Este artículo está bajo una licencia de Creative Commons Attribution-NonCommercial 4.0
Debe ser citado como
Avello, R. et al (2019) Some references and comments about coding and educational robotics and their relationship with computational and creative thinking. Blog RED de Hypotheses. El aprendizaje en la Sociedad del Conocimiento. https://red.hypotheses.org/1935
The speed of change in our society has accelerated since the birth of the Internet and will accelerate rapidly through the implementation of artificial intelligence (AI) innovations, for example, in health and social care, in transport and education, as well as in the learning analysis. New technological tools, technology-based services and support are being introduced into our daily lives faster than ever. Among these technological advances, especially AI, robotic technology has increased dramatically in recent years. News headlines from major news sources, including the New York Times, CNN, Wall Street Journal and BBC, frequently present several robotic innovations, which is a strong indication of this phenomenon (Yiannoutsou, 2017).
The need for competencies development in rapidly changing societies has been debated throughout the world (Zapata, 2015) and these have been called 21st century skills/competencies or generic/transversal competences. These 21st century competencies describe the wide range of competencies necessary to fully participate in modern societies and support the employability of citizens. However, there are several definitions and connotations related to these competencies. For example, UNESCO (Five Pillars) emphasizes the definition of learning and education for sustainable development.
In the description of UNESCO Universal Learning, they analyze what learning is important for all children and young people for the 21st century and for a decent life. The OECD (DeSeCo) analyzes skills, which meet complex demands, by mobilizing psychosocial resources in different contexts. The EU (lifelong learning, 8 key competences) analyzes the competencies (knowledge, skills and attitudes) necessary for personal fulfillment, active citizenship, social inclusion and employment (Voogt and Roblin, 2012). For example, according to DeSeCo (OECD, 2005), people in the 21st century must be able to use a wide range of tools, including socio-cultural (language) and digital (technological) tools, to interact effectively with the environment, engage and interact in a heterogeneous group, carry out a work oriented to research and problem solving, assume responsibility for managing their own lives and act autonomously. In this environment, both critical thinking, including computational and creative thinking are necessary to learn these skills.
Particularly important in this context is what has been called unplugged computational thinking (CT), which Zapata-Ros (2019) refers to the set of activities and its educational design, which are developed to encourage children, in the first stages of cognitive development (early childhood education, first tranche of primary education, home games with parents and friends, etc.), skills that can then be evoked to support and enhance a good learning of computational thinking in other stages or in technical, professional or even university training. Activities that are usually done with chips, cards, board games or playground, mechanical toys, etc. And that has been incorporated into the official curricula of some countries such as Singapore and Hong Kong.
The characteristic of problem solving, such as building a robot or developing a code, is a process that consists of different steps (for example, problem formulation – evaluation of ideas – choice of solution – test and evaluation). This process requires critical, creative and computational thinking. In general, critical thinking is the analysis of facts to form a judgment. However, there are various types and situations for critical thinking and there are several different definitions, which generally include rational, skeptical, impartial analysis or the evaluation of objective evidence. And creativity is understanding as a context-related process to generate or recognize ideas, alternatives or possibilities to solve problems individually or in collaboration with others, and can be considered as original, valuable and useful by a reference group.
In this sense, creative thinking is needed when generating and playing with unusual and radical ideas related to the problem or design. Creative thinking can be stimulated by both an unstructured process and brainstorming, as well as by a structured process such as lateral thinking (Fisher, 2005). On the other hand, computational thinking is necessary to solve problems in the context of the design of a code or robot. It is necessary to design algorithms that make computers do jobs and to explain and interpret the world as a complex of information processes. The characteristics of computational thinking are decomposition, pattern recognition or data representation, generalization or abstraction and algorithms (Grover and Pea, 2013).
Computational thinking has gained great attention in the field of education in recent years, especially after the launch of Code Hour in December 2013 in the EE.UU. And England implemented its computer education in 2014 (García-Valcárcel, and Caballero-González, 2019). In a seminal article on computational thinking by Jeannette Wing in 2006, she predicted that computational thinking would be a fundamental skill used by everyone in the world in the mid-21st century (Wing 2006). In general terms, computational thinking consists of problem solving using basic concepts, procedures and development of programs and algorithms in computer science, and can help develop as: creativity, problem solving, abstract thinking, recursion, iteration, collaborative methods, patterns, among others.
However, there is a more holistic approach to what computational thinking is. This refers to the set of skills and other elements of cognitive and procedural development that we can find in the skills that serve programmers to do their homework, but which are also useful to people in their professional and personal lives as a way of organizing the resolution of their problems, and of representing the reality around them. These complex skills we said that it constitutes a new literacy (Zapata-Ros, 2015) — or the most substantial part of it — and an inculturation to handle a new culture, the digital culture in the knowledge society. In this way Zapata-Ros (2015), has determined 15 of these elements, among which there are as diverse as ascending thinking, descending thinking, pattern language or synectics. Without ruling out the classics of “successive approximations or trial error, problem solving and abstract thinking.”
This definition by accumulation of skills has also been formulated by Professor Shuchi Grover (2018, March 13), of Stanford, who also points out the difficulty of defining the CT, and then adopts the position of defining it by breaking down the skills as its component parts. So most of them involve or are skills, but they are always easy to operationalize (they are all central parts of computer science, educators and researchers have found that it is easier to operationalize for the purposes of teaching, curriculum and evaluation design) and above all they are possible to include in an educational design.
These are skills that include powers to operationalize logic (logical thinking), algorithms (algorithm), patterns, abstraction (abstract thinking), generalization (ascending thinking), evaluation and automation. It also means approaches such as “breaking down” problems into subproblems to facilitate resolution (downward thinking), creating computational artifacts (usually through coding); reusing solutions, testing and debugging (trial and error); iterative refinement (iteration). Finally, he points out that the CT “also implies collaboration (collaborative methods) and creativity”. So this definition also by accumulation coincides in ten of the fifteen elements of the previous definition.
There is another basic coincidence and it is that in the Groves article (2018, March 13) the relevance of Computational Thinking is pointed out in that it constitutes one more to those already accepted as competences for the digital society. In any case, what both developments have in common is that computational thinking is a point of cultural inflection, a new literacy.
In Europe, we find projects about computational thinking; one is Erasmus+ KA2 “TACCLE3 – Coding. The contents presented through the project’s website (http://taccle3.eu/), are an example of successful educational practices and experiences in the process of incorporation and promotion of these skills (García-Peñalvo et al., 2016). Researchers Karen Brennan (Harvard University) and Mitch Resnick (MIT) have made a significant contribution to the conceptual framework on computational thinking by formulating an alternative model on this style of thinking. The model was proposed within the research project that resulted in the creation of Scratch, a visual programming platform “by blocks” that allows children and young people to create their own interactive stories with animations and simulations in a playful environment. The model of computational thinking formulated by Brennan and Resnick (2012) is based on three dimensions: computational concepts, practices, and perspectives.
The skills to innovate or employ creative, critical and computational thinking cannot be cultivated through educational practice, focusing largely on memorizing knowledge without providing opportunities for students to transfer them to practice and use knowledge in various problem solving situations. There are urgent calls for innovative educational approaches worldwide that can foster the learning of 21st century competences, especially competences needed for innovators including critical thinking, problem-solving, creativity, inventiveness, collaboration and teamwork, and communication skills through transdisciplinary, learner-centered, collaborative, and project-based learning (PBL). These pedagogical approaches have been designed according to learning science research outcomes. Krajcik and Shin (2015), emphasized the following characteristics of these approaches and describe PBL as an example approach:
- PBL starts with a driving question, that is, a problem to be solved and focuses on the learning goals of the curriculum that students are required to master.
- Students are active in learning and explore the driving question by participating collaboratively in scientific and engineering practices, like designing, coding, inquiring and communicating, that are central to expert performance in science and engineering.
- Students create a set of tangible products, like a program code or a robot, that address the driving question. These are shared artefacts are kind of cognitive tools and publicly accessible external representations.
In this regard, many researchers have been investigating the coding and use of robots to support the education and learning of students. Studies have shown that robots can help students develop problem solving skills and learn computer programming, math and science. The educational approach based mainly on the development of logic and creativity in the new generations since the first stage of education is very promising (García-Valcárcel and Caballero-González, 2019). For these purposes, the use of robotic systems is becoming fundamental if applied from the earliest stage of education. In elementary, secondary and k12 schools, robot programming is fun and, therefore, represents an excellent tool for introducing ICTs and helping the development of logical and linguistic skills, and children’s creativity.
The landscape of educational robotics and coding is vast, but fragmented inside and outside school environments and situations. In the last two decades, robots have begun their incursion into the formal education system. Although several researchers have emphasized the learning potential of robotics, the slow pace of its introduction is partially justified by the cost of the kits and the different priorities of schools to access technology. Recently, the cost of electronic kits and components has decreased (i.e., LEGO Mindstorms – http://www.mindstorms.lego.com/, Arduino – https://www.arduino.cc, Raspberry Pi – https: / /www.raspberrypi.org, among others), while its capabilities and the availability of hardware and support software have increased (Yiannoutsou, 2017). With these benefits, educational robotics kits have become more attractive to schools.
In this context, several technology providers, teachers, academics, companies that focus on delivering educational material, etc., invest in the creation of different learning activities around robotic kits, to show their characteristics and make them attractive inside and outside of schools. Therefore, an increasing number of learning activities has emerged. These activities share common elements, but they are also very diverse, since they address different aspects of robotics as teaching and learning technology, and their success lies in how well they have identified these aspects and how well they address them.
This is partly due to the fact that robotics is a technology with special characteristics compared to other learning technologies: they are inherently multidisciplinary, which in terms of design of a learning activity can mean collaboration and immersion in different subjects; they are widely used in formal and non-formal learning environments; its tangible dimension causes disturbances, especially in formal educational environments, which are closely related to the introduction of innovations in organizations and schools (that is, from considering orchestrations in the classroom to establishing or not establishing connections with the curriculum, etc.); they are relevant to the new learning practices that now flourish on the Internet, such as the creators’ movement, the “Do it yourself” and “Do it with others” communities, etc.
This recent development of cutting-edge educational tools, both software and hardware, has provided opportunities for children to participate in various improved technological activities, such as “advanced scientific exploration, creating interactive textiles, building simulations and games, programming video games, designing a virtual robotics system, create sophisticated worlds and 3D games through programming, build new types of cybernetic creatures, explore environmental science and geographic information systems” (Blikstien 2013, p. 5) and build robotic inventions. Although such developments have contributed to the popularity of the movement of manufacturers and digital manufacturing, there is still a division in the population of potential users between those who have and those who do not. It is crucial to bring the education of the manufacturer to all classrooms so that everyone has the opportunity to learn from the activities of the manufacturer. For this reason, it is important to identify student learning outcomes through robotics creation activities (Wang, Lim, Lavonen and Clark-Wilson, 2019).
Some research provides evidence that shows the positive changes that occur in students immersed in training courses in programming skills and computational thinking using programmable robots (Chen, Shen, Barth-Cohen, Jiang, Huang and Eltoukhy, 2017; Durak and Saritepeci, 2018). In the Spanish context, for example, programs are increasingly aimed at children in the early stages of education in mathematical content, such as algebra, with the use of robotic devices adapted to children for the successful development of skills computational thinking (Alsina and Acosta, 2018). In Cuba, Matias et al. (2018) describe an experience in the course of Educational Robotics “Learn to play” taught to students of a k12 school with the mBlock software and the mBot kit. Specifically, the programming is described, the different components of the robot, such as: LEDs, buzzers, motors and ultrasonic sensors with which students must interact.
Since computer science is part of robotics manufacturing, it provides the right environment in which students gain computational thinking skills. For example, students demonstrate their abstraction and algorithmic thinking through the algorithm they create, since an algorithm is an abstraction of a process, broken down in orderly steps. These steps are created with sensor inputs, carry out the series of ordered steps and produce outputs to achieve the objective. Students who can create effective algorithms for their problems develop the ability to formulate the steps to effectively use the robotic tool (Bruni and Nisdeo, 2017).
This requires the skills to identify, analyse and implement the solution with the most effective and efficient steps. Experienced programmers can create effective but simple solutions. These skills must be supported by the right provisions, including persistence, tolerance, the ability to communicate and work effectively with others, and the ability to deal with open problems. These provisions can be obtained from your participation in the performance of robotics activities and the learning process. Through robotics manufacturing activities, students gain the necessary confidence to deal with complexity. Very often, students encounter complex problems while doing robotics, which helps students build confidence to persist.
One way to reduce the barrier for teachers and educators is to connect such learning activities with existing learning standards. However, simply taking robotics activities to classrooms does not automatically generate desirable learning outcomes. With the use of robotics kits there is generally no correct way to solve a challenge. Not having a correct answer but multiple ways of addressing a problem is an experience that many teachers are not familiar with. That is why more scientific research is needed in this regard, in terms of successful interventions that show evidence and good practices that serve as training and guides teachers.
Alsina, A., & Acosta, Y. (2018). Iniciación al álgebra en Educación Infantil a través del pensamiento computacional: Una experiencia sobre patrones con robots educativos programables. Revista Iberoamericana de Educación Matemática, 52, 218-235. https://bit.ly/2PC1hLt
Bell, P., Hoadley, C.M., & Linn, M.C. (2004). Design-based research. In M.C. Linn, E.A. Davis, & P. Bell (Eds.), Internet environments for science education (pp. 73-88). Mahwah, New Jersey, Lawrence Erlbaum Associates.
Blikstien, P. (2013). Digital fabrication and ‘making in education”: The democratization of invention. In J. W. H. C. Buching (Ed.), FabLabs: Of makers and inventors. Bielefeld, Germany: Transcript Publishers.
Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 Annual Meeting of the American Educational Research Association (AERA) (pp. 1-25). Vancouver, Canada.
Bruni, F., & Nisdeo, M. (2017). Educational robots and children’s imagery: A preliminary investigation in the first year of primary school. Research on Education and Media, 9(1), 37-44. https://doi.org/10.1515/rem-2017-0007
Chen, G., Shen, J., Barth-Cohen, L., Jiang, S., Huang, X., & Eltoukhy, M.M. (2017). Assessing elementary students’ computational thinking in everyday reasoning and robotics programming. Computers and Education, 109, 162-175. https://doi.org/10.1016/j.compedu.2017.03.001
Durak, H.Y., & Saritepeci, M. (2018). Analysis of the relation between computational thinking skills and various variables with the structural equation model. Computers & Education, 116, 191-202. https://doi.org/10.1016/j.compedu.2017.09.004
Fisher, R. (2005). Teaching children to think. Cheltenham: Nelson Thomes Ltd.
García-Peñalvo, F.J., Rees, A.M., Hughes, J., Jormanainen, I., Toivonen, T., & Vermeersch, J. (2016). A survey of resources for introducing coding into schools. Proceedings of the Fourth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM’16) (pp.19-26). Salamanca, Spain, November 2-4, 2016. New York: ACM. https://doi.org/10.1145/3012430.3012491
García-Valcárcel, A., y Caballero-González, Y.A. (2019). Robotics to develop computational thinking in early Childhood Education. Comunicar, n. 59, v. XXVII, 63-72. DOI: https://doi.org/10.3916/C59-2019-06
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
Juuti, K., & Lavonen, J. (2006). Design-Based Research in Science Education. Nordina 3(1), 54-68.
Krajcik, J., & Shin, N. (2015). Project-based learning. In K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (2nd ed., pp. 275–297). New York, NY: Cambridge University Press.
OECD (2005). Definition and selection of competencies (DeSeCo): Executive summary. Paris: OECD Publishing. Retrieved from http://www.oecd.org/pisa/35070367.pdf
Voogt, J. & Roblin, N.P. (2012). A comparative analysis of international frameworks for 21st century competences: Implications for national curriculum policies. Journal of Curriculum Studies, 44(3), 299-321. doi:10.1080/00220272.2012.668938
Wang, TH., Lim, K.Y.T., Lavonen, J., & Clark-Wilson, a. (2019). International Journal of Science and Mathematics Education, 17(Suppl 1):1. Doi: https://doi.org/10.1007/s10763-019-09999-8
Yiannoutsou, N., Nikitopoulou, S., Kynigos, C., Gueorguiev, I., & Fernandez, J. A. (2017). Activity plan template: a mediating tool for supporting learning design with robotics. In Robotics in Education (pp. 3-13). Springer, Cham.
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 knowledge society (EKS), (20). https://repositorio.grial.eu/handle/grial/1690