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:
Model the role of protocols in transmitting data across networks and the Internet.

Descriptive Statement:
Protocols are rules that define how messages between computers are sent. They determine how quickly and securely information is transmitted across networks, as well as how to handle errors in transmission. Students model how data is sent using protocols to choose the fastest path and to deal with missing information. Knowledge of the details of how specific protocols work is not expected. The priority at this grade level is understanding the purpose of protocols and how they enable efficient and errorless communication. For example, students could devise a plan for sending data representing a textual message and devise a plan for resending lost information. Alternatively, students could devise a plan for sending data to represent a picture, and devise a plan for interpreting the image when pieces of the data are missing. Additionally, students could model the speed of sending messages by Bluetooth, Wi-Fi, or cellular networks and describe ways errors in data transmission can be detected and dealt with.

Standard:
Develop guidelines that convey systematic troubleshooting strategies that others can use to identify and fix errors.

Descriptive Statement:
Troubleshooting complex problems involves the use of multiple sources when researching, evaluating, and implementing potential solutions. Troubleshooting also relies on experience, such as when people recognize that a problem is similar to one they have seen before and adapt solutions that have worked in the past. For example, students could create a list of troubleshooting strategies to debug network connectivity problems such as checking hardware and software status and settings, rebooting devices, and checking security settings. Alternatively, students could create troubleshooting guidelines for help desk employees based on commonly observed problems (e.g., problems connecting a new device to the computer, problems printing from a computer to a network printer).

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:
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.