Difference between revisions of "Computational Thinking"
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{{WikiEntry|key=Computational thinking|qCode=5157342}} is a skill set that applies algorithmic procedures to process data content. This is the foundation of [[data-centric knowledge]]. To acquire knowledge in the area of computational thinking, we suggest this field of knowledge to be first connected with the source of data, which is the [[cognitive foundation]] of data acquisition, and then, think about its use cases in a broader context, which is the [[industrial/societal | {{WikiEntry|key=Computational thinking|qCode=5157342}} is a skill set that applies algorithmic procedures to process data content. This is the foundation of [[data-centric knowledge]]. To acquire knowledge in the area of computational thinking, we suggest this field of knowledge to be first connected with the source of data, which is the [[cognitive foundation]] of data acquisition, and then, think about its use cases in a broader context, which is the [[industrial/societal frontiers]] of computational instrumentation and application consequences. In other words, [[computational thinking]] cannot exists as an isolated field, like the way pure mathematics and pure computational theory are being taught today. It needs to subsume these content, but relate these content to the contextualized data sources in terms of [[cognitive foundation]], and always consider the applicability and feasibility of computational instrument in societal and industrial contexts. At the end, computation is not just a mental skill, it requires instrumentation and relevant socio-technical operational environment to make sense to people and make impacts on the environment. | ||
Therefore, we suggest to teach computational thinking in the following way: | Therefore, we suggest to teach computational thinking in the following way: | ||
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Present universal abstraction, logic, arithmetic, representational theory, topology, and algorithmic thinking to learners at all levels. Make explict connections between the skills of counting numbers, assessing proportions, encoding relative positions in a data-centric, relational manner. So that students will acquire the insight of how great mathematicians have developed over the years to compress data into asserted information carrying symbols, and even derive actionable knowledge. | Present universal abstraction, logic, arithmetic, representational theory, topology, and algorithmic thinking to learners at all levels. Make explict connections between the skills of counting numbers, assessing proportions, encoding relative positions in a data-centric, relational manner. So that students will acquire the insight of how great mathematicians have developed over the years to compress data into asserted information carrying symbols, and even derive actionable knowledge. | ||
=DevOps and Security | =DevOps and Data Security= | ||
Modern computing is resource intensive and lives on a globalized communication network. Learning computational thinking involves the awareness and access to data and computing resources that may extend across the entire galaxy, even the universe. Therefore, knowing how to get access to proper data sources, publish onto these data sharing platforms, while ensuring one's own data assets can be protected, would be the essential skill set to be acquired in the | Modern computing is resource intensive and lives on a globalized communication network. Learning computational thinking involves the awareness and access to data and computing resources that may extend across the entire galaxy, even the universe. Therefore, knowing how to get access to proper data sources, publish onto these data sharing platforms, while ensuring one's own data assets can be protected, would be the essential skill set to be acquired in the knowledge content focused on computational thinking. | ||
[[Category:Cognitive Foundation]] | |||
Latest revision as of 08:20, 21 March 2022
Computational thinking(Q5157342) is a skill set that applies algorithmic procedures to process data content. This is the foundation of data-centric knowledge. To acquire knowledge in the area of computational thinking, we suggest this field of knowledge to be first connected with the source of data, which is the cognitive foundation of data acquisition, and then, think about its use cases in a broader context, which is the industrial/societal frontiers of computational instrumentation and application consequences. In other words, computational thinking cannot exists as an isolated field, like the way pure mathematics and pure computational theory are being taught today. It needs to subsume these content, but relate these content to the contextualized data sources in terms of cognitive foundation, and always consider the applicability and feasibility of computational instrument in societal and industrial contexts. At the end, computation is not just a mental skill, it requires instrumentation and relevant socio-technical operational environment to make sense to people and make impacts on the environment.
Therefore, we suggest to teach computational thinking in the following way:
User Experience Design
This is to be learned with activities and knowledge content embedded in cognitive foundation curricula. Extensively leverage the power of modern web browsers, to demonstrate sound, words, pictures, 3D interactive models, are directly accessible on their personal computing devices. Having exposed to these computable functionalities of these devices, will significantly change their perception of what they can do with their own devices.
User Experience provisioned through Operating Systems
All students at all ages should be regularly informed of the latest features in modern, network operating systems. Every new release of features, or new devices, will change the operating modes and security profile of everyone's data assets. Therefore, having constant reminders or information sessions on how to use new features should be organized in the language framework of operating systems to help students see the implications of new data manipulation features offered by various device/software vendors.
Data Structures and Algorithms
Present universal abstraction, logic, arithmetic, representational theory, topology, and algorithmic thinking to learners at all levels. Make explict connections between the skills of counting numbers, assessing proportions, encoding relative positions in a data-centric, relational manner. So that students will acquire the insight of how great mathematicians have developed over the years to compress data into asserted information carrying symbols, and even derive actionable knowledge.
DevOps and Data Security
Modern computing is resource intensive and lives on a globalized communication network. Learning computational thinking involves the awareness and access to data and computing resources that may extend across the entire galaxy, even the universe. Therefore, knowing how to get access to proper data sources, publish onto these data sharing platforms, while ensuring one's own data assets can be protected, would be the essential skill set to be acquired in the knowledge content focused on computational thinking.