The bayesian thinking, a pervasive computational thinking
Abstract
In its simplest sense, computational thinking is considered as a series of specific skills that programmers use to do their homework. They 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 that surrounds them.
In a more elaborate scheme, this complex of skills constitutes a new literacy --- or the most substantial part of it --- and an inculturation to deal with a new culture: digital culture in the knowledge society.
We have seen how Bayesian Probability is used in epidemiology models to determine models of data evolution on contagion and deaths in COVID. We have also seen it in natural language processing.
We could also see it in many cases in the most varied scientific and process analysis fields. In this way, with the automation of Bayesian methods and the use of probabilistic graphical models, it is possible to identify patterns and anomalies in voluminous data sets in fields. Fields as diverse as linguistic corpus, astronomical maps, adding functionalities to the practice of magnetic resonance imaging, or to the habits of buying with cards, online or smartphones.
In this new way of proceeding, big data analysis and Bayesian theory are associated..
If we consider that Bayesian thinking (this way of proceeding) as one more element of computational thinking, then, to what has been said on previous occasions, we must now add the idea of generalized computational thinking, which goes beyond education
It is no longer about aspects purely associated with professional or vital practice to deal with life and the world of work, but as a preparation for basic research and for a research methodology in almost all disciplines. Thus defined, computational thinking is influencing research in almost all areas, both in the sciences and the humanities.
An instruction focused on this component of computational thinking, or including it, at an early stage, in Secondary, would allow to activate these learnings as very valuable and very complex components at a later stage. In professional or research activity, in the training phase, undergraduate and postgraduate degrees, of these professions. Those that train for these activities and professions.
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References
Abraham, D., Blum, A. & Sandholm, T. 2007 Clearing algorithms for barter exchange markets: enabling nationwide kidney exchanges. In Proc. 8th ACM Conf. on Electronic Commerce, pp. 295–304. New York, NY: Association for Computing Machinery.
Barrow, B. (2019 Apr) Bayesian Analysis in Natural Language Processing: Cohen. https://linguistlist.org/issues/30/30-1843/
Bloom, B. S. (1984a). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational researcher, 13(6), 4-16. https://www.jstor.org/stable/pdf/1175554.pdf
Bloom, B. (May 68). Learning for Mastery. Instruction and Curriculum. https://files.eric.ed.gov/fulltext/ED053419.pdf
Bloom, B. S. (1976). Human characteristics and school learning. McGraw-Hill. Página 59 de 63
Bloom, B. (1984b). The 2 Sigma Problem: The Search for Methods of Group Instruction as effective as One-to-One Tutoring, Educational Researcher, 13:6 (4-16). https://inst.eecs.berkeley.edu//~cs375/sp15/resources/Bloom_The2SigmaProblem.pdf
Bocconi, S., Chioccariello, A., Dettori, G., Ferrari, A., Engelhardt, K., Kampylis, P., & Punie, Y. (2016). Developing computational thinking in compulsory education. European Commission, JRC Science for Policy Report, 68.
Bruce, V. (2005). About Face. http://www.inf.ed.ac.uk/research/programmes/comp-think/slides/Bruce.pdf
Bundy, A. 2007 Computational thinking is pervasive. J. Scient. Pract. Comput.1, 67–69. https://www.inf.ed.ac.uk/publications/online/1245.pdf
Bundy, A. (2012) COMPUTATIONAL THINKING SEMINARS. SCHOOL OF INFORMATICS. University of Edimburgh. https://www-inf-ed-ac-uk.translate.goog/research/programmes/comp-think/previous.html?_x_tr_sch=http&_x_tr_sl=en&_x_tr_tl=es&_x_tr_hl=es&_x_tr_pto=nui,op
Cohen, S. (2019). Bayesian analysis in natural language processing. Synthesis Lectures on Human Language Technologies, 12(1), 1-343.
Coll, C. (2019). Presentación y prólogo del libro" El pensamiento computacional. Análisis de una competencia clave". Revista de Educación a Distancia, 19. https://revistas.um.es/red/article/view/395281
Drury, B.(2019 Aug). Bayesian Analysis in Natural Language Processing, in Review: Computational Linguistics; Text/Corpus Linguistics: Cohen (2019). https://linguistlist.org/issues/30/30-4380/
Fisher, J. & Henzinger, T. A. 2007 Executable cell biology. Nat. Biotechnol. 25, 1239–1249. (doi:10.1038/nbt1356)
Gagné, R. M. (1965). The conditions of learning and theory of instruction ( 1st ed.). New York, NY: Holt, Rinehart & Winston.
Gagné, R. M., & Briggs, L. J. (1974). The principles of instructional design ( 1st ed.). New York, NY: Holt.
Gagné, R. M. (1985). The conditions of learning and theory of instruction ( 4th ed.). New York, NY: Holt, Rinehart & Winston. Gagne, R. M.,& Medsker, K. L. (1996). The conditions of learning: Training applications. Fort Worth, TX: Harcourt Brace College Publishers.
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
Merrill, M. D. (2002a). First principles of instruction. Educational technology research and development, 50(3), 43-59. http://csapoer.pbworks.com/f/First+Principles+of+Instruction+(Merrill,+2002).pdf
Merrill, M. D. (2012). First principles of instruction. John Wiley & Sons. Merrill, M. D. (1991). Constructivism and instructional design. Educational technology, 31(5), 45-53.
Merrill, M. D. (2002b). First principles of instruction. Educational Technology Research and Development, 50(3), 43-59. Instructional-Design Theories and Models, Volume III: Building a Common Knowledge Base.
Merrill, M. D. (2009). First Principles of Instruction. In C. M. Reigeluth & A. Carr (Eds.), Instructional Design Theories and Models: Building a Common Knowledge Base (Vol. III). New York: Routledge Publishers.
Raja, T. (2014). We Can Code It! Why computer literacy is key to winning the 21st century. Mother Jones, June
Shields, P. M., & Rangarajan, N. (2013). A playbook for research methods: Integrating conceptual frameworks and project management. New Forums Press.
Roig-Vila, R., & Moreno-Isac, V. (2020). El pensamiento computacional en Educación. Análisis bibliométrico y temático. Revista de Educación a Distancia, 20(63). https://revistas.um.es/red/article/view/402621
Rosenberg, J. (2020). More confidently uncertain? Teaching learners to apply Bayesian methods to make sense of scientific phenomena. https://joshuamrosenberg.com/publications/rosenberg-2020-icls-ecw-copyright.pdf y https://edarxiv.org/7rptw/download
Stross, R. (31 de marzo de 2012) Computer Science for the Rest of Us. https://www.nytimes.com/2012/04/01/business/computer-science-for-non-majors-takes-many-forms.html
Verity, R., Okell, L. C., Dorigatti, I., Winskill, P., Whittaker, C., Imai, N., ... & Ferguson, N. M. (2020). Estimates of the severity of coronavirus disease 2019: a model-based analysis. The Lancet infectious diseases, 20(6), 669-677. https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30243-7/fulltext y https://www.thelancet.com/action/showPdf?pii=S1473-3099%2820%2930243-7
Wing, J.M. (2006) Computational thinking. it represents a universally applicable attitude and skill set everyone, not just computer scientists, would be eager to learn and use. Commun. ACM 49(3). https://doi.org/10.1109/vlhcc.2011.6070404
Wing, J.M. (July 2008) Computational thinking and thinking about computing. The Royal Society Publishing. https://doi.org/10.1098/rsta.2008.0118 https://royalsocietypublishing.org/doi/10.1098/rsta.2008.0118 https://royalsocietypublishing.org/doi/pdf/10.1098/rsta.2008.0118
Zapata-Ros, M. (2014) https://red.hypotheses.org/776
Zapata-Ros, M. (2015). Pensamiento computacional: Una nueva alfabetización digital. RED. Revista de Educación a Distancia, (46). Recuperado de: https://www.um.es/ead/red/46/
Zapata Ros, M. & Pérez Paredes, P. (2018). El pensamiento computacional, análisis de una competencia clave. New York: Create Space Independent Publishing. https://www.amazon.es/pensamiento-computacional-analisis-competencia-clave/dp/1718987730
Zapata-Ros, M. (Enero 2018) https://red.hypotheses.org/1079
Zapata-Ros, M. (January 2019) Pensamiento computacional desconectado. http://dx.doi.org/10.13140/RG.2.2.12945.48481
Zapata-Ros, M. (Agosto 2020). El pensamiento computacional, una cuarta competencia clave planteada por la nueva alfabetización (II). Una nueva línea: computational thinking everywhere, pervasive computational thinking y el pensamiento bayesiano. RED de Hypotheses. https://red.hypotheses.org/2123
Zapata-Ros, M. (febrero de 2021 ). Capítulo 5. La evaluación en la educación de la pandemia y después de la pandemia. DOI: 10.13140/RG.2.2.21911.29605 http://dx.doi.org/10.13140/RG.2.2.21911.29605 , y https://www.researchgate.net/publication/349212529_Capitulo_5_La_evaluacion_en_la_educacion_de_la_pandemia_y_despues_de_la_pandemia?channel=doi&linkId=60250c0e92851c4ed563a7be&showFulltext=true
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