Search the California Content Standards

Standard:
Justify the selection of specific control structures by identifying tradeoffs associated with implementation, readability, and performance.

Descriptive Statement:
The selection of control structures in a given programming language impacts readability and performance. Readability refers to how clear the program is to other programmers and can be improved through documentation. Control structures at this level may include, for example, conditional statements, loops, event handlers, and recursion. Students justify control structure selection and tradeoffs in the process of creating their own computational artifacts. The discussion of performance is limited to a theoretical understanding of execution time and storage requirements; a quantitative analysis is not expected. For example, students could compare the readability and program performance of iterative and recursive implementations of procedures that calculate the Fibonacci sequence. Alternatively, students could compare the readability and performance tradeoffs of multiple if statements versus a nested if statement.

Standard:
Iteratively design and develop computational artifacts for practical intent, personal expression, or to address a societal issue by using events to initiate instructions.

Descriptive Statement:
In this context, relevant computational artifacts can include programs, mobile apps, or web apps. Events can be user-initiated, such as a button press, or system-initiated, such as a timer firing. For example, students might create a tool for drawing on a canvas by first implementing a button to set the color of the pen. Alternatively, students might create a game where many events control instructions executed (e.g., when a score climbs above a threshold, a congratulatory sound is played; when a user clicks on an object, the object is loaded into a basket; when a user clicks on an arrow key, the player object is moved around the screen).

Standard:
Create data visualizations to help others better understand real-world phenomena.

Descriptive Statement:
People transform, generalize, simplify, and present large data sets in different ways to influence how other people interpret and understand the underlying information. Students select relevant data from large or complex data sets in support of a claim or to communicate the information in a more sophisticated manner. Students use software tools or programming to perform a range of mathematical operations to transform and analyze data and create powerful data visualizations (that reveal patterns in the data). For example, students could create data visualizations to reveal patterns in voting data by state, gender, political affiliation, or socioeconomic status. Alternatively, students could use U.S. government data on criticially endangered animals to visualize population change over time.

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:
Describe issues that impact network functionality.

Descriptive Statement:
Many different organizations, including educational, governmental, private businesses, and private households rely on networks to function adequately in order to engage in online commerce and activity. Quality of Service (QoS) refers to the capability of a network to provide better service to selected network traffic over various technologies from the perspective of the consumer. Students define and discuss performance measures that impact network functionality, such as latency, bandwidth, throughput, jitter, and error rate. For example, students could use online network simulators to explore how performance measures impact network functionality and describe impacts when various changes in the network occur. Alternatively, students could describe how pauses in television interviews conducted over satellite telephones are impacted by networking factors such as latency and jitter.

Standard:
Describe the design characteristics of the Internet.

Descriptive Statement:
The Internet connects devices and networks all over the world. Large-scale coordination occurs among many different machines across multiple paths every time a web page is opened or an image is viewed online. Through the domain name system (DNS), devices on the Internet can look up Internet Protocol (IP) addresses, allowing end-to-end communication between devices. The design decisions that direct the coordination among systems composing the Internet also allow for scalability and reliability. Students factor historical, cultural, and economic decisions in their explanations of the Internet. For example, students could explain how hierarchy in the DNS supports scalability and reliability. Alternatively, students could describe how the redundancy of routing between two nodes on the Internet increases reliability and scales as the Internet grows.

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.