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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: 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
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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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.
Jesús Moreno, Gregorio Robles, Marcos Román y Juan David Rodríguez RIITE, Núm. 7 (2019), 26-35
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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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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
Standards
1
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:
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 denition
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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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).
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 inuential keywords in CT denitions.
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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.
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 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
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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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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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... 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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... 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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... 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): ...
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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.
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.
Chapter
New theories often emerge from seemingly contradictory empirical evidences. This is precisely the starting point of this chapter. Recent computational thinking (CT) research in K-12 shows different results depending on whether the computational concepts involved are used to solve visuospatial (Román-González, Pérez-González, and Jiménez-Fernández 2017) or linguistic-narrative problems (Howland and Good 2015). Furthermore, the former study empirically demonstrates that CT is mainly a problem-solving ability linked with fluid intelligence, which is characterized by adapting to the context demands. All of the above suggests that CT could be manifested in multiple and different ways depending on the type of problems to be solved. In other words, we hypothesize the existence not of a single, but of multiple computational thinkings; analogous to the existence of multiple intelligences postulated by Howard Gardner (1983, 1999). In this vein, this chapter aims to address a triple goal. Firstly, we intend to ground our theory through a complete and comprehensive review of K-12 educational interventions, along which CT has been developed, mostly by means of computer programming, in order to solve different kinds of problems: verbal-linguistic, logical-mathematical, musical, bodily-kinesthetic, visual-spatial, interpersonal, intrapersonal or naturalistic problems. Secondly, we anticipate how to empirically contrast the theory through a proof-of-concept design of several items that will be part of a battery of CT assessment tests, which will allow to check the hypothesized multifactorial structure of CT. Thirdly, we speculate about some relevant implications that would arise in case of confirming the theory, for example: the possibility of establishing a personalized CT profile for each student; the subsequent design of multiple CT interventions and curricula that may include all types of problems and, therefore, may be more equitable and inclusive; ultimately, CT might serve as the anchor that Gardner’s theory needs to be finally contrasted.
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There is a debate about the way to introduce computational thinking (CT) in schools. Different proposals are on the table; these include the creation of new computational areas for developing CT, the introduction of CT in STEM areas, and the cross-curricular integration of CT in schools. There is also concern that no student should be left behind, independently of their economic situation. To this effect, an unplugged approach is the most cost-effective solution. In addition, this topic is interesting in the context of a pandemic situation that has prevented the sharing of materials between students. This study analyzes an unplugged cross-curricular introduction of CT in the Social Sciences area among sixth grade students. A group of 14 students was selected to carry out an unplugged intervention design—where they were required to program an imaginary robot on paper—in the Social Sciences area. Their CT development and academic results were compared to those of 31 students from the control group who continued attending regular classes. Results showed that an unplugged teaching style of CT in Social Sciences lessons significantly increased CT (p < 0.001) and with a large effect size (d = 1.305) without differences in students’ academic achievement. The findings show that children can potentially develop their CT in non-STEM lessons, learning the same curricular contents, and maintaining their academic results.
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El pensamiento computacional utiliza procesos cognitivos para formular problemas y soluciones que puedan ser automatizadas y procesadas por un agente de pensamiento. Su definición, evaluación y cómo enseñarlo, son aspectos frágilmente desarrollados desde la investigación educativa. El objetivo de esta investigación es proporcionar pautas pedagógicas que permitan diseñar acciones formativas para el desarrollo del pensamiento computacional del profesorado. En este estudio exploratorio participaron 63 docentes en formación. Las evidencias obtenidas con el uso de un cuestionario y un grupo de discusión nos indican que las actividades sin tecnología y la programación de robots son estrategias eficientes para el desarrollo del pensamiento computacional. La creatividad, la resolución de problemas, la cooperación y la comunicación, son las habilidades periféricas que se pueden activar en mayor medida. Además, presentamos un modelo de formación del profesorado en pensamiento computacional que incluye los siguientes pasos secuenciales a seguir: metodología “aprender pensando”, diseñar problemas reales, realizar actividades desenchufadas, programar robots y exponer los recursos creados.
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This article presents an investigation that has measured the causal impact of an intervention carried out within the School of Computational Thinking project framework, launched by the Spanish Ministry of Education and Vocational Training in the 2018-2019 academic year. Specifically, it studies whether it is possible to improve the development of mathematical competence through programming activities in 5th grade of Primary Education. The research design is based on the lessons learned from the ScratchMaths project developed by the University College London in the United Kingdom. Two groups of non-equivalent students have been used, the experimental group and the control group, without random assignment, with pre-test and post-test measurement on the mathematical competence variable. More than 3,700 students participated in the investigation. The results show that students in the experimental group developed this competence to a greater extent than students in the control group, with a significant and positive impact. Being the intervention effect size d=0.449, it can be stated that the project achieved the intended effect on mathematical competence. The generalization of computational thinking experiences in the curriculum can guarantee the improvement of the quality of the educational processes.
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Este artículo presenta los resultados de la investigación que ha medido el impacto causal de la intervención realizada en el marco del proyecto Escuela de Pensamiento Computacional, que el Ministerio de Educación y Formación Profesional de España puso en marcha en el curso académico 2018-2019. En concreto, el trabajo estudia si es posible mejorar el desarrollo de la competencia matemática del alumnado a través de actividades de programación usando el lenguaje Scratch en 5º de Educación Primaria. El diseño de la investigación consiste en un estudio empírico de intervención basado en las lecciones aprendidas del proyecto ScratchMaths, desarrollado por la University College London en Reino Unido. Se han usado dos grupos de estudiantes no equivalentes, grupo experimental y grupo de control, sin asignación aleatoria, con medición pre-test y post-test sobre la variable competencia matemática. Para ello, se ha contado con la participación de más de 3.700 estudiantes, que fueron asignados bien al grupo experimental -que trabajó la competencia matemática a través de actividades de programación informática- o al grupo de control -que lo hizo con otras actividades y recursos habituales en el área de Matemáticas. Los resultados muestran que el alumnado del grupo experimental desarrolló en mayor medida esta competencia que el alumnado del grupo de control, apreciándose un impacto significativo y positivo sobre la misma. Con un tamaño del efecto de la intervención d=0,449 puede afirmarse que el proyecto logró el efecto pretendido sobre la competencia matemática de los estudiantes. La generalización de experiencias de pensamiento computacional en el currículum podrá garantizar la mejora de la calidad de los procesos de enseñanza y aprendizaje.
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Conference Paper
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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.
Article
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: http://www.sciencedirect.com/science/article/pii/S0747563216306185