Search the California Content Standards

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:
Translate between different representations of data abstractions of real-world phenomena, such as characters, numbers, and images.

Descriptive Statement:
Computers represent complex real-world concepts such as characters, numbers, and images through various abstractions. Students translate between these different levels of data representations. For example, students could convert an HTML (Hyper Text Markup Language) tag for red font into RGB (Red Green Blue), HEX (Hexadecimal Color Code), HSL (Hue Saturation Lightness), RGBA( Red Green Blue Alpha), or HSLA (Hue Saturation Lightness and Alpha) representations. Alternatively, students could convert the standard representation of a character such as ! into ASCII or Unicode.

Standard:
Describe tradeoffs associated with how data elements are organized and stored.

Descriptive Statement:
People make choices about how data elements are organized and where data is stored. These choices affect cost, speed, reliability, accessibility, privacy, and integrity. Students describe implications for a given data organziation or storage choice in light of a specific problem. For example, students might consider the cost, speed, reliability, accessibility, privacy, and integrity tradeoffs between storing photo data on a mobile device versus in the cloud. Alternatively, students might compare the tradeoffs between file size and image quality of various image file formats and how choice of format may be infuenced by the device on which it is to be accessed (e.g., smartphone, computer).

Standard:
Evaluate the ways computing impacts personal, ethical, social, economic, and cultural practices.

Descriptive Statement:
Computing may improve, harm, or maintain practices. An understanding of how equity deficits, such as minimal exposure to computing, access to education, and training opportunities, are related to larger, systemic problems in society enables students to create more meaningful artifacts. Students illustrate the positive, negative, and/or neutral impacts of computing. For example, students could evaluate the accessibility of a product for a broad group of end users, such as people who lack access to broadband or who have various disabilities. Students could identify potential bias during the design process and evaluate approaches to maximize accessibility in product design. Alternatively, students could evaluate the impact of social media on cultural, economic, and social practices around the world.

Standard:
Identify impacts of bias and equity deficit on design and implementation of computational artifacts and apply appropriate processes for evaluating issues of bias.

Descriptive Statement:
Biases could include incorrect assumptions developers have made about their users, including minimal exposure to computing, access to education, and training opportunities. Students identify and use strategies to test and refine computational artifacts with the goal of reducing bias and equity deficits and increasing universal access. For example, students could use a spreadsheet to chart various forms of equity deficits, and identify solutions in existing software. Students could use and refine the spreadsheet solutions to create a strategy for methodically testing software specifically for bias and equity.

Standard:
Demonstrate ways a given algorithm applies to problems across disciplines.

Descriptive Statement:
Students identify how a given algorithm can be applied to real-world problems in different disciplines. For example, students could demonstrate how a randomization algorithm can be used to select participants for a clinical medical trial or to select a flash card to display on a vocabulary quiz. Alternatively, students could demonstrate how searching and sorting algorithms are needed to organize records in manufacturing settings, or to support doctors queries of patient records, or to help governments manage support services they provide to their citizens.

Standard:
Study, discuss, and think critically about the potential impacts and implications of emerging technologies on larger social, economic, and political structures, with evidence from credible sources.

Descriptive Statement:
For example, after studying the rise of artifical intelligence, students create a cause and effect chart to represent positive and negative impacts of this technology on society.

Standard:
Demonstrate the flow of execution of a recursive algorithm.

Descriptive Statement:
Recursion is a powerful problem-solving approach where the problem solution is built on solutions of smaller instances of the same problem. A base case, which returns a result without referencing itself, must be defined, otherwise infinite recursion will occur. Students represent a sequence of calls to a recursive algorithm and show how the process resolves to a solution. For example, students could draw a diagram to illustrate flow of execution by keeping track of parameter and returned values for each recursive call. Alternatively, students could create a video showing the passing of arguments as the recursive algorithm runs.

Standard:
Select and use data collection tools and techniques to generate data sets.

Descriptive Statement:
Data collection and organization is essential for obtaining new information insights and revealing new knowledge in our modern world. As computers are able to process larger sets of data, gathering data in an efficient and reliable matter remains important. The choice of data collection tools and quality of the data collected influences how new information, insights, and knowledge will support claims and be communicated. Students devise a reliable method to gather information, use software to extract digital data from data sets, and clean and organize the data in ways that support summaries of information obtained from the data. At this level, students may, but are not required to, create their own data collection tools. For example, students could create a computational artifact that records information from a sonic distance sensor to monitor the motion of a prototype vehicle. Alternatively, students could develop a reliable and practical way to automatically digitally record the number of animals entering a portion of a field to graze. Additionally, students could also find a web site containing data (e.g., race results for a major marathon), scrape the data from the web site using data collection tools, and format the data so it can be analyzed.

Standard:
Use data analysis tools and techniques to identify patterns in data representing complex systems.

Descriptive Statement:
Data analysis tools can be useful for identifying patterns in large amounts of data in many different fields. Computers can help with the processing of extremely large sets of data making very complex systems manageable. Students use computational tools to analyze, summarize, and visualize a large set of data. For example, students could analyze a data set containing marathon times and determine how age, gender, weather, and course features correlate with running times. Alternatively, students could analyze a data set of social media interactions to identify the most influential users and visualize the intersections between different social groups.