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Computer Science Principles
  • Introduction
  • Overview
  • Course at a Glance
  • Course Exam Description
  • Create Performance Task
  • Reference Sheet
  • Resources
  • Big Idea 1
    • 1.1 Collaboration
    • 1.2 Program Function and Purpose
    • 1.3 Program Design and Development
    • 1.4 Identifying and Correcting Errors
  • Big Idea 2
    • 2.1 Binary Numbers
    • 2.2 Data Compression
    • 2.3 Extracting Information from Data
    • 2.4 Using Programs with Data
  • Big Idea 3
    • 3.1 Variables and Assignments
    • 3.2 Data Abstraction
    • 3.3 Mathematical Expressions
    • 3.4 Strings
    • 3.5 Boolean Expression
    • 3.6 Conditionals
    • 3.7 Nested Conditionals
    • 3.8 Iteration
    • 3.9 Developing Algorithms
    • 3.10 Lists
    • 3.11 Binary Search
    • 3.12 Calling Procedures
    • 3.13 Developing Procedures
    • 3.14 Libraries
    • 3.15 Random Values
    • 3.16 Simulations
    • 3.17 Algorithmic Efficiency
    • 3.18 Undecidable Problems
  • Big Idea 4
    • 4.1 The Internet
    • 4.2 Fault Tolerant
    • 4.3 Parallel and Distributed Computing
  • Big Idea 5
    • 5.1 Beneficial and Harmful Effects
    • 5.2 Digital Divide
    • 5.3 Computing Bias
    • 5.4 Crowdsourcing
    • 5.5 Legal and Ethical Concerns
    • 5.6 Safe Computing
  • Code
    • Week 10
    • Week 11
    • Week 12
    • Week 13
    • Week 14
    • Week 15
    • Week 16
    • Week 17
    • Week 18
    • Week 19
    • Week 20
    • Week 21
    • Week 22
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Introduction

Based on the Understanding by Design® (Wiggins and McTighe) model, the AP Computer Science Principles Course and Exam Description provides a clear and detailed description of the course requirements necessary for student success. The course is designed to be equivalent to a first-semester introductory college computing course. The major areas of study in the course are organized around big ideas that encompass ideas foundational to studying computer science.

The AP Computer Science Principles course framework is organized into five big ideas.

Big Idea

Exam Weighting

Big Idea 1: Creative Development

10%–13%

Big Idea 2: Data

17%–22%

Big Idea 3: Algorithms and Programming

30%–35%

Big Idea 4: Computer Systems and Networks

11%–15%

Big Idea 5: Impact of Computing

21%–26%

Computational Thinking Practices

The AP Computer Science Principles course framework included in the course and exam description outlines distinct skills from computational thinking practices that students should practice and develop throughout the year—skills that will help them learn to think and act like computer scientists. Emphasis is placed on creativity and collaboration as pedagogical strategies to be used to develop a diverse, appealing, and inclusive classroom environment.

Computational Thinking Practice

Description

Exam Weighting (Multiple-Choice Section)

1. Computational Solution Design

Design and evaluate computational solutions for a purpose.

18%–25%

2. Algorithms and Program Development

Develop and implement algorithms.

20%–28%

3. Abstraction in Program Development

Develop programs that incorporate abstractions.

7%–12%

4. Code Analysis

Evaluate and test algorithms and programs.

12%–19%

5. Computing Innovations

Investigate computing innovations.

28%–33%

6. Responsible Computing

Contribute to an inclusive, safe, collaborative, and ethical computing culture.

Not assessed

This website is written by Mr. Cheng, a high school computer science teacher and adjunct professor. He can be reached at mcheng @ appinventor.net He is available for consulting and tutoring.

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