Search the California Content Standards

Standard:
Test and analyze the effects of changing variables while using computational models.

Descriptive Statement:
Variables within a computational model may be changed, in order to alter a computer simulation or to more accurately represent how various data is related. Students interact with a given model, make changes to identified model variables, and observe and reflect upon the results. For example, students could test a program that makes a robot move on a track by making changes to variables (e.g., height and angle of track, size and mass of the robot) and discussing how these changes affect how far the robot travels. (CA NGSS: MS-PS2-2) Alternatively, students could test a game simulation and change variables (e.g., skill of simulated players, nature of opening moves) and analyze how these changes affect who wins the game. (CA NGSS: MS-ETS1-3) Additionally, students could modify a model for predicting the likely color of the next pick from a bag of colored candy and analyze the effects of changing variables representing the common color ratios in a typical bag of candy. (CA CCSS for Mathematics 7.SP.7, 8.SP.4)

Standard:
Collaborate with many contributors when creating a computational artifact.

Descriptive Statement:
Users have diverse sets of experiences, needs, and wants. These need to be understood and integrated into the design of computational artifacts. Students use applications that enable crowdsourcing to gather services, ideas, or content from a large group of people. At this level, crowdsourcing can be done at the local level (e.g., classroom, school, or neighborhood) and/or global level (e.g., age-appropriate online communities). For example, a group of students could use electronic surveys to solicit input from their neighborhood regarding an important social or political issue. They could collaborate with a community artist to combine animations and create a digital community collage informing the public about various points of view regarding the topic. (VAPA Visual Art 8.5.2, 8.5.4)

Standard:
Compare levels of abstraction and interactions between application software, system software, and hardware.

Descriptive Statement:
At its most basic level, a computer is composed of physical hardware on which software runs. Multiple layers of software are built upon various layers of hardware. Layers manage interactions and complexity in the computing system. System software manages a computing device's resources so that software can interact with hardware. Application software communicates with the user and the system software to accomplish its purpose. Students compare and describe how application software, system software, and hardware interact. For example, students could compare how various levels of hardware and software interact when a picture is to be taken on a smartphone. Systems software provides low-level commands to operate the camera hardware, but the application software interacts with system software at a higher level by requesting a common image file format (e.g., .png) that the system software provides.

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:
Use collaboration tools and methods to increase connectivity with people of different cultures and careers.

Descriptive Statement:
Increased digital connectivity and communication between people across a variety of cultures and in differing professions has changed the collaborative nature of personal and professional interaction. Students identify, explain, and use appropriate collaborative tools. For example, students could compare ways that various technological collaboration tools could help a team become more cohesive and then choose one of these tools to manage their teamwork. Alternatively, students could use different collaborative tools and methods to solicit input from not only team members and classmates but also others, such as participants in online forums or local communities.

Standard:
Categorize and describe the different functions of operating system software.

Descriptive Statement:
Operating systems (OS) software is the code that manages the computer’s basic functions. Students describe at a high level the different functions of different components of operating system software. Examples of functions could include memory management, data storage/retrieval, processes management, and access control. For example, students could use monitoring tools including within an OS to inspect the services and functions running on a system and create an artifact to describe the activity that they observed (e.g., when a browser is running with many tabs open, memory usage is increased). They could also inspect and describe changes in the activity monitor that occur as different applications are executing (e.g., processor utilization increases when a new application is launched).

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.

Standard:
Evaluate the ability of models and simulations to test and support the refinement of hypotheses.

Descriptive Statement:
A model could be implemented as a diagram or a program that represents key properties of a physical or other system. A simulation is based on a model, and enables observation of the system as key properties change. Students explore, explain, and evaluate existing models and simulations, in order to support the refinement of hypotheses about how the systems work. At this level, the ability to accurately and completely model and simulate complex systems is not expected. For example, a computer model of ants following a path created by other ants who found food explains the trail-like travel patterns of the insect. Students could evaluate if the output of the model fits well with their hypothesis that ants navigate the world through the use of pheromones. They could explain how the computer model supports this hypothesis and how it might leave out certain aspects of ant behavior and whether these are important to understanding ant travel behavior. Alternatively, students could hypothesize how different ground characteristics (e.g., soil type, thickness of sediment above bedrock) relate to the severity of shaking at the surface during an earthquake. They could add or modify input about ground characteristics into an earthquake simulator, observe the changed simulation output, and then evaluate their hypotheses.