Computer Science Standards
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Showing 11 - 15 of 15 Standards
Standard Identifier: 9-12.CS.3
Grade Range:
9–12
Concept:
Computing Systems
Subconcept:
Troubleshooting
Practice(s):
Testing and Refining Computational Artifacts (6.2)
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).
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 Identifier: 9-12.DA.10
Grade Range:
9–12
Concept:
Data & Analysis
Subconcept:
Collection, Visualization, & Transformation
Practice(s):
Creating Computational Artifacts (5.2)
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.
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 Identifier: 9-12S.CS.2
Grade Range:
9–12 Specialty
Concept:
Computing Systems
Subconcept:
Hardware & Software
Practice(s):
Communicating About Computing (7.2)
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).
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 Identifier: 9-12S.DA.7
Grade Range:
9–12 Specialty
Concept:
Data & Analysis
Subconcept:
Collection, Visualization, & Transformation
Practice(s):
Communicating About Computing (7.1)
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.
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 Identifier: 9-12S.DA.8
Grade Range:
9–12 Specialty
Concept:
Data & Analysis
Subconcept:
Collection, Visualization, & Transformation
Practice(s):
Developing and Using Abstractions, Communicating About Computing (4.1, 7.1)
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.
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.
Showing 11 - 15 of 15 Standards
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