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En este glosario se recogen frases y palabras significativas para ayudar a entender los conceptos y procesos del aprendizaje.



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Pensamiento computacional

(Last edited: Monday, 6 April 2015, 11:05 AM)

pensamiento computacional. Este pensamiento fue definido recientemente por ISTE y CSTA como un proceso de solución de problemas que incluye, entre otros: analizar problemas, organizar y representar datos de manera lógica, automatizar soluciones mediante pensamiento algorítmico, usar abstracciones y modelos, comunicar procesos y resultados, reconocer patrones, y, generalizar y transferir

Wikipedia...

Computational Thinking is a problem-solving process that includes the following characteristics:[

  • Analyzing and logically organizing data
  • Data modeling, data abstractions, and simulations
  • Formulating problems such that computers may assist
  • Identifying, testing, and implementing possible solutions
  • Automating solutions via algorithmic thinking
  • Generalizing and applying this process to other problems

 

Problem-based learning is an important element of Science, Technology, Engineering, and Mathematics, also known as STEM, education in particular . The connection Computational Thinking makes in the context of problem solving processes is present in STEM focused classes. Characteristics of Computational Thinking reformulate problems by decomposing them into smaller and more manageable segments. These strategies enable students to transform complex problems into multiple step procedures that is not only easier to navigate through, but also provides an efficient way of thinking (Wing, 2006). In STEM education Computational Thinking is defined as a set of cognitive skills that let K-12 educators identify patterns, break apart complex problems into smaller steps , organize and create a series of steps to provide solutions, and build a representation of interacting data through simulations. Teachers in STEM focused classrooms that include Computational Thinking, allow students to practice problem-solving skills such as trial and error (Barr, et al, 2011).