Search the California Content Standards

Standard:
Decompose problems into smaller subproblems through systematic analysis, using constructs such as procedures, modules, and/or classes.

Descriptive Statement:
Decomposition enables solutions to complex problems to be designed and implemented as more manageable subproblems. Students decompose a given problem into subproblems that can be solved using existing functionalities, or new functionalities that they design and implement. For example, students could design a program for supporting soccer coaches in analyzing their teams' statistics. They decompose the problem in terms of managing input, analysis, and output. They decompose the data organization by designing what data will be stored per player, per game, and per team. Team players may be stored as a collection. Data per team player may include: number of shots, misses, saves, assists, penalty kicks, blocks, and corner kicks. Students design methods for supporting various statistical analyses and display options. Students design output formats for individual players or coaches.

Standard:
Create computational artifacts using modular design.

Descriptive Statement:
Computational artifacts are created by combining and modifying existing computational artifacts and/or by developing new artifacts. To reduce complexity, large programs can be designed as systems of interacting modules, each with a specific role, coordinating for a common overall purpose. Students should create computational artifacts with interacting procedures, modules, and/or libraries. For example, students could incorporate a physics library into an animation of bouncing balls. Alternatively, students could integrate open-source JavaScript libraries to expand the functionality of a web application. Additionally, students could create their own game to teach Spanish vocabulary words using their own modular design (e.g., including methods to: control scoring, manage wordlists, manage access to different game levels, take input from the user, etc.).

Standard:
Refine computational models to better represent the relationships among different elements of data collected from a phenomenon or process.

Descriptive Statement:
Computational models are used to make predictions about processes or phenomena based on selected data and features. They allow people to investigate the relationships among different variables to understand a system. Predictions are tested to validate models. Students evaluate these models against real-world observations. For example, students could use a population model that allows them to speculate about interactions among different species, evaluate the model based on data gathered from nature, and then refine the model to reflect more complex and realistic interactions.

Standard:
Explain the beneficial and harmful effects that intellectual property laws can have on innovation.

Descriptive Statement:
Laws and ethics govern aspects of computing such as privacy, data, property, information, and identity. Students explain the beneficial and harmful effects of intellectual property laws as they relate to potential innovations and governance. For example, students could explain how patents protect inventions but may limit innovation. Alternatively, students could explain how intellectual property laws requiring that artists be paid for use of their media might limit the choice of songs developers can use in their computational artifacts.

Standard:
Explain the privacy concerns related to the collection and generation of data through automated processes.

Descriptive Statement:
Data can be collected and aggregated across millions of people, even when they are not actively engaging with or physically near the data collection devices. Students recognize automated and non-evident collection of information and the privacy concerns they raise for individuals. For example, students could explain the impact on an individual when a social media site's security settings allows for mining of account information even when the user is not online. Alternatively, students could discuss the impact on individuals of using surveillance video in a store to track customers. Additionally, students could discuss how road traffic can be monitored to change signals in real time to improve road efficiency without drivers being aware and discuss policies for retaining data that identifies drivers' cars and their behaviors.

Standard:
Evaluate the social and economic implications of privacy in the context of safety, law, or ethics.

Descriptive Statement:
Laws govern many aspects of computing, such as privacy, data, property, information, and identity. International differences in laws and ethics have implications for computing. Students make and justify claims about potential and/or actual privacy implications of policies, laws, or ethics and consider the associated tradeoffs, focusing on society and the economy. For example, students could explore the case of companies tracking online shopping behaviors in order to decide which products to target to consumers. Students could evaluate the ethical and legal dilemmas of collecting such data without consumer knowledge in order to profit companies. Alternatively, students could evaluate the implications of net neutrality laws on society's access to information and on the impacts to businesses of varying sizes.

Standard:
Describe how artificial intelligence drives many software and physical systems.

Descriptive Statement:
Artificial intelligence is a sub-discipline of computer science that enables computers to solve problems previously handled by biological systems. There are many applications of artificial intelligence, including computer vision and speech recognition. Students research and explain how artificial intelligence has been employed in a given system. Students are not expected to implement an artificially intelligent system in order to meet this standard. For example, students could observe an artificially intelligent system and notice where its behavior is not human-like, such as when a character in a videogame makes a mistake that a human is unlikely to make, or when a computer easily beats even the best human players at a given game. Alternatively, students could interact with a search engine asking various questions, and after reading articles on the topic, they could explain how the computer is able to respond to queries.

Standard:
Implement an algorithm that uses artificial intelligence to overcome a simple challenge.

Descriptive Statement:
Artificial intelligence algorithms allow a computer to perceive and move in the world, use knowledge, and engage in problem solving. Students create a computational artifact that is able to carry out a simple task commonly performed by living organisms. Students do not need to realistically simulate human behavior or solve a complex problem in order to meet this standard. For example, students could implement an algorithm for playing tic-tac-toe that would select an appropriate location for the next move. Alternatively, students could implement an algorithm that allows a solar-powered robot to move to a sunny location when its batteries are low.

Standard:
Implement searching and sorting algorithms to solve computational problems.

Descriptive Statement:
One of the core uses of computers is to store, organize, and retrieve information when working with large amounts of data. Students create computational artifacts that use searching and/or sorting algorithms to retrieve, organize, or store information. Students do not need to select their algorithm based on efficiency. For example, students could write a script to sequence their classmates in order from youngest to oldest. Alternatively, students could write a program to find certain words within a text and report their location.

Standard:
Evaluate algorithms in terms of their efficiency.

Descriptive Statement:
Algorithms that perform the same task can be implemented in different ways, which take different amounts of time to run on a given input set. Algorithms are commonly evaluated using asymptotic analysis (i.e., “Big O”) which involves exploration of behavior when the input set grows very large. Students classify algorithms by the most common time classes (e.g., log n, linear, n log n, and quadratic or higher). For example, students could read a given algorithm, identify the control constructs, and in conjunction with input size, identify the efficiency class of the algorithm.