Computational thinking cannot avoid trends that are otherwise marked for technology-supported education. However, there is a margin of variability. Next, we comment experiences of the authors and their commitment to the theoretical corpus of learning and instructional design make them conceive, within that margin, as more favorable trends for more effective learning within the field of social performance and personal development of individuals:
A. Learning ecologies of computational thinking.
Under this denominator various trends are grouped with a common factor: the premise that the context in which learning takes place has an enormous influence on students and their educational development. It is explained in The Ecology of Learning. Several Streams of Research Take a Broad Approach to Understanding the Learning Process and in Media ecology: An interdisciplinary approach to the study of communication by Breslow (2001 and 1986) and Nystrom (1973). This perspective is not new, now the difference is that it is structured in its approach and takes on a new meaning with the technological learning environments, particularly important now in the computational thinking environments as a cohesive element and interaction of the components that constitute it.
The theoretical construct has its roots in the production of seminal thinkers such as John Dewey, Jean Piaget, Lev Semenovich Vgotsky and Kurt Lewin; and it implies powerful news about how teaching is handled and learning is achieved when it transcends the individual, in its origin, as a cause of its formation, in its projection and in its interactive nature. There we include pedagogical perspectives such as context development and situated learning. To these contributions must be added those of Paper and recently those of Grover (2018) that we have found. But what will be the characteristics of these particular ecologies. It is something that remains to be determined by practice and research.
B. Computational thinking presents a challenge that can be pointed out as the most important.
That is the inclusiveness of all kinds. Its new frontier is Adaptive Artificial Intelligence and recommendation algorithms. In this regard and with learning ecologies, a version of artificial intelligence that has to do with intelligent learning environments (considered these as an evolution of adaptive environments and context-sensitive environments) and with recommendation algorithms.
C. Computational thinking as literacy and key competence.
Ephemerally computational thinking will have a strong validity considered as early learning of programming, of coding. But every time they will impose computational thinking modalities that will make sense as a new literacy in a new culture and as a key competence, in the sense that we have described elsewhere, in the line that Grover (2018) and Zapata-Ros (2015) advocate for now: As an accumulation of various related skills because of the meaning attributed to them being useful to this type of thinking, which serves to do things and work. Other collateral or basic initiatives will also come, such as unpplugged, educational robotics or the development of algorithm skills close to AI. But always aimed at developing a computational thinking of these characteristics.
D. In general, the differences between superficial learning and deep learning will be more marked.
In relation to what was commented in the previous section, this characteristic will particularly affect learning in environments of computational thinking, robotics, artificial intelligence, etc. In the Knowledge Society, in a first stage of the Internet and networks, a notable feature of its development has been a boom of banality and relevance. Myths, including educational ones, have proliferated in the network and have affect educational and research institutions, contaminating them in different ways. One in the application of supposed educational principles and procedures derived from it, and another in the nature of the contents themselves.
Traditionally, the difference between what they call “deep” and “superficial” learning is based on the fact that deep learning is accepted as learning that goes beyond memorization and reaches a more complete understanding of concepts and ideas, the results of the fundamental decisions that instructors make about how their courses will work (for example, the type of homework and exams that they plan). But currently the border is not only in purely memoristic assimilation, or even in a weak or linear understanding, as opposed to the authentic acquisition of knowledge, which entails attribution of meaning and execution with autonomy of what has been learned, but also encompasses the critical sense, the discernment between the consistent and logical of what is not or the metacognition. It is not only an application to humans of a concept, deep learning, which was defined thinking about machines, but a way of learning that goes beyond memorization and the application of trivial patterns in learning.
This post is included in an article of RED Distance Education Magazine. It should be cited as:
Avello-Martínez, R., Lavonen, J. y Zapata, M. (2020). Coding and educational robotics and their relationship with computational and creative thinking. A compressive review. RED. Revista Educación a Distancia, 62. DOI: http://dx.doi.org/10.6018/red/6
Breslow, L. (1986). Media ecology: An interdisciplinary approach to the study of communication. Paper presented at the Association for Integrative Studies Conference, Bowling Green, Ohio
Breslow, L. (2001). The Ecology of Learning Several Streams of Research Take a Broad Approach to Understanding the Learning Process. http://web.mit.edu/fnl/vol/142/breslow.htm
Grover, S. & Pea, R. (2013). Computational Thinking in K–12 A Review of the State of the Field. Educational Researcher. 42. doi:10.3102/0013189×12463051
Grover, S. (2018, March 13). The 5th ‘C’ of 21st century skills? Try computational thinking (not coding. Retrieved from EdSurge News: https://edtechbooks.org/-Pz
Nystrom, C. (1973). Towards a science of media ecology: The formulation of integrated conceptual paradigms for the study of human communication systems. Unpublished doctoral dissertation, New York University
Zapata-Ros, M. (2015). Pensamiento computacional: Una nueva alfabetización digital. RED. Revista de Educación a Distancia, 46. Retrieved from http://www.um.es/ead/red/46/zapata.pdf
Zapata-Ros, M. (2019). Pensamiento computacional desenchufado. Education in the knowled