This syllabus outlines the course content, assessment methods, and expectations for AP Computer Science Principles (CSP). This course aims to provide a broad, foundational understanding of computer science, emphasizing computational thinking, problem-solving, and the societal impacts of computing. We will explore a range of topics, fostering creativity and critical analysis while developing essential programming skills.
Instructor: [Your Name/Teacher's Name] Email: [Your Email Address] Website/Learning Management System: [Link to your website or LMS]
Course Overview
AP CSP is designed to be an introductory course, welcoming students with diverse backgrounds and levels of prior experience. The curriculum focuses on developing computational thinking skills applicable across various fields, rather than intensive programming expertise. Students will learn to:
- Develop Computational Thinking Skills: This includes problem decomposition, pattern recognition, abstraction, algorithms, and the ability to represent data effectively.
- Explore Programming Concepts: We will use a block-based visual programming language (like App Lab or Blockly) and potentially a text-based language (like Python) to build simple programs, emphasizing the underlying logic and structure.
- Understand the Impact of Computing: This involves analyzing the societal, ethical, and cultural effects of technology, including issues of privacy, security, bias, and accessibility.
- Communicate about Computing: Students will learn to articulate their ideas, both verbally and in writing, using appropriate terminology and examples.
- Collaborate and Work Effectively in Teams: Many assignments will involve teamwork, developing essential collaboration skills.
Course Content
The course will cover the following key topics, aligned with the College Board's AP CSP framework:
Unit 1: Creative Development
- Introduction to Computer Science: What is computer science? Its history and evolution. The role of algorithms and abstraction.
- Computational Thinking: Problem-solving strategies, pattern recognition, and decomposition. Algorithm design and representation.
- Data Representation: Binary numbers, data types, and encoding. The relationship between data and information.
- Creative Expression: Using programming to create visual and interactive projects. Exploring design principles.
Unit 2: Data and Information
- Data Abstraction: Representing complex data in simplified forms.
- Data Structures: Arrays, lists, and other ways to organize data.
- Databases: Introduction to database management systems and data querying.
- Data Analysis: Basic statistical analysis and visualization of data.
- Big Data: Understanding the challenges and opportunities of managing and analyzing large datasets.
Unit 3: Algorithms
- Algorithm Design: Developing step-by-step instructions to solve problems.
- Algorithm Efficiency: Analyzing the performance of different algorithms.
- Algorithm Types: Searching, sorting, and other common algorithm categories.
- Pseudocode and Flowcharts: Representing algorithms visually.
Unit 4: Programming
- Introduction to Programming: Learning a programming language (block-based and/or text-based).
- Control Structures: Conditional statements, loops, and functions.
- Debugging and Testing: Identifying and fixing errors in code.
- Program Design: Planning and developing effective programs.
Unit 5: The Internet
- The Internet's Structure: Understanding how the internet works.
- Networking Concepts: IP addresses, routers, and protocols.
- Security and Privacy: Issues of online safety and data protection.
- The World Wide Web: Exploring the relationship between the internet and the web.
Unit 6: Global Impact of Computing
- Ethical Considerations: Addressing the ethical implications of technology.
- Social Impacts: Examining the societal effects of computing.
- Global Connectivity: Understanding the impact of technology on different cultures.
- Bias in Algorithms: Recognizing and mitigating bias in computer systems.
Assessment
Your grade will be based on a variety of assessments, including:
- Projects: Individual and group projects designed to apply learned concepts. (40%)
- Labs: Hands-on activities to reinforce programming and computational thinking skills. (20%)
- Quizzes: Regular short quizzes to assess understanding of key concepts. (15%)
- Exams: Midterm and final exams covering the material from each unit. (20%)
- Participation: Active engagement in class discussions and activities. (5%)
Required Materials
- Textbook: [Textbook Name and Edition if applicable]
- Access to a computer with internet connectivity.
Course Policies
- Late Submissions: Late assignments will be penalized [State your policy on late submissions].
- Academic Honesty: All work must be your own. Plagiarism will not be tolerated.
- Attendance: Regular attendance is crucial for success in this course.
- Classroom Conduct: Maintain a respectful and collaborative learning environment.
This syllabus is subject to change at the instructor's discretion. Any modifications will be announced in class and/or through the learning management system. Please feel free to contact me with any questions or concerns.
