Robótica DIYpensamiento computacional para mejorar la resolución de problemas

  1. Beatriz Ortega Ruipérez 1
  2. Mikel Asensio 2
  1. 1 Universidad Internacional de La Rioja
    info

    Universidad Internacional de La Rioja

    Logroño, España

    ROR https://ror.org/029gnnp81

  2. 2 Universidad Autónoma de Madrid
    info

    Universidad Autónoma de Madrid

    Madrid, España

    ROR https://ror.org/01cby8j38

Zeitschrift:
RELATEC: Revista Latinoamericana de Tecnología Educativa

ISSN: 1695-288X

Datum der Publikation: 2018

Ausgabe: 17

Nummer: 2

Seiten: 129-143

Art: Artikel

DOI: 10.17398/1695-288X.17.2.129 DIALNET GOOGLE SCHOLAR lock_openOpen Access editor

Andere Publikationen in: RELATEC: Revista Latinoamericana de Tecnología Educativa

Ziele für nachhaltige Entwicklung

Zusammenfassung

Programming is being included in educational curricula around the world to develop computational thinking. However, there is no consensus on what processes this thought implies, nor on how to intervene and evaluate its development. Therefore, the objective is to propose a teaching strategy for programming and robotics, which really develops this thinking and can be applied to solve problems, from a maker perspective that facilitates the transfer of knowledge to real contexts. To this end, a robotics course has been taught, insisting on the cognitive processes of this thinking that are commonly used in problem solving (abstraction, data processing, creation of an algorithm), and encouraging the use of a computational strategy, using the processes of this thought not employed in problem solving (decomposition of the problem, automation, parallelism, simulation). To measure it, digital tests have been created based on the multiple complex-systems approach, used in PISA 2012. The results indicate that computational thinking is applied more easily to the execution of the algorithm than to the representation of the problem. This finding allows us to establish a programming learning process that facilitates the development of computational thinking, to solve any problem by applying a computational strategy: focusing first on applying this strategy to the creation of the algorithm and then to the representation of the problem

Bibliographische Referenzen

  • Asenjo, E. y Asensio, M. (en prensa). The force under suspicion: building a perceived interactivity scale in museums. Journal on Computing and Cultural Heritage_JOCCH
  • Asensio, M. y Asenjo, E. (Eds.) (2011). Lazos de Luz Azul. Museos y Tecnologías 1, 2 y 3.0. Barcelona: Editorial Universitat Oberta de Catalunya.
  • Brennan, K. y Resnick, M. (2012) New frameworks for studying and assessing the development of computational thinking. Proceedings of the 2012 annual meeting of the American Educational Research Association. Vancouver, Canada
  • Chiu, T. K. F., y Churchill, D. (2015). Exploring the characteristics of an optimal design of digital materials for concept learning in mathematics: Multimedia learning and variation theory. Computers y Education, 82, 280–291. http://dx.doi.org/10.1016/j.compedu.2014.12.001
  • CSTA y ISTE (2011) Computational Thinking Leadership Toolkit, First edition. Computer Science Teachers Association (CSTA) y International Society for Technology in Education (ISTE). https://goo.gl/syFwSF
  • Greiff, S., Wüstenberg, S., y Funke, J. (2012). Dynamic problem solving: A new assessment perspective. Applied Psychological Measurement, 36(3), 189-213. http://dx.doi.org/10.1177/0146621612439620
  • Herde, C. N., Wüstenberg, S., y Greiff, S. (2016). Assessment of complex problem solving: What we know and what we don’t know. Applied Measurement in Education, 29(4), 265-277. http://dx.doi.org/10.1080/08957347.2016.1209208
  • Hmelo-Silver, C.E. (2012) International perspectives on problem-based learning: context, cultures, challenges and adaptations. Interdisciplinary Journal of Problem-Based Learning, 6, 10-15. http://dx.doi.org/10.7771/1541-5015.1310
  • Holyoak, K. J. y Morrison, R. G. (Eds.)(2012) The oxford handbook of thinking and reasoning. Oxford University Press. http://dx.doi.org/10.1093/oxfordhb/9780199734689.001.0001
  • Jiménez-Rasgado, G. (2018). La programación como fuente motivadora para la construcción del conocimiento y el desarrollo de habilidades de pensamiento. International Journal of Studies in Educational Systems, 2(8), 269-278.
  • Kafai, Y. B. y Burke, Q. (2017) Computational Participation: Teaching Kids to Create and Connect Through Code. In P.J. Rich, C.B. Hodges (eds.), Emerging Research, Practice, and Policy on Computational Thinking, Educational Communications and Technology: Issues and Innovations. Springer.
  • Koh, K. H., Nickerson, H., Basawapatna, A., y Repenning, A. (2014, June). Early validation of computational thinking pattern analysis. In Proceedings of the 2014 conference on Innovation y technology in computer science education, 213-218. ACM. http://dx.doi.org/10.1145/2591708.2591724
  • Lu, Y., Bridges, E.M. y Hmelo-Silver, C.E. (2014) Problem-Based Learning. In: Sawyer, R.K. (Ed.) The Learning Sciences, pp. 298-318. N.Y.: Cambridge University Press.
  • Lye, S. Y., y Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12? Computers in Human Behavior, 41, 51-61. http://dx.doi.org/10.1016/j.chb.2014.09.012
  • Marais, C., y Bradshaw, K. (2015). Problem-solving ability of first year CS students: A case study and intervention. In Proceedings of the 44th Conference of the Southern African Computers Lecturers' Association.
  • Mayer, RE. y Estrella, G. (2014). Benefits of emotional design in multimedia instruction. Learning and Instruction, 33, 12-18. http://dx.doi.org/10.1016/j.learninstruc.2014.02.004
  • Moreno-Leon, J., Robles, G., y Roman-Gonzalez, M. (2015). Dr. Scratch: Análisis Automático de Proyectos Scratch para Evaluar y Fomentar el Pensamiento Computacional. Revista de Educación a Distancia, (46).
  • Muñoz, R., Barcelos, T. S., Villarroel, R., y Silveira, I. F. (2017). Using Scratch to Support Programming Fundamentals. International Journal on Computational Thinking, 1(1), 68-78. http://dx.doi.org/10.14210/ijcthink.v1.n1.p68
  • National Research Council (2010) Report of a Workshop on the Scope and Nature of Computational Thinking. Washington, DC: The National Academies Press
  • OECD (2010). PISA 2012 field trial: problem solving framework. Paris: OECD
  • Ortega-Ruipérez, B. (2018) Pensamiento computacional y resolución de problemas (Tesis doctoral). Universidad Autónoma de Madrid, Madrid. https://goo.gl/ortqFs
  • Park, S. Y., Song, K. S., y Kim, S. (2015). EEG Analysis for Computational Thinking based Education Effect on the Learners’Cognitive Load. Proceedings of the Applied Computer and Applied Computational Science (ACACOS’15), 23-25. Kuala Lumpur, Malaysia.
  • Ritchhart, R. y Perkins, D. N. (2005) Learning to Think: The Challenges of Teaching Thinking. En Holyoak, K. J. y Morrison, R. G. (Eds.) The Cambridge Handbook of Thinking and Reasoning, pp. 775-802. Cambridge University Press.
  • Robles, G., Moreno-León, J., Aivaloglou, E., y Hermans, F. (2017). Software clones in Scratch projects: On the presence of copy-and-paste in Computational Thinking learning. In Software Clones (IWSC), 2017 IEEE 11th International Workshop on (pp. 1-7). Austria.
  • Sáez-López, J. M. y Cózar-Gutiérrez, R. (2017) Pensamiento computacional y programación visual por bloques en el aula de Primaria. Educar, 53(1), 129-146. http://dx.doi.org/10.5565/rev/educar.841
  • Santacana, J. Asensio, M. y Llonch, N. (Eds.) (2018). App, arqueología & m-learning. Reconstruir, restituir interpretar y evaluar APP. Barcelona: Rafael Dalmau.
  • Selby, C. y Woollard, J. (2013) Computational thinking: the developing definition. University of Southampton (E-prints). https://goo.gl/sKdd9G
  • Shute, V. J., Sun, C., y Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142-158. https://doi.org/10.1016/j.edurev.2017.09.003
  • Stahl, G., Koschmann, T. y Suthers, D. (2014). Computer-Supported Collaborative Learning. In: Sawyer, R.K. (Ed.) The Learning Sciences, pp. 479-500. N.Y.: Cambridge University Press.
  • Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and instruction, 4(4), 295-312.
  • Valverde-Berrocoso, J.; Fernández-Sánchez, M. y Garrido-Arroyo, M. (2015) El pensamiento computacional y las nuevas ecologías del aprendizaje. Revista de Educación a Distancia. (46) http://dx.doi.org/10.6018/red/46/3
  • Wing, J. M. (2006) Computational thinking. Communications of the ACM, 49, 33-35. http://dx.doi.org/10.1145/1118178.1118215
  • Wing, J. M. (2008) Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 366, 3717-3725. The Royal Society.
  • Wing, J. M. (2011) Research Notebook: Computational Thinking: What and Why? The Link. Pittsburgh, PA: Carneige Mellon. Recuperado de https://www.cs.cmu.edu/link/research-notebook-computational-thinkingwhat-and-why
  • Wing, J. M. (2014). Computational thinking benefits society. Social Issues in Computing. New York: Academic Press.
  • Wing, J.M. (2017). Computational thinking’s influence on research and education for all. Italian Journal of Educational Technology, 25(2), 7-14. http://dx.doi.org/10.17471/2499-4324/922
  • Zapata-Ros, M. (2015) Pensamiento computacional: Una nueva alfabetización digital. Revista de Educación a Distancia. Universidad de Murcia. http://dx.doi.org/10.6018/red/46/4
Fuente de los datos: Dialnet