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)