In its simplest sense, computational thinking is considered as a series of specific skills that help programmers to do their homework, but that are also useful to people in their professional life and in their personal life as a way to organize the resolution on 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, the digital culture in the knowledge society.We have seen how Bayesian probability is used in epidemiology models to determine models for the evolution of data on contagion and deaths in COVID and in natural language processing.We could also see it in a multitude of 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 as diverse as linguistic corpus, astronomical maps, add functionalities to the practice of the magnetic resonance imaging, or to card, online or smartphone purchasing habits. 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 and more relevant 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 ordinary professional or vital practice to deal with life and the world of work, as has been what we have called computational thinking until now, but as a preparation for basic research and research methodology in almost all disciplines. Because, thus defined, computational thinking is influencing research in almost all areas, both in the sciences and in the humanities. An instruction focused on this component of computational thinking, Bayesian thinking, of including it at an early stage, in Secondary (K-12), including the inverse probability formula, would allow, based on Merrill’s First principles of learning, and in particular in the activation principle, activate these learning as very valuable and very complex components in a later stage of professional or research activity, or in the training passed, undergraduate and postgraduate degrees, of these professions or that train for these activities and professions.
More confidently uncertain? Teaching learners to apply Bayesian methods to make sense of scientific phenomena
