Computational thinking

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Current revision as of 11:20, 21 September 2019

Know what you would make with computers if you could. (Self in relation to world)

• Develop a personal style of pseudocode.
• Collaborate with computers as alien intelligences, utilizing digital logic.

See the world outside of class in terms of computing and its precepts. (Computing in relation to world)

• creating algorithms
• problem solving and heuristics
• Use pseudocode as a way to write and think about algorithms, and to translate between algorithms and programs.
• requirements and specification of non-programming problems, abstraction
• think like a computer, think like a human, choose and adapt modes of thought.
• Apply computer-based ways of problem-solving, representing, explaining, mediating reality, answering questions, communicating, etc. outside the context of computers.
• Apply collaboration with alien strengths and weaknesses of computers to cooperating with unique humans.
• abstract search space
• constructivist theory of learning, Origins of Intelligence by Piaget

Participate in computing as a discipline and course of study. (Computing in relation to self)

• Describe CS as a degree program— what it is, what courses there are— and draw distinctions between subfields.
• Describe CS careers.
• Describe major fields of study in CS with some familiarity.
  • programming languages
  • databases
  • graphics
  • AI, robotics, and machine learning
  • theory
  • data science
  • operating systems
  • networking
  • architecture
• Establishing a Kuhnic paradigm
  • History of mathematicians, Lovelace & Babbage, ENIAC, Harvard Mark I, computing scientists, discoveries
  • Combinatorial puzzles (river crossing, tower of Hanoi, knights/knaves)
  • scientific method, Philosophy of science, Kant, Wittgenstein, Kuhn
  • affective domain learning of code of conduct
• Quine— naming, quoting, quantifying, use/mention
• represent program as a graph with control flow edges (flow chart) or data flow edges (dataflow programming, logic circuit diagrams)
• time as an abstraction— clocks, timestamps, runtime, and ordering of events
• Information theory
  • bit as unit of information, logarithmic measure of surprise
  • isomorphism
  • coding as bitstrings (dichotomy, false dichotomy, powers of two, 20 questions, dice, coins, Venn diagrams, tables, graphs, etc.)
    • letters (game tree/decision tree metaphor, divide-by-2 strategy, Huffman coding)
    • Natural numbers (binary, other bases, abacus metaphor) (number theory)
    • numeral systems
    • Integers (offset, sign/magnitude, BCD, one’s complement, two’s complement)
    • colors, playing cards, etc.
    • raster images, vector images
    • audio
    • video
    • memory/arrays, linked lists