6-8.DA.8 (Computer Science)
Standard Identifier:
6-8.DA.8
Content Area:
Computer Science
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)
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