Page 1: Fundamentals of Algorithms and Computational Thinking
This comprehensive page covers the essential elements of algorithmic thinking and computational problem-solving approaches. The content explores flowchart representations, computational thinking concepts, sorting algorithms, searching methods, and various programming fundamentals.
Definition: Computational thinking is defined as the use of computers to solve problems through algorithm development and implementation of key concepts like abstraction, decomposition, and algorithmic thinking.
Example: In creating a chess program, abstraction is demonstrated by representing the board as arrays and pieces as objects with positions, while omitting unnecessary visual elements.
Highlight: The page introduces several sorting and searching algorithms, including bubble sort, merge sort, insertion sort, linear search, and binary search, each with detailed step-by-step implementations.
Vocabulary:
- Abstraction: Using symbols and variables to represent real-world problems while removing unnecessary elements
- Decomposition: Breaking down complex problems into smaller, manageable parts
- Algorithm: A series of steps to perform an action or solve a problem
- Pseudocode: Simplified language used to design algorithms
Quote: "There are usually several different approaches, and not one single right way to do this" - referring to problem decomposition.
The page also covers common error types (syntax and logic errors), trace tables for algorithm testing, and exam reference language conventions. Flowchart representations are explained with different shapes representing various functions such as start/end points, input/output operations, processes, and decision points. The content concludes with practical guidance on completing and correcting algorithms, emphasizing the importance of systematic approach to problem-solving in computer science.
