez, señalan que Scratch puede ser útil para el desarrollo de los procesos del pensamiento computacional en el aula de Educación Primaria, añadiendo que lo incluirán en su futuro profesional docente.
... Nevertheless, Wing's definition of Computational Thinking is not universally accepted and has been often reviewed in the latest years, resulting in a range of views on the subject [1], [6]- [10]. Some authors advocate for a narrower, "traditional view" of CT and its application. ...
Conference Paper
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Computational Thinking (CT) is a set of thinking processes used by computer scientists to formulate problems and describe solutions. In recent years, CT has been largely explored by the Computing Education community. Due to its potential to contribute to problem solving and analytical thinking, CT could also be a relevant subject in K-12 education. For such, there is a need to qualify educators in CT. In this paper, we describe our experience in a large-scale course on Computational Thinking for pre-service teachers. Our goal is to qualify pre-service teachers in CT, both in the comprehension of its definition and how it can be incorporated in classroom. Our experience indicates that the participants had a positive awareness of CT concepts, as well as an adequate understanding of the pedagogical approaches to teach the subject. They also developed positive attitudes towards the field of Computer Science.
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Der vorliegende Forschungsband Scholarship of Teaching and Learning: Eine forschungsgeleitete Fundierung und Weiterentwicklung hochschul(fach)didaktischen Handelns setzt hier an und hat zum Ziel, auf Basis einer Bestandsaufnahme ausgewählter aktueller SoTL-Projekte an deutschsprachigen Hochschulen eine forschungsgeleitete Auseinandersetzung mit der Lehre zu führen. Dies inkludiert Konzepte, damit verbundene Gelingensfaktoren und Herausforderungen für die erfolgreiche Umsetzung sowie daraus entstandene Good Practices. In den Prozess der forschungsgeleiteten Auseinandersetzung involviert sind nicht nur Lehrende, Forschende und Hochschuldidaktiker*innen, sondern auch Studierende, entweder als Akteur*innen oder als ‚critical friends‘ und Mitforschende. Durch einen wissenschaftsgeleiteten Dialog im geplanten Band entlang der unten genannten Thesen können Impulse für die zukünftige Weiterentwicklung und Intensivierung von SoTL an Hochschulen abgeleitet werden. Im Rahmen dieses Forschungsbandes wird eine große Bandbreite von Fachdisziplinen, fachspezifischen Wissensbeständen, Methoden und didaktischen Fragestellungen erfasst und dem interessierten Fachpublikum vorgestellt. Eine Verbreitung und Verortung von SoTL in den Hochschulen und Universitäten in Deutschland, Österreich und der Schweiz findet sowohl in der hochschuldidaktischen Grundausbildung − etwa in den Zertifikatsprogrammen in mehreren Bundesländern und Kantonen – als auch in der standortbezogenen Studiengangentwicklung und Hochschulentwicklung im Kernelement Lehre statt.
Technical Report
La Escuela de Pensamiento Computacional e Inteligencia Artificial (EPCIA) es un proyecto del Ministerio de Educación y Formación Profesional, que se desarrolla en colaboración con las Consejerías y Departamentos de Educación de las comunidades y ciudades autónomas. El objetivo del proyecto es ofrecer recursos educativos abiertos, formación, acompañamiento y evidencias de impacto en las prácticas educativas y en el aprendizaje del alumnado, a fin de impulsar la incorporación del pensamiento computacional en la práctica docente a través de actividades de programación y robótica. Este proyecto, que está dirigido a docentes de todas las etapas educativas no universitarias y de cualquier materia o especialidad, lanzó su primera edición en el curso 18/19 en la que se inscribieron más de 700 docentes y durante el curso 19/20 en la que se inscribieron más de un millar de docentes de la práctica totalidad del país para participar en el proyecto. En este caso, la temática se centró en la Inteligencia Artificial. Uno de los objetivos de este proyecto es que la formación de los docentes se traslade a las aulas. Por ello, las tareas prácticas con las que el profesorado participante se familiarizó durante la fase de formación estaban diseñadas para ser utilizadas directamente en el aula. De este modo, los docentes de esta edición de la EPCIA han llevado a la práctica, con su alumnado, al menos 5 sesiones de trabajo relacionado con el pensamiento computacional y la Inteligencia Artificial. Por último, y en paralelo con la Fase 2, de puesta en práctica, se realizó una investigación para medir el impacto del proyecto en el aprendizaje y en la práctica docente. Esta investigación se ha desarrollado de forma independiente, pero coordinada, en las tres propuestas de la EPCIA: las actividades desconectadas, la programación con bloques (Scratch) y el desarrollo de apps con App Inventor, estas dos últimas combinadas con Machine Learning for Kids. Son los resultados de esta investigación los que se presentan en este informe.
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A guide to computational thinking education, with a focus on artificial intelligence literacy and the integration of computing and physical objects. Computing has become an essential part of today's primary and secondary school curricula. In recent years, K–12 computer education has shifted from computer science itself to the broader perspective of computational thinking (CT), which is less about technology than a way of thinking and solving problems—“a fundamental skill for everyone, not just computer scientists,” in the words of Jeanette Wing, author of a foundational article on CT. This volume introduces a variety of approaches to CT in K–12 education, offering a wide range of international perspectives that focus on artificial intelligence (AI) literacy and the integration of computing and physical objects. The book first offers an overview of CT and its importance in K–12 education, covering such topics as the rationale for teaching CT; programming as a general problem-solving skill; and the “phenomenon-based learning” approach. It then addresses the educational implications of the explosion in AI research, discussing, among other things, the importance of teaching children to be conscientious designers and consumers of AI. Finally, the book examines the increasing influence of physical devices in CT education, considering the learning opportunities offered by robotics. Contributors Harold Abelson, Cynthia Breazeal, Karen Brennan, Michael E. Caspersen, Christian Dindler, Daniella DiPaola, Nardie Fanchamps, Christina Gardner-McCune, Mark Guzdial, Kai Hakkarainen, Fredrik Heintz, Paul Hennissen, H. Ulrich Hoppe, Ole Sejer Iversen, Siu-Cheung Kong, Wai-Ying Kwok, Sven Manske, Jesús Moreno-León, Blakeley H. Payne, Sini Riikonen, Gregorio Robles, Marcos Román-González, Pirita Seitamaa-Hakkarainen, Ju-Ling Shih, Pasi Silander, Lou Slangen, Rachel Charlotte Smith, Marcus Specht, Florence R. Sullivan, David S. Touretzky
Conference Paper
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In this paper we present a web-based open source tool and a method for generating insight from any text or discourse using text network analysis. The tool (InfraNodus) can be used by researchers and writers to organize and to better understand their notes, to measure the level of bias in discourse, and to identify the parts of the discourse where there is a potential for insight and new ideas. The method is based on text network analysis algorithm, which represents any text as a network and identifies the most influential words in a discourse based on the terms' co-occurrence. Graph community detection algorithm is then applied in order to identify the different topical clusters, which represent the main topics in the text as well as the relations between them. The community structure is used in conjunction with other measures to identify the level of bias or cognitive diversity of the discourse. Finally, the structural gaps in the graph can indicate the parts of the discourse where the connections are lacking, therefore highlighting the areas where there's a potential for new ideas. The tool can be used as stand-alone software by end users as well as implemented via an API into other tools. Another interesting application is in the field of recommendation systems: structural gaps could indicate potentially interesting non-trivial connections to any connected datasets.
Conference Paper
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Computational thinking is nowadays being widely adopted and investigated. Educators and researchers are using two main approaches to teach these skills in schools: with computer programming exercises, and with unplugged activities that do not require the use of digital devices or any kind of specific hardware. While the former is the mainstream approach, the latter is especially important for schools that do not have proper technology resources, Internet connections or even electrical power. However, there is a lack of investigations that prove the effectiveness of the unplugged activities in the development of computational thinking skills, particularly for primary schools. This paper, which summarizes a quasi-experiment carried out in two primary schools in Spain, tries to shed some light on this regard. The results show that students in the experimental groups, who took part in the unplugged activities, enhanced their computational thinking skills significantly more than their peers in the control groups who did not participate during the classes, proving that the unplugged approach may be effective for the development of this ability.
Conference Paper
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Computational thinking (CT) is emerging as a key set of problem-solving skills that must be developed by the new generations of digital learners. However, there is still a lack of consensus on a formal CT definition, on how CT should be integrated in educational settings, and specially on how CT can be properly assessed. The latter is an extremely relevant and urgent topic because without reliable and valid assessment tools, CT might lose its potential of making its way into educational curricula. In response, this paper is aimed at presenting the convergent validity of one of the major recent attempts to assess CT from a summative-aptitudinal perspective: the Computational Thinking Test (CTt). The convergent validity of the CTt is studied in middle school Spanish samples with respect to other two CT assessment tools, which are coming from different perspectives: the Bebras Tasks, built from a skill-transfer approach; and Dr. Scratch, an automated tool designed from a formative-iterative approach. Our results show statistically significant, positive and moderately intense, correlations between the CTt and a selected set of Bebras Tasks (r=0.52); and between the CTt and Dr. Scratch (predictive value r=0.44; concurrent value r=0.53). These results support the statement that CTt is partially convergent with Bebras Tasks and with Dr. Scratch. Finally, we discuss if these three tools are complementary and may be combined in middle school.
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In the past decade, Computational Thinking (CT) and related concepts (e.g. coding, programing, algorithmic thinking) have received increasing attention in the educational field. This has given rise to a large amount of academic and grey literature, and also numerous public and private implementation initiatives. Despite this widespread interest, successful CT integration in compulsory education still faces unresolved issues and challenges. This report provides a comprehensive overview of CT skills for schoolchildren, encompassing recent research findings and initiatives at grassroots and policy levels. It also offers a better understanding of the core concepts and attributes of CT and its potential for compulsory education. The study adopts a mostly qualitative approach that comprises extensive desk research, a survey of Ministries of Education and semi-structured interviews, which provide insights from experts, practitioners and policy makers. The report discusses the most significant CT developments for compulsory education in Europe and provides a comprehensive synthesis of evidence, including implications for policy and practice.
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Computational Thinking (CT) has become popular in recent years and has been recognised as an essential skill for all, as members of the digital age. Many researchers have tried to define CT and have conducted studies about this topic. However, CT literature is at an early stage of maturity, and is far from either explaining what CT is, or how to teach and assess this skill. In the light of this state of affairs, the purpose of this study is to examine the purpose, target population, theoretical basis, definition, scope, type and employed research design of selected papers in the literature that have focused on computational thinking, and to provide a framework about the notion, scope and elements of CT. In order to reveal the literature and create the framework for computational thinking, an inductive qualitative content analysis was conducted on 125 papers about CT, selected according to pre-defined criteria from six different databases and digital libraries. According to the results, the main topics covered in the papers composed of activities (computerised or unplugged) that promote CT in the curriculum. The targeted population of the papers was mainly K-12. Gamed-based learning and constructivism were the main theories covered as the basis for CT papers. Most of the papers were written for academic conferences and mainly composed of personal views about CT. The study also identified the most commonly used words in the definitions and scope of CT, which in turn formed the framework of CT. The findings obtained in this study may not only be useful in the exploration of research topics in CT and the identification of CT in the literature, but also support those who need guidance for developing tasks or programs about computational thinking and informatics.
Conference Paper
Artificial Intelligence (AI) and Machine Learning (ML) have heavily irrupted in society, bringing new applications and possibilities while introducing some ethical problems. Governments and institutions around the world are working on the challenges posed by AI in all aspects, from economy to education. Therefore, introducing AI-related content at school and exploring how this kind of content can be taught becomes mandatory. In this paper we carry out a bibliographic revision of previous works done on ML, and then describe an educational resource developed by the institution of the first two authors (INTEF) aimed to teach ML in schools with Scratch and Machine Learning for Kids. The testimonials of three educators, who have implemented their own version of these resources, are depicted. More efforts should be made to introduce AI-related content in education.
Conference Paper
Computational thinking (CT) is a popular phrase that refers to a collection of computational ideas and habits of mind that people in computing disciplines acquire through their work in designing programs, software, simulations, and computations performed by machinery. Recently a computational thinking for K-12 movement has spawned initiatives across the education sector, and educational reforms are under way in many countries. However, modern CT initiatives should be well aware of the broad and deep history of computational thinking, or risk repeating already refuted claims, past mistakes, and already solved problems, or losing some of the richest and most ambitious ideas in CT. This paper presents an overview of three important historical currents from which CT has developed: evolution of computing's disciplinary ways of thinking and practicing, educational research and efforts in computing, and emergence of computational science and digitalization of society. The paper examines a number of threats to CT initiatives: lack of ambition, dogmatism, knowing versus doing, exaggerated claims, narrow views of computing, overemphasis on formulation, and lost sight of computational models.
Computational thinking (CT) is being located at the focus of educational innovation, as a set of problem-solving skills that must be acquired by the new generations of students to thrive in a digital world full of objects driven by software. However, there is still no consensus on a CT definition or how to measure it. In response, we attempt to address both issues from a psychometric approach. On the one hand, a Computational Thinking Test (CTt) is administered on a sample of 1,251 Spanish students from 5th to 10th grade, so its descriptive statistics and reliability are reported in this paper. On the second hand, the criterion validity of the CTt is studied with respect to other standardized psychological tests: the Primary Mental Abilities (PMA) battery, and the RP30 problem-solving test. Thus, it is intended to provide a new instrument for CT measurement and additionally give evidence of the nature of CT through its associations with key related psychological constructs. Results show statistically significant correlations at least moderately intense between CT and: spatial ability (r = 0.44), reasoning ability (r = 0.44), and problem-solving ability (r = 0.67). These results are consistent with recent theoretical proposals linking CT to some components of the Cattel-Horn-Carroll (CHC) model of intelligence, and corroborate the conceptualization of CT as a problem-solving ability. Available at: