To cite this article:

Roncoroni Osio, U., Lavín, E. & Bailón Maxi, J. (2020). Computational Thinking: Digital Literacy Without Computers, Icono 14, 18 (2), 379-405. doi: 10.7195/ri14.v18i2.1570

1. Introduction

Network economics, digital manufacturing, robotics, artificial intelligence, and social networks may all create disruptive changes in companies, production, marketing, and communications. In education, academic institutions, teachers, and students have reported difficulties in relating to the digital revolution and reacting efficiently before the complex political, economic, and social transformations. In fact, after abundant initial excitement, people started to recognize that educational technology is not delivering the expected results (Buckingham, 2006; Dussel and Quevedo, 2010; Denning, 2017; Wolf, 2018), which may be due to two possible reasons.

First, current technologies and, in particular, digital technologies, are neither politically nor culturally neutral. As pointed out by Deleuze (2006) and Flusser (1984), modern machines are complex systems that trap users in a chain of hidden processes and are increasingly dependent on political and economic powers. Therefore, digital media must be read considering the contradictions of the modern capitalist system: the dynamics of democratic innovation and the reality of monopolies. The former is expressed in Barners Lee’s Internet utopia and in hacker and free software culture (Stallmann, 2002; Lanier, 2014). The latter comprises the new control and censorship systems from digital monopolies, such as Google or Facebook.

Second, the socio-cultural processes, characteristics, and effects of digital media are still not sufficiently clear. The consumption of technological developments has accelerated, as ICTs are deemed practical and functional tools without cultural connotations. However, the creative nature of the digital media, as well as the fact that commercial applications condition users through interfaces in a symbolic universe and in a unique and globalized epistemological and functional paradigm, is widely ignored.

The recent curriculum reforms in Europe and the United States (Ministero dell’ Istruzione, dell’Universitá e della Ricerca [MIUR], 2010; Johnson, Adams Becker, Estrada y Freeman, 2015) were an attempt at improving ICT usage by introducing computational thinking into the elementary and high-school syllabus (Wing, 2006; Aho, 2012). Still, this solution faces the same constraints that it intends to overcome. In effect, digital media establish a true zeitgeist that, after achieving command of the technological device, requires neuro-linguistic, cognitive, computational, and, above all, philosophical and aesthetic principles. These contents go beyond the environment of Sciences, Technology, Engineering and Mathematics (STEM) disciplines and address humanities and analog knowledge and skills, which are precisely what ICTs expect to replace. In this paper, we will discuss some critical matters from an educational standpoint: the limits of technocentrism (Papert, 1980) and technological solutionism (Morozov, 2014);¹, the creative nature of digital media; and the importance of cultural identity and traditional educational practices, for which we will later propose alternative technological solutions.

2. Method

2.1. Research Problem

In its original edition, Wing (2006) proposes that computational thinking seeks to prepare future professionals, from all areas and not just from computer sciences, to face network societies and the digital world. In this sense, computational thinking focuses on developing STEM knowledge, skills, and competences.

Nevertheless, some researchers, such as Denning (2017), claim that, except for information technology specialists, computational thinking has little practical and educational use. Along the same lines, Guzdial, Kay, Norris and Soloway (2019) assert that its contents are too generic and incomplete to make an effective contribution to the innovation of educational policies and, in particular, in terms of creativity, inclusion, and sustainability (Fonseca, 2005; Varma, 2006). Some scholars have also observed that the computational thinking theory lacks clear vision of the peculiar characteristics of digital media, which are essential to actual educational innovation (Buckingham, 2006; Manovich, 2013).

2.2. Objectives, Hypothesis, and Justification

Our study seeks to review and overcome some constraints facing the computational thinking paradigm, in order to fully leverage its potential, under the assumption that computational thinking must also account for humanities and preserving analog educational practices and local cultural heritage, based on the development of three tasks.

The first task is revisiting the concept of computational thinking but focusing on the development of STEM cognitive capacities, as originally proposed by Wing (2006). Computational thinking shall not be confined to simply computational sciences, because not everything is computable; even what is computable depends on future socio-cultural contexts. Hence, we need a more complex overview of the criteria-establishment and problem-solving mental processes involved in computational thinking. Here, computational thinking would develop between the analogical and digital borders. The challenge thereof is to ensure that these domains mutually collaborate and benefit from each other without conflicting.

The second task is to reveal the most important characteristics of digital media within their association with complexity science, language, and humanities. According to Reiman and Madav (2017), without including the most human aspects in education, computational thinking will not be able to surpass merely functional and contingent aspects.

Finally, the third task proposes a computerless digital literacy alternative for which we will discuss the educational advantages and possibilities from the Turing machine and shape grammars².

The digital media teaching method we propose is especially relevant for a country like Perú, as it includes cultural values and ancestral knowledge from its different regions, which contribute to narrowing gaps and foster the inclusion of students from remote areas with scarce economic resources.

2.3. Methodology, Materials, and Results

The content herein is derived from the research studies developed in the Institute for Scientific Research (IDIC) at the University of Lima in the last three years. From a theoretical perspective, we performed a critical review of the indexed literature, developed academic content for courses and workshops, and designed an experimental educational software³. Specifically, two C# application were developed: a Turing machine simulation and a yupana, the Incan calculator, simulator^4. In addition, the workshops conducted focused on designing the interface and corresponding functions.

Fieldwork has also been critical in testing our methodological and didactical hypotheses, and it has revealed a basic component for improving the effectiveness of educational technology. The previous manual development facilitates the understanding of the digital simulations and their critical assessment. This applies to students, researchers, and programmers who design educational applications.

2.4. Paper Structure

First, this paper reviews technocentrism within its social context and with special emphasis on certain aspects of complexity sciences that contribute to the development of computer sciences. Then, we assess the following digital media features: numeric language and its metamedia dimension (Kay, 1984).

In the subsequent section, these reflections are considered to discuss the educational relevance of the computational thinking paradigm in inclusive, creative, and sustainable terms.

The last section addresses the advantages of the Turing machine and shape grammars. Our conclusions are then constructed around a paradox: to develop real computational thinking we need the humanities, contents, and didactics that technocentrism has almost succeeded in replacing. Moreover, computer devices are dispensable and counterproductive for learning and creativity.

3. Discussion

ICTs are tools that essentially foster scientific and economic development and facilitate the management of academic institutions, which explains the STEM approach to computational thinking. In this sense, technocentrism fulfills some purpose; however, digital media are not only utility tools but also important agents of the contractions exhibited by network societies.

3.1. Limits of Technocentrism

Behind certain reform inconsistencies and the few academical successes of digital media, there lies conflict among technoscience, the knowledge society, and the sustainability issues from the neoliberal development model. These are the main beacons that will steer our comprehension of the role that computational thinking plays in these dynamics.

3.1.1. Complexity and Computational Thinking

The computational thinking theoretical framework is grounded on a concept of science that neither reflects the discussion nor questions realism and neo-positive determinism, whereas science, for instance, currently accounts for chaos, time, and the dynamic systems. Hence, computational thinking must include generative algorithms, fractals, and artificial life (Figures 3, 4, and 6). In fact, these complexity principles sustain the development of new cognitive tools used in specific applications in computers, in information technology, and in audiovisual production, such as video games.

Furthermore, in philosophy, the scientific method is highly criticized, thus giving way to hermeneutics, post-structuralism, and linguistics. In this light, the contributions from neuroscience and cognitive psychology regarding the relationships among cases, emotions, and rationality are particularly significative (Damasio, 2005; Kahneman, 2012). Here, the illusion of the logical and organized world on which computer science relies is questioned. This deconstruction of the Cartesian method is key to understanding big data and machine learning limitations (Rahimi, 2019; Zador, 2019). The scientific approach used by these technologies is an illusion; for example, the selection of data and learning parameters for neural networks is only supported on the beliefs held by the corresponding programmers: “We are prone to overestimate how much we understand about the world and to underestimate the role of chance in events” (Kahneman, 2012, p. 27).

Taking these factors into account, we are justified in asserting that computational thinking, as a conceptual instrument, must help future citizens recognize the polarities that feed the contradictions of network societies: the rigid and mechanistic logic of computational thinking and the unpredictable complexity of socio-cultural events.

3.1.2. The Disagreements of the Knowledge Society

The contradictions among technocentrism, computational thinking, and socio-cultural complexity are also reflected in the knowledge society and in the technological “solutionism” fallacies (Morozov, 2014). The first illusion that vanishes is the one of free access to knowledge through the Internet, which is supported on questions such as “Why should we conduct research or reflect?” and “Why should we develop theories?” For technological solutionism, theories are useless, because they rely on the idea that large servers can extract true knowledge and build tendencies that can be accurately verified simply by processing a huge amount of available data (Han, 2014).

In addition, Kurz (2003), Deleuze (2006), Rifkin (2013), and Lanier (2014) have proved how relations among digital media, politics, and economy generate new ways of power and control. Internet monopolies, such as Google or Facebook, ultimately test information as an objective correspondence to the phenomena of reality by invading our privacy and controlling data access criteria, which is actually filtered by their priorities.

In this sense, the technocentric profile of computational thinking is inadequate and self-referential, as it attempts to solve problems from a computational perspective when these problems have originated from the computational structures themselves.

3.1.3. Development, Educational Policies, and Sustainability

The last step in revisiting computational thinking contents is to determine its compatibility against the development and market demands.

In terms of technological development, globalized neoliberalism has been characterized by a lack of inclusion and sustainability and appearance of new colonization methods (Harris et al., 2018; Varma, 2006). Here, business logic traps students within the contingent and forces them to learn skills or knowledge that will soon be expropriated by artificial intelligence and robots.

Regarding educational policies, formal instruction competes against “invisible learning” (Cobo Romaní and Moravec, 2011), as ICTs are implemented without considering the fact that young people also learn from digital content outside the traditional education establishments: cell phones, game consoles, and the Internet. Furthermore, the quality of the educational software is far from the market level, which justifies its shunning from students who are used to AAA games designed for Xbox or PlayStation (Buckingham, 2006). Invisible learning, scientific and technological acceleration, and virtual education require self-learning aptitude, educational self-discipline, and enough autonomy for students to acquire knowledge on their own without mentors or tutors. Hence, young people do not receive training on how to search for information online or on the ethical, social, and political criteria used to guarantee its quality, that is, on critically and socially contextualized thinking, which is only acquired through historical, philosophical, and literary knowledge. To offer students the possibility of acquiring these essential competences, computational thinking must somehow reconnect itself with human sciences.

3.1.4. Complements for a Computational Thinking Epistemology

What can we deduce to amend the theoretical framework of computational thinking? First, we must consider the evident relationship between complexity sciences, computational thinking, and creativity. The digital audiovisual production industry, for example, requires professional skills based on the creative hybridization of science, humanities, and arts. Second, we must also consider the idiosyncrasies of the knowledge society, the hazards of monopolies, and artificial intelligence, which overflow qualitative parameters and resort to humanities in an attempt to verify the technological grounds, criteria, and purposes. Consequently, the risks of misunderstanding pragmatism and creating a computational myopia that rejects humanities are quite evident.

In relation to our hypothesis, we conclude that the necessary knowledge and skills required for understanding digital media exceed what computer sciences and machines can currently offer. In the next section, by analyzing particular properties of digital media, we will provide evidence on the limits of technocentrism and its potential for complexity.

3.2. About the Nature of Digital Media

Computational thinking is proposed as the knowledge and skills required to survive in scenarios designed by technological advancements; however, its effectiveness is limited when it is based on a superficial and even incorrect vision of digital media and its epistemological and pedagogical requirements. Hence, this section seeks to itemize three digital media aspects that must be considered within the computational thinking framework: the computational process itself, digital matter, and software media layers as metamedia.

3.2.1. The Computational Process

One of the reasons computers are misused in education (Buckingham, 2006; Wolf, 2018) is that neither their properties nor the characteristics of the computational process are fully comprehended, just as Turing demonstrated though its machine. First, computer science does not just mean calculations but an iterative process that acts through the positioning, reading, and writing of zeroes and ones in a linear space. Second, computational thinking neither exhibits intelligence nor conscience, because it is indifferent to the informative content it reads or writes. The magic of the computational process lies in precisely achieving the correct results, despite not knowing how to calculate or read. For this, fantasy, design, linguistic reasoning, and symbolic manipulation are all needed; contents which are not entirely included, even in their STEM extension (Henriksen, 2017).

3.2.2. Digital Matter

To begin, one must mention the mythologies that obscure the relationship between the analogic and the digital. Digital media are not, in the digital aspect, a novelty, because they have always simulated analogic processes. Therefore, they cannot be understood without knowing their ancestry or the physical world and the historical and natural models on which they are based. Two examples are 3D design, which is based on a Renaissance-era perspective, and bitmap images, whose algorithms simulate traditional artistic and photographic technics, such as the futurist aeropittura effects used in Photoshop filters (Figure 1).

Figure 1:

Aeropittura. Futurists studied this effect in a nose-diving airplane, as vision gets blurry due to the centrifugal force. Source: Italian Ways. Right: Motion blur filter.

Source: Prepared by the authors.

Another digital media feature that is not fully leveraged is the aggregation of all impressions expressed by the senses (shapes, colors, and sounds) into the same binary numeric information. This one is imperceptible, as audiovisual outputs through monitors or loudspeakers belong to the analog domain. Unawareness of this critical detail leads to continue explaining digital instances from analog instances, which conceals original and numeric content within a black box.

Then, students are rarely able to completely comprehend and leverage those digital concepts and processes that do not exist in the analog world and, therefore, the feedback among text, images, sounds, and calculations that redefines the analog concept of multimedia within the abstract algorithm, code, and data planes.

Here, the digital concept allows processes to be used as content (codes and algorithms), means (texts, images, or sounds), or tools (applications). From the educational content standpoint, it is essential to understand that digital matter does not exist without support from the techniques, recipes, and ingredients of the physical world.

Figure 2:

From analog drawings to 3D rendering models. 3D modeling is based on the observation of natural processes and phenomena, whose comprehension is enhanced by drawings. In computational thinking, these natural processes and phenomena are simulated through fractals, shape grammars, or artificial life.

Source: Prepared by the authors.

An example is the creation of virtual 3D worlds for videogames or animated movies (Figure 2), which are based on organic and inorganic natural processes, such as fractals and rendering processes⁵ studied in complexity sciences (Flake, 2000).

3.2.3. Software Layers

In accordance with the foregoing, an obvious conclusion is that the digital medium itself is the software and its multiple layers: a repository and channel that transports data and information; an operating tool; a corpus of concepts and knowledge; and a communication medium, as the code and the interfaces are texts. Besides, regarding the text, the code steers the processes executed in the computer based on linguistic rules that govern their writing and reading (Fishwick, 2008; Manovich, 2013).

From here, unique software features may be deduced, as asserted by one of the forefathers of computational technology, Alan Kay (1984, p. 52-59): “It is not a tool, although it can act like many tools. It is the first metamedium, and as such, it has degrees of freedom for representation and expression never before encountered and, as yet, barely investigated”⁶. That is, software is a tool that can be used to create other tools.

Creativity is its essential feature. For example, programming languages can originate other programming languages (Python was written in the C programming language), and these new programming languages can be used to create applications such as videogames or Digital Audio Workstations. Still, this feature is underestimated, if not completely ignored, and thus untapped in education.

3.3. Computational Thinking 2.0

In summary, computational thinking must provide future professionals with critical tools and specific competences to creatively exploit software properties as a primary process for all digital applications. First, these skills must foster the continuous self-learning that social, scientific, and technological transformations require from new generations and that, in most cases, takes place outside traditional education establishments. Furthermore, computational thinking must include the typical characteristics and knowledge of digital media, because they are the engines that power the evolution of the technological culture landscape.

In fact, as the literature has evidenced (Fonseca, 2005; Varma, 2006; Denning, 2017; Guzdial et al., 2019), educational computational thinking applications face two risks: the dissemination of their contents as part of a generic interdisciplinary field, thus hindering computational thinking from becoming a new independent discipline, and focusing exclusively on specialized content, thus restricting the universality of their target audience.

Second, for computational thinking, there is a scenario composed of combinations of interdisciplinary content supported by analog and specific knowledge from the computational universe. The challenge of managing digital media is revealed in its entire complexity, as epistemological domains—ranging from engineering to the arts—must be coordinated, which requires huge cognitive efforts from individuals and complex structural changes from education establishments. This is the moment when technocentrism, which consists precisely of viewing digital media from an instrumental perspective, without creativity and relation to socio-cultural contexts, hinders computational thinking.

3.3.1. Computational Thinking 2.0 and Creativity

From the standpoint of computational thinking as an educational practice, our assessment reveals that critical competences must be prioritized, thus demystifying technological devices. Likewise, computational thinking evidently faces two major challenges: designing new competences to manage the convergence of media and languages and exploring the particular creative dimension of software. In both cases, analog references and new coding difficulties come into play. Hence, digital media must not be taught through commercial applications dedicated to specific tasks, such as Excel or Photoshop, as they trap users within the context defined by the programmer, and they perform tasks that can also be performed analogously. The only difference would be qualitative: less time and less costs.

With this limitation, the creative potential typical of digital media is lost. Introducing coding as a competence is good progress, but it will not be sufficient if not completed based on software demands.

3.3.2. Information Literacy and Computational Creativity

The complexity of computational thinking corresponds to the complex nature of software: both are creative or expressive mediums and research, production, and communication tools. In the first modality, the digital domain is general, transdisciplinary, and basically humanistic, whereas, in the second, the digital domain focuses on computer sciences and constitutes a set of disciplines surrounding mathematics and engineering. Still, it is clear that computational thinking cannot solve both cultures in the same manner.

For the individualization of contents and methods adequate for the socio-cultural dimension of computational thinking, we will revisit the concept of information literacy as proposed by Shapiro and Hughes (1996). From this viewpoint, any digital media concepts and methods that can be generalized and included in any subject are considered. In other words, information literacy comprises the following actions: a) associating the contents from each discipline with their appropriate technical solution based on digital media properties and b) learning how to use computational thinking as creative and expressive aid. Precisely, this is what computational creativity proposes, which, in fact, some authors deem a new branch of computational thinking (Mazzone and Elgammal, 2019).

Based on the characteristics of digital culture and considering software a creative metamedium, IT devices and digital gadgets are unnecessary in education, as they interfere with the potential of computational thinking, its inclusion, and sustainability. The latter statement is supported both from the perspective of information literacy and computer sciences, into which we have divided the computational thinking field.

3.3.3. A Digital Culture Without Computers

Figure 3:

Yupana. Source: Yupana and quipu. Right: Gil, A. (s. f.). Tipón, Perú. Source: fotoAleph. The Tipón archaeological site in Perú is an example of using natural computational thinking for hydraulics. In effect, these are finite state machines in architectural balance with the landscape.

The use and abuse of digital applications, which are almost always imported in Latin America, impose a digital culture model limited to passive consumption, which even allows for a technological expression of colonization (Fonseca, 2005; Varma, 2006; Lanier, 2014), thus consuming space from educational autonomy, student creativity, and the resources for the original software research.

Limiting the use of ICT devices strengthens cultural identity, as it yields space for ethnomathematics and ethnocomputing, such as the Peruvian yupana (Figure 3) or Shipibo kené prints (Figure 4) (Apaza Luque, 2017)⁴.

Figure 4:

Continuation between nature, tradition, and computational thinking. Leaf veins, a kené drawing, and a Peano curve.

Source: Prepared by the authors.

These ancestral devices and technologies enable students to construct a spontaneous connection between the environment, cultural traditions, and computational thinking, as these devices and technologies can be built and operated manually using resources from their own environments. For designing computational processes, the best tools are freehand drawings and artwork, because, first, they convey ideas to process development without interference and, second, they can be transversally applied in different subjects. Therefore, the most abstract contents become significative, reinforcing student self-esteem and their creative impulses.

As discussed by Wing (2006) and Aho (2012), computer sciences encompass algorithmic and recursive thinking, logic, data management, and interactive systems design. Thus, they correctly define STEM contents, which prepare students to professionally manage computational or programming applications. Still, the STEM model is insufficient. Within this context, let us recover what is essential: the nature of the computational metamedium and the difference between the design of the original technologies and the consumption of existing applications. Even in the restricted context of computer sciences, the essentially creative nature of the digital media is present; therefore, computational thinking is not really associated to premanufactured ICT solutions. In addition, methodological problems clearly arise regarding how computational thinking is taught in classes. However, there are technologies, which are not fully exploited yet, that offer possible alternatives for education.

3.3.4. Computational Thinking 2.0 Methods and Materials

In the education reforms of the United States and various European Union countries, the first curricular change aimed at improving student digital competences has been the introduction of coding. This decision, in the opinion of many educators and researchers (Schmidt-Crawford, 2018; Guzdial et al., 2019), is validated, because programming improves logical thinking, design thinking, project management, and the soft skills required for effective collaborative and group work. However, in order for coding to be effective and significative, it requires vast previous knowledge and enough time to learn the programming languages used (even for virtual languages, such as Scratch), and, particularly, it requires something that some students may lack: commitment and passion. Finally, there is no evidence that coding actually improves STEM skills and that it may be useful for students who will not pursue computer sciences (Schmidt-Crawford, 2018). Thus, coding, without denying its educational use, cannot be the main approach to the digital world.

An alternative, which requires further research in the future, may be computational processes that foster simpler algorithm designs and programming principles, softening the transition between analog processes and the implementation of information technologies. Based on what students have learned through software development and the results from the experimental workshops conducted, we suggest using Turing machines and shape grammars, as explained below.

Both procedures are performed through a minimalist syntaxis and a compact visual language using mechanisms that are quickly acquired and that can be developed manually, as this facilitates understanding programming processes in a more intuitive manner (Figures 5 and 6).

The brilliant simplicity of these processes allows us to solve any computable problem with just our brains, our hands, and simple tools, such as pencils, paper, or daily-use objects.

Then, computational implementation is only useful in executing processes faster; however, for this very reason, it obscures its cognitive and creative processes, thus leaving users to face the limitations of the interface.

Figure 5:
Shape grammar examples. Left: Prusinkiewicz, P., Cieslak, M., Ferraro, P. and Hanan, J. (2018). Modeling plant development with L-systems. Source: Springer Link. Right: Shen, G. (2014). Shape grammar exercises.
Source: Slide Share.

Figure 6:

From natural shapes to procedural drawings and generative software.

Source: Prepared by the authors based on a drawing by Crousse, V. (2018) and software by Roncoroni, U. (2017).

4. Conclusions

This paper discusses certain dimensions regarding socio-cultural context and digital media, such as the epistemological and methodological strengths and weaknesses of computational thinking. These conclusions describe the educational complexity elements to which computational thinking has to adapt and unveil the interweaving issues evidenced by the literature, educational practices, and software design.

First, social, scientific, and cultural evolution, as well as the crisis of some neo-positivist principles, have revealed the limits of technological technocentrism and solutionism. Paradoxically, these limitations are also disclosed against network society and innovations, such as artificial intelligence and robotics. In this light, computational thinking must strive to help future professionals and citizens to move into an unpredictable landscape, wherein multiple social, cultural, and environmental elements collide into one another. Digital media are one of the causes for these transformations, but the knowledge and skills required exceed the domain of computer sciences. Therefore, humanities are critical tools.

Regarding the technological context, we have assessed the computational process, digital media complexities, and software as a digital means in itself, thus identifying its essentially creative nature as a metamedium. The analysis leads to the conclusion that the main objective of an inclusive and sustainable technological digital development strategy is the production of original technology. We have also emphasized, through the computational creativity concept, the critical role played by cultural identities and traditions (ethnomathematics and ethnocomputing), as they uphold an essential element for creativity: the availability of different and unique ingredients. For computational thinking, all this calls for contents with complexity that far exceeds that of computer sciences and the STEM model. Even the STEM paradigm seems insufficient (Henriksen, 2017), because the convergence with art should not be brought in a romantic sense but should rather be coherently provided along with complexity sciences and software properties.

These conclusions have served to update the current computational thinking model. At first, computational thinking contents are effectively very wide and generic, but there is little to do. On the one hand, the complexity of the technological cultural landscape created by digital media is real. Thus, we proposed dividing the computational thinking field into generic and humanistic contents: information literacy and the computer science content related to computational creativity. In the former case, computational thinking would be added content to each school subject, with the task of translating its own cognitive content and processes to be executed by a machine. In the latter, computational thinking would be an individual subject included in the core curriculum. In addition, humanities must evidently modernize and adapt their criteria to digital media characteristics, which is yet to happen.

On the other hand, this complexity also demands alternative technological solutions and didactic practices. Hence, we proposed recovering traditional and analog knowledge and skills, such as freehand procedural or algorithmic drawings (Figures 5 and 6), and we have introduced two possible solutions: the Turing machine and shape grammars. These two solutions achieve the optimum goal of the digital culture by combining science, art, creativity, nature, and cultural traditions (Figures 7 and 8).

Figure 7:

Left: Manual Development of the Turing machine. Source: Prepared by the authors. Right: Roncoroni, U. (2018). Turing machines 10.0. Software used for studying and designing Turing machines, developed as part of the research project at the University of Lima between 2018 and 2019. The application can be freely downloaded.

Source: Digital Poiesis.

Figure 8:

Roncoroni, U. (2019). Digital Yupana. Software developed within the framework of the research project at the University of Lima between 2018 and 2019. The application can be freely downloaded.

Source: Digital Poiesis.

The ideas proposed herein are neither the only possible ones nor maybe the best. We may conclude by reaffirming a few basic principles and proving a recommendation for academic institutions:

• Computational and digital concepts are the offspring of certain principles, ideals, and purposes of historical, political, and social contexts. As these contents are embedded in digital media, they must not be used as didactic means; that would be like placing the horses behind the cart.

• The greatest educational power is acquired through humanities and natural processes, from freehand drawings to using analog materials and devices. Therefore, to fully understand digital media and strengthen computational thinking, we must limit the use of technology.

Finally, digital media are a more complex challenge than what scientific literature and education reforms assume. Likewise, further research and experimentation are required to address them more consistently. This will not be an easy task. Even though the digital rhetoric appeals to freedom of access and collaboration, in the real world, digital media are controlled by monopolies that exploit both technology and users. Hence, neither these powers nor the market is interested in improving the technological culture of the population, because it releases users from the network to which they are connected. In this light, we, as educators, must provide students with the critical devices required to move within this context and, maybe, this is the highest commitment of computational thinking.

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