Not the same: a text network analysis on computational thinking definitions to study its relationship with computer programming

Abstract

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.

Full text

Revista Interuniversitaria de Investigación en Tecnología Educativa (RIITE)
Nº 7 Diciembre 2019 pp. 26-35 ISSN: 2529-9638 DOI: http://dx.doi.org/10.6018/riite.397151
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Not the same: a text network analysis on computational
thinking definitions to study its relationship with computer
programming
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)
jesus.moreno@programamos.es
Gregorio Robles
Universidad Rey Juan Carlos (España)
grex@gsyc.urjc.es
Marcos Román-González
Universidad Nacional de Educación a Distancia (UNED) (España)
mroman@edu.uned.es
Juan David Rodríguez García
Instituto Nacional de Tecnologías Educativas y de Formación del Profesorado (España)
juanda.rodriguez@educacion.gob.es
Recibido: 26/09/2019
Aceptado: 9/12/2019
Publicado: 26/12/2019
ABSTRACT
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.
KEYWORDS
Computer Science Education; Programming; Text Structure

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RESUMEN
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.
PALABRAS CLAVE
Educación informática; Programación; Estructura de Texto
CITA RECOMENDADA
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: http://dx.doi.org/10.6018/riite.397151
1. INTRODUCTION
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
competence?
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
principales.
• Es posible aunar nodos comunes que evidencian que la dispersión conceptual es solo aparente.

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2. BACKGROUND
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).

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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 define CT, we also find authors and organizations that
modify their initial proposals over time. Hence, in the [Interim] CSTA K-12 Computer Science
Standards
1
we find yet another definition 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:
1
https://www.csteachers.org/Page/standards

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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 definition
Publication
Type
(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
3. METHODS
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
surround”.
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

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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).
4. RESULTS
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 influential keywords in CT definitions.

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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 influential contextual clusters. These words
are the nodes that have more connections within each group, being in consequence the most
influential 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 influential communities of words in CT definitions.
Cluster
Words in the context
Color1
1
computer, science, tool
Orange
2
problem, solve, solution
SpringGreen
3
abstraction, simulation, decomposition
Fuchsia
4
system, information, algorithmic
Olive
5
logic, debug, performance
Purple
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.
4. DISCUSSION AND CONCLUSIONS
The results of the text network analysis show that neither programming nor coding emerge
among the most influential 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

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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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INFORMACIÓN SOBRE LOS AUTORES
Jesús Moreno-León
Programamos
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.
Web: http://jemole.me/
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.
Web: http://gsyc.urjc.es/grex
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
publications.
Web: http://goo.gl/oox5Qn
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.
Web: http://juandarodriguez.es
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