Computer Science Standards
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Showing 1 - 10 of 11 Standards
Standard Identifier: K-2.DA.7
Grade Range:
K–2
Concept:
Data & Analysis
Subconcept:
Storage
Practice(s):
Developing and Using Abstractions (4.2)
Standard:
Store, copy, search, retrieve, modify, and delete information using a computing device, and define the information stored as data.
Descriptive Statement:
Information from the real world can be stored and processed by a computing device. When stored on a computing device, it is referred to as data. Data can include images, text documents, audio files, and video files. Students store, copy, search, retrieve, modify, and delete information using a computing device and define the information stored as data. For example, students could produce a story using a computing device, storing it locally or remotely (e.g., in the cloud). They could then make a copy of the story for peer revision and editing. When the final copy of the story is complete, students delete any unnecessary files. They search for and retrieve data from a local or remote source, depending on where it was stored. (CA CCSS for ELA/Literacy W.K.6, W.K.5, W1.6, W.1.5, W.2.6, W.2.5) Alternatively, students could record their voices singing an age-appropriate song. They could store the data on a computing device, search for peers' audio files, retrieve their own files, and delete unnecesary takes. (VAPA Music K.2.2, 1.2.2, 2.2.2)
Store, copy, search, retrieve, modify, and delete information using a computing device, and define the information stored as data.
Descriptive Statement:
Information from the real world can be stored and processed by a computing device. When stored on a computing device, it is referred to as data. Data can include images, text documents, audio files, and video files. Students store, copy, search, retrieve, modify, and delete information using a computing device and define the information stored as data. For example, students could produce a story using a computing device, storing it locally or remotely (e.g., in the cloud). They could then make a copy of the story for peer revision and editing. When the final copy of the story is complete, students delete any unnecessary files. They search for and retrieve data from a local or remote source, depending on where it was stored. (CA CCSS for ELA/Literacy W.K.6, W.K.5, W1.6, W.1.5, W.2.6, W.2.5) Alternatively, students could record their voices singing an age-appropriate song. They could store the data on a computing device, search for peers' audio files, retrieve their own files, and delete unnecesary takes. (VAPA Music K.2.2, 1.2.2, 2.2.2)
Standard Identifier: K-2.DA.8
Grade Range:
K–2
Concept:
Data & Analysis
Subconcept:
Collection, Visualization, & Transformation
Practice(s):
Developing and Using Abstractions, Communicating About Computing (4.4, 7.1)
Standard:
Collect and present data in various visual formats.
Descriptive Statement:
Data can be collected and presented in various visual formats. For example, students could measure temperature changes throughout a day. They could then discuss ways to display the data visually. Students could extend the activity by writing different narratives based on collected data, such as a story that begins in the morning when temperatures are low and one that begins in the afternoon when the sun is high and temperatures are higher. (CA CCSS for ELA/Literacy RL.K.9, RL.1.9, RL.2.9, W.K.3, W.1.3, W.2.3). Alternatively, students collect peers' favorite flavor of ice cream and brainstorm differing ways to display the data. In groups, students can choose to display and present the data in a format of their choice. (CA CCSS for Mathematics K.MD.3, 1.MD.4, 2.MD.10)
Collect and present data in various visual formats.
Descriptive Statement:
Data can be collected and presented in various visual formats. For example, students could measure temperature changes throughout a day. They could then discuss ways to display the data visually. Students could extend the activity by writing different narratives based on collected data, such as a story that begins in the morning when temperatures are low and one that begins in the afternoon when the sun is high and temperatures are higher. (CA CCSS for ELA/Literacy RL.K.9, RL.1.9, RL.2.9, W.K.3, W.1.3, W.2.3). Alternatively, students collect peers' favorite flavor of ice cream and brainstorm differing ways to display the data. In groups, students can choose to display and present the data in a format of their choice. (CA CCSS for Mathematics K.MD.3, 1.MD.4, 2.MD.10)
Standard Identifier: 3-5.DA.7
Grade Range:
3–5
Concept:
Data & Analysis
Subconcept:
Storage
Practice(s):
Developing and Using Abstractions (4.2)
Standard:
Explain that the amount of space required to store data differs based on the type of data and/or level of detail.
Descriptive Statement:
All saved data requires space to store it, whether locally or not (e.g., on the cloud). Music, images, video, and text require different amounts of storage. Video will often require more storage and different format than music or images alone because video combines both. The level of detail represented by that data also affects storage requirements. For instance, two pictures of the same object can require different amounts of storage based upon their resolution, and a high-resolution photo could require more storage than a low-resolution video. Students select appropriate storage for their data. For example, students could create an image using a standard drawing app. They could save the image in different formats (e.g., .png, .jpg, .pdf) and compare file sizes. They should also notice that different file sizes can result in differences in quality or resolution (e.g., some pictures could be more pixelated while some could be sharper). Alternatively, in an unplugged activity, students could represent images by coloring in squares within a large grid. They could model how a larger grid requires more storage but also represents a clearer image (i.e., higher resolution).
Explain that the amount of space required to store data differs based on the type of data and/or level of detail.
Descriptive Statement:
All saved data requires space to store it, whether locally or not (e.g., on the cloud). Music, images, video, and text require different amounts of storage. Video will often require more storage and different format than music or images alone because video combines both. The level of detail represented by that data also affects storage requirements. For instance, two pictures of the same object can require different amounts of storage based upon their resolution, and a high-resolution photo could require more storage than a low-resolution video. Students select appropriate storage for their data. For example, students could create an image using a standard drawing app. They could save the image in different formats (e.g., .png, .jpg, .pdf) and compare file sizes. They should also notice that different file sizes can result in differences in quality or resolution (e.g., some pictures could be more pixelated while some could be sharper). Alternatively, in an unplugged activity, students could represent images by coloring in squares within a large grid. They could model how a larger grid requires more storage but also represents a clearer image (i.e., higher resolution).
Standard Identifier: 3-5.DA.8
Grade Range:
3–5
Concept:
Data & Analysis
Subconcept:
Collection, Visualization, & Transformation
Practice(s):
Communicating About Computing (7.1)
Standard:
Organize and present collected data visually to highlight relationships and support a claim.
Descriptive Statement:
Raw data has little meaning on its own. Data is often sorted or grouped to provide additional clarity. Organizing data can make interpreting and communicating it to others easier. Data points can be clustered by a number of commonalities. The same data could be manipulated in different ways to emphasize particular aspects or parts of the data set. For example, students could create and administer electronic surveys to their classmates. Possible topics could include favorite books, family heritage, and after school activities. Students could then create digital displays of the data they have collected such as column histogram charts showing the percent of respondents in each grade who selected a particular favorite book. Finally, students could make quantitative statements supported by the data such as which books are more appealing to specific ages of students. As an extension, students could write an opinion piece stating a claim and supporting it with evidence from the data they collected. (CA CCSS for Mathematics 3.MD.3, 4.MD.4, 5.MD.2) (CA CCSS for ELA/Literacy W.3.1, W.4.1, W.5.1) Alternatively, students could represent data in tables and graphical displays to describe weather experienced in the last several years. They could select the type of graphical display based on the specific data represented (e.g., daily high/low temperatures on a scatter plot, average temperatures for a month across years in a column chart). Students could then make a claim about expected weather in future months based on the data. (CA NGSS: 3-ESS2-1)
Organize and present collected data visually to highlight relationships and support a claim.
Descriptive Statement:
Raw data has little meaning on its own. Data is often sorted or grouped to provide additional clarity. Organizing data can make interpreting and communicating it to others easier. Data points can be clustered by a number of commonalities. The same data could be manipulated in different ways to emphasize particular aspects or parts of the data set. For example, students could create and administer electronic surveys to their classmates. Possible topics could include favorite books, family heritage, and after school activities. Students could then create digital displays of the data they have collected such as column histogram charts showing the percent of respondents in each grade who selected a particular favorite book. Finally, students could make quantitative statements supported by the data such as which books are more appealing to specific ages of students. As an extension, students could write an opinion piece stating a claim and supporting it with evidence from the data they collected. (CA CCSS for Mathematics 3.MD.3, 4.MD.4, 5.MD.2) (CA CCSS for ELA/Literacy W.3.1, W.4.1, W.5.1) Alternatively, students could represent data in tables and graphical displays to describe weather experienced in the last several years. They could select the type of graphical display based on the specific data represented (e.g., daily high/low temperatures on a scatter plot, average temperatures for a month across years in a column chart). Students could then make a claim about expected weather in future months based on the data. (CA NGSS: 3-ESS2-1)
Standard Identifier: 6-8.DA.7
Grade Range:
6–8
Concept:
Data & Analysis
Subconcept:
Storage
Practice(s):
Developing and Using Abstractions (4.4)
Standard:
Represent data in multiple ways.
Descriptive Statement:
Computers store data as sequences of 0s and 1s (bits). Software translates to and from this low-level representation to higher levels that are understandable by people. Furthermore, higher level data can be represented in multiple ways, such as the digital display of a color and its corresponding numeric RGB value, or a bar graph, a pie chart, and table representation of the same data in a spreadsheet. For example, students could use a color picker to explore the correspondence between the digital display or name of a color (high-level representations) and its RGB value or hex code (low-level representation). Alternatively, students could translate a word (high-level representation) into Morse code or its corresponding sequence of ASCII codes (low-level representation).
Represent data in multiple ways.
Descriptive Statement:
Computers store data as sequences of 0s and 1s (bits). Software translates to and from this low-level representation to higher levels that are understandable by people. Furthermore, higher level data can be represented in multiple ways, such as the digital display of a color and its corresponding numeric RGB value, or a bar graph, a pie chart, and table representation of the same data in a spreadsheet. For example, students could use a color picker to explore the correspondence between the digital display or name of a color (high-level representations) and its RGB value or hex code (low-level representation). Alternatively, students could translate a word (high-level representation) into Morse code or its corresponding sequence of ASCII codes (low-level representation).
Standard Identifier: 6-8.DA.8
Grade Range:
6–8
Concept:
Data & Analysis
Subconcept:
Collection, Visualization, & Transformation
Practice(s):
Communicating About Computing (7.1)
Standard:
Collect data using computational tools and transform the data to make it more useful.
Descriptive Statement:
Data collection has become easier and more ubiquitous. The cleaning of data is an important transformation for ensuring consistent format, reducing noise and errors (e.g., removing irrelevant responses in a survey), and/or making it easier for computers to process. Students build on their ability to organize and present data visually to support a claim, understanding when and how to transform data so information can be more easily extracted. Students also transform data to highlight or expose relationships. For example, students could use computational tools to collect data from their peers regarding the percentage of time technology is used for school work and entertainment, and then create digital displays of their data and findings. Students could then transform the data to highlight relationships representing males and females as percentages of a whole instead of as individual counts. (CA CCSS for Mathematics 6.SP.4, 7.SP.3, 8.SP.1, 8.SP.4) Alternatively, students could collect data from online forms and surveys, from a sensor, or by scraping a web page, and then transform the data to expose relationships. They could highlight the distribution of data (e.g., words on a web page, readings from a sensor) by giving quantitative measures of center and variability. (CA CCSS for Mathematics 6.SP.5.c, 7.SP.4)
Collect data using computational tools and transform the data to make it more useful.
Descriptive Statement:
Data collection has become easier and more ubiquitous. The cleaning of data is an important transformation for ensuring consistent format, reducing noise and errors (e.g., removing irrelevant responses in a survey), and/or making it easier for computers to process. Students build on their ability to organize and present data visually to support a claim, understanding when and how to transform data so information can be more easily extracted. Students also transform data to highlight or expose relationships. For example, students could use computational tools to collect data from their peers regarding the percentage of time technology is used for school work and entertainment, and then create digital displays of their data and findings. Students could then transform the data to highlight relationships representing males and females as percentages of a whole instead of as individual counts. (CA CCSS for Mathematics 6.SP.4, 7.SP.3, 8.SP.1, 8.SP.4) Alternatively, students could collect data from online forms and surveys, from a sensor, or by scraping a web page, and then transform the data to expose relationships. They could highlight the distribution of data (e.g., words on a web page, readings from a sensor) by giving quantitative measures of center and variability. (CA CCSS for Mathematics 6.SP.5.c, 7.SP.4)
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: 9-12.DA.8
Grade Range:
9–12
Concept:
Data & Analysis
Subconcept:
Storage
Practice(s):
Developing and Using Abstractions (4.1)
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.
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 Identifier: 9-12.DA.9
Grade Range:
9–12
Concept:
Data & Analysis
Subconcept:
Storage
Practice(s):
Recognizing and Defining Computational Problems (3.3)
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).
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 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.
Showing 1 - 10 of 11 Standards
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