一、運算思維 Computational Thinking (CT)
運算思維適用於生活中每一個領域的學習，相關的名詞有不同的定義。
將運算思維應用在電腦科學:
運算思維=科技解決問題的歷程
將運算思維架構應用在機器人教學
1. 解構(Decomposition) - 拆解問題
ex: 解析掃地機器人功能(自走、避障)
2. 模式識別(Pattern Recognition)- 找出異同、識別、歸納模式
ex: 自走-馬達，避障-紅外光
3. 模式歸納(Pattern Generalization)- 抽象化、探索模式的規律性
ex: 馬達、紅外光線-程式功能、規律性
4. 設計算法設計(Algorithm Design)-找出解決方法
ex: 掃地機器人執行流程
二、CSTA和ISTE (2011)定義運算思維技能如下表1所示:
三、 Google定義運算思維概念相關的11個專有名詞
Abstraction is identifying and extracting relevant information to define main idea(s)
Ex:摘要行事曆中每週、每天、時間的事情。
Algorithm Design is creating an ordered series of instructions for solving similar problems or for doing a task
Ex:數學四則運算、刷牙順序。
Automation is having computers or machines do repetitive tasks
Ex:計算全校學生的段考成績、統計出缺勤。
Data Collection is gathering information
Ex:收集全班同學的生日、性別、星座。
Data Analysis is making sense of data by finding patterns or developing insights
Ex:統計每個星座或每月生日的人數。
Data Representation is depicting and organizing data in appropriate graphs, charts, words, or images
Ex:利用圖表說明出席率與星座的關聯性。
Decomposition is breaking down data, processes, or problems into smaller, manageable parts
Ex:解析(3,2)的意思，3代表x座標往右移3單位，2代表y座標往上移2單位。
Ex:解析256.37 as follows: 2*102+5*101+6*100+3*10-1+7*10-2
Parallelization is simultaneous processing of smaller tasks from a larger task to more efficiently reach a common goal
Ex:同步化，不同組別執行不同的任務，但是學習目標相同。
Pattern Generalization is creating models, rules, principles, or theories of observed patterns to test predicted outcomes
Ex:類化，相同功能程式，用不同的表達方式。
Pattern Recognition is observing patterns, trends, and regularities in data
Ex:找出規則或趨勢。
Simulation is developing a model to imitate real-world processes
Ex:發展擬真模組。
Reference: More reference docs, lesson plans, and demonstrations can be found on Google’s Exploring Computational Thinking website (g.co/exploringCT)