CSP Index « PD4CS - Python programming, common student misconceptions, student projects

AP Computer Science Principles (CSP) Index

The new AP Computer Science Principles (CSP) course is designed to introduce students to foundational computing concepts and to explore the impact of computing and technology on the world and society. The course is organized around the investigation of seven big ideas, all of which are fundamental principles in computer science. The Principles Index links the learning objectives (LO) of the seven big ideas to the professional development material of PD4CS. More information about the CSP course can be found here.

Many of the PD4CS posts contain extensive discussions of misconceptions students have and common challenges they face. A list of all misconceptions is available here. The PD material is designed as teacher-facing. Teachers are welcome to also use it as supplements to their own lessons and multiple posts give suggestions on how to do so.

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BIG IDEA 1: CREATIVITY
LO 1.1.1 Apply a creative development process when creating computational artifacts.	- Loops – Scratch - Loops – Nested Loops - Algorithms – Solving Problems
LO 1.2.1 Create a computational artifact for creative expression.	- Loops – Scratch - Loops – Nested Loops - Algorithms – Solving Problems - Recursion – Algorithms
LO 1.2.2 Create a computational artifact using computing tools and techniques to solve a problem.	- Internet – HTML - Internet – Javascript - Internet – PHP
LO 1.2.3 Create a new computational artifact by combining or modifying existing artifacts.	- Loops – Scratch - Loops – Python - Internet – CSS
LO 1.2.4 Collaborate in the creation of computational artifacts.	- Git and GitHub
LO 1.2.5 Analyze the correctness, usability, functionality, and suitability of computational artifacts.	- Debugging – Common Error Messages in Python - Debugging – Programs without Compile-time and Run-time Errors - Debugging – Common Mistakes not Producing an Error Message - Algorithms – Solving Problems - Recursion – Write a recursive or non-recursive function?
LO 1.3.1 Use computing tools and techniques for creative expression.	- Recursion – Algorithms

BIG IDEA 2: ABSTRACTION
LO 2.1.1 Describe the variety of abstractions used to represent data.	- Algorithms – Solving Problems
LO 2.1.2 Explain how binary sequences are used to represent digital data.	N/A
LO 2.2.1 Develop an abstraction when writing a program or creating other computational artifacts.	- Data Structures - Functions - Recursion - Classes and Objects
LO 2.2.2 Use multiple levels of abstraction to write programs.	- Data Structures - Functions - Recursion - Classes and Objects - Modules and Packages
LO 2.2.3 Identify multiple levels of abstractions that are used when writing programs.	- Data Structures - Functions - Classes and Objects
LO 2.3.1 Use models and simulations to represent phenomena.	- Recursion – Algorithms
LO 2.3.2 Use models and simulations to formulate, refine, and test hypotheses.	- Simulation and Modeling - Big Data

BIG IDEA 3: DATA AND INFORMATION
LO 3.1.1 Use computers to process information, find patterns, and test hypotheses about digitally processed information to gain insight and knowledge.	- Loops – Python - Loops – Nested Loops - Algorithms – Solving Problems - Recursion – Algorithms - Big Data – All About Data - Big Data – Visualization - Big Data – What is a data scientist?
LO 3.1.2 Collaborate when processing information to gain insight and knowledge.	- Git and GitHub – Overview - Git and GitHub – Example of Using GitHub in a Project – Individual Part - Git and GitHub – Example of Using GitHub in a Project – Collaboration Part - Git and GitHub – Additional Resources - Big Data – All About Data - Big Data – Visualization - Big Data – What is a data scientist?
LO 3.1.3 Explain the insight and knowledge gained from digitally processed data by using appropriate visualizations, notations, and precise language.	- Big Data – All About Data - Big Data – Visualization - Big Data – What is a data scientist?
LO 3.2.1 Extract information from data to discover and explain connections, patterns, or trends.	- Big Data – All About Data - Big Data – Visualization - Big Data – What is a data scientist?
LO 3.2.2 Use large data sets to explore and discover information and knowledge.	- Algorithms – Solving Problems - Big Data – All About Data - Big Data – Visualization - Big Data – What is a data scientist?
LO 3.3.1 Analyze how data representation, storage, security, and transmission of data involve computational manipulation of information.	- Information Security - Big Data – All About Data - Big Data – Visualization - Big Data – What is a data scientist?

BIG IDEA 4: ALGORITHMS
LO 4.1.1 Develop an algorithm for implementation in a program.	- Algorithms – Overview - Algorithms – Solving Problems - Recursion – Algorithms
LO 4.1.2 Express an algorithm in a language.	- Loops – Python - Loops – Nested Loops - Algorithms – Solving Problems - Recursion – Algorithms
LO 4.2.1 Explain the difference between algorithms that run in a reasonable time and those that do not run in a reasonable time (ignoring "nondeterministic polynomial" language).	- Algorithms – Solving Problems - Recursion – Algorithms - Recursion – Write a recursive or non-recursive function?
LO 4.2.2 Explain the difference between solvable and unsolvable problems in computer science (without determining whether a given problem is solvable or unsolvable).	- Algorithms – Unsolvable Problems
LO 4.2.3 Explain the existence of undecidable problems in computer science.	- Algorithms – Unsolvable Problems
LO 4.2.4 Evaluate algorithms analytically and empirically for efficiency, correctness, and clarity.	- Loops – Python - Loops – Nested Loops - Algorithms – Solving Problems - Recursion – Algorithms

BIG IDEA 5: PROGRAMMING
LO 5.1.1 Develop a program for creative expression, to satisfy, personal curiosity, or to create new knowledge.	- Variables - Conditionals - Data Structures - Loops - Functions - Algorithms – Solving Problems - Recursion – Algorithms
LO 5.1.2 Develop a correct program to solve problems.	- Variables - Conditionals - Data Structures - Loops - Functions - Algorithms – Solving Problems - Recursion – Algorithms
LO 5.1.3 Collaborate to develop a program.	- Git and GitHub
LO 5.2.1 Explain how programs implement algorithms.	- Functions - Algorithms – Solving Problems - Recursion – Algorithms
LO 5.3.1 Use abstraction to manage complexity in programs.	- Data Structures - Functions - Classes and Objects - Modules and Packages
LO 5.4.1 Evaluate the correctness of a program.	- Variables – Student Misconceptions and Challenges - Data Structures – Student Misconceptions and Challenges - Loops – Student Misconceptions and Challenges - Functions – Student Misconceptions and Challenges - Debugging - Algorithms – Student Misconceptions and Challenges - Recursion – Misconceptions and Challenges - Classes – Common Misconceptions and Challenges - Internet – Student Misconceptions and Challenges
LO 5.5.1 Employ the appropriate mathematical and logical concepts in programming.	- Conditionals — Boolean Expressions - Conditionals — Conditional Statements - Data Structures – Lists and Tuples in Python - Data Structures – Arrays in Python - Data Structures – Other Data Structures - Data Structures – Use of Data Structures in PD Material - Loops – Python - Loops – Nested Loops

BIG IDEA 6: THE INTERNET
LO 6.1.1 Explain the abstractions in the Internet and how the Internet functions (without reference to the specific components involved).	- Internet – Introduction
LO 6.2.1 Explain characteristics of the Internet and the systems built on it.	- Internet – Introduction
LO 6.2.2 Explain how the characteristics of the Internet influence the systems built on it.	- Internet – Introduction
LO 6.3.1 Identify existing cybersecurity concerns and potential options to address these issues with the Internet and the systems built on it.	- Information Security

BIG IDEA 7: GLOBAL IMPACT
LO 7.1.1 Explain how computing innovations affect communication, interaction, and cognition.	- Simulation and Modeling - Big Data
LO 7.1.2 Explain how people participate in a problem-solving process that scales.	- Simulation and Modeling - Big Data
LO 7.2.1 Explain how computing has impacted innovations in other fields.	- Simulation and Modeling - Big Data
LO 7.3.1 Analyze the beneficial and harmful effects of computing.	- Information Security - Big Data – All About Data
LO 7.4.1 Explain the connections between computing and economic, social, and cultural contexts.	- Big Data – All About Data - Undergraduate Computing Degrees – Overview