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Day 2: Computational Thinking | == Day 2: Computational Thinking == | ||
Recognizing and applying computing concepts in real-life situations is an important aspect of Computational Thinking. This makes CT a fruitful basis for collaboration of computing teachers with colleagues from other school subjects. | Recognizing and applying computing concepts in real-life situations is an important aspect of Computational Thinking. This makes CT a fruitful basis for collaboration of computing teachers with colleagues from other school subjects. | ||
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* construct examples of such opportunities by describing contexts taken from ‘non-ict’ school subjects, each with a challenging learning activity; | * construct examples of such opportunities by describing contexts taken from ‘non-ict’ school subjects, each with a challenging learning activity; | ||
* identify the CT-elements involved in the learning activities and discuss their pedagogical challenges: which success factors, pitfalls, good practices can be recognized? | * identify the CT-elements involved in the learning activities and discuss their pedagogical challenges: which success factors, pitfalls, good practices can be recognized? | ||
== Background == | |||
(from Chris Stephenson's talk) | |||
=== Thinking computationally === | |||
* What is the power/limit of human and computer intelligence? | |||
* How difficult is the problem? | |||
* How can the problem be solved? | |||
* How can technology be applied to the problem? | |||
* What computational strategies might be employed | |||
=== 'Doing' comptuationally === | |||
* Recognizing aspects of computation in the world around us | |||
* Applying computing tools and techniques to understand and reason about natural and artificial systems and processes | |||
* Thinking logically, algorithmically, and (at higher levels) abstractly and recursively | |||
=== Core concepts === | |||
* Data collection | |||
* Data analysis | |||
* Data representation | |||
* Problem decomposition | |||
* Abstraction | |||
* Algorithms and procedures | |||
* Parallelization | |||
* Modeling and simulation | |||
=== Core capacities === | |||
* Design solutions (abstraction, automation, algorithm creation, data collection and analysis) | |||
* Implement designs | |||
* Test and debug | |||
* Model/analyse | |||
* Reflect on practice | |||
* Recognize and move between levels of abstraction | |||
* Innovate, explore, and create across disciplines | |||
* Modeling and simulation | |||
* Problem solve in groups/teams | |||
* Employ diverse learning strategies | |||
=== Core dispositions === | |||
* Confidence in dealing with complexity | |||
* Persistence in working with difficult problems | |||
* Ability to handle ambiguity | |||
* Ability to deal with open-ended problems | |||
* Working with others to achieve a common goal or solution | |||
* Knowing one’s own strengths and weaknesses | |||
Revision as of 08:48, 16 September 2014
Day 2: Computational Thinking
Recognizing and applying computing concepts in real-life situations is an important aspect of Computational Thinking. This makes CT a fruitful basis for collaboration of computing teachers with colleagues from other school subjects.
In your working group,
- discuss opportunities to link computing themes to other school subjects;
- construct examples of such opportunities by describing contexts taken from ‘non-ict’ school subjects, each with a challenging learning activity;
- identify the CT-elements involved in the learning activities and discuss their pedagogical challenges: which success factors, pitfalls, good practices can be recognized?
Background
(from Chris Stephenson's talk)
Thinking computationally
- What is the power/limit of human and computer intelligence?
- How difficult is the problem?
- How can the problem be solved?
- How can technology be applied to the problem?
- What computational strategies might be employed
'Doing' comptuationally
- Recognizing aspects of computation in the world around us
- Applying computing tools and techniques to understand and reason about natural and artificial systems and processes
- Thinking logically, algorithmically, and (at higher levels) abstractly and recursively
Core concepts
- Data collection
- Data analysis
- Data representation
- Problem decomposition
- Abstraction
- Algorithms and procedures
- Parallelization
- Modeling and simulation
Core capacities
- Design solutions (abstraction, automation, algorithm creation, data collection and analysis)
- Implement designs
- Test and debug
- Model/analyse
- Reflect on practice
- Recognize and move between levels of abstraction
- Innovate, explore, and create across disciplines
- Modeling and simulation
- Problem solve in groups/teams
- Employ diverse learning strategies
Core dispositions
- Confidence in dealing with complexity
- Persistence in working with difficult problems
- Ability to handle ambiguity
- Ability to deal with open-ended problems
- Working with others to achieve a common goal or solution
- Knowing one’s own strengths and weaknesses