Computer Science Standards
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Showing 1 - 5 of 5 Standards
Standard Identifier: K-2.DA.9
Grade Range:
K–2
Concept:
Data & Analysis
Subconcept:
Inference & Models
Practice(s):
Developing and Using Abstractions (4.1)
Standard:
Identify and describe patterns in data visualizations, such as charts or graphs, to make predictions.
Descriptive Statement:
Data can be used to make inferences or predictions about the world. For example, students could record the number of each color of candy in a small packet. Then, they compare their individual data with classmates. Students could use the collected data to predict how many of each colored candy will be in a full size bag of like candy. (CA CCSS for Mathematics K.MD.3, 1.MD.4, 2.MD.10) Alternatively, students could sort and classify objects according to their properties and note observations. Students could then create a graph or chart of their observations and look for connections/relationships (e.g., items that are hard are usually also smooth, or items that are fluffy are usually also light in weight.) Students then look at pictures of additional objects and make predictions regarding the properties of the objects pictured. (CA NGSS: 2-PS1-1, 2-PS1-2)
Identify and describe patterns in data visualizations, such as charts or graphs, to make predictions.
Descriptive Statement:
Data can be used to make inferences or predictions about the world. For example, students could record the number of each color of candy in a small packet. Then, they compare their individual data with classmates. Students could use the collected data to predict how many of each colored candy will be in a full size bag of like candy. (CA CCSS for Mathematics K.MD.3, 1.MD.4, 2.MD.10) Alternatively, students could sort and classify objects according to their properties and note observations. Students could then create a graph or chart of their observations and look for connections/relationships (e.g., items that are hard are usually also smooth, or items that are fluffy are usually also light in weight.) Students then look at pictures of additional objects and make predictions regarding the properties of the objects pictured. (CA NGSS: 2-PS1-1, 2-PS1-2)
Standard Identifier: 3-5.DA.9
Grade Range:
3–5
Concept:
Data & Analysis
Subconcept:
Inference & Models
Practice(s):
Communicating About Computing (7.1)
Standard:
Use data to highlight and/or propose relationships, predict outcomes, or communicate ideas.
Descriptive Statement:
The accuracy of data analysis is related to how the data is represented. Inferences or predictions based on data are less likely to be accurate if the data is insufficient, incomplete, or inaccurate or if the data is incorrect in some way. Additionally, people select aspects and subsets of data to be transformed, organized, and categorized. Students should be able to refer to data when communicating an idea, in order to highlight and/or propose relationships, predict outcomes, highlight different views and/or communicate insights and ideas. For example, students can be provided a scenario in which they are city managers who have a specific amount of funds to improve a city in California. Students can collect data of a city concerning land use, vegetation, wildlife, climate, population density, services and transportation (HSS.4.1.5) to determine and present what area needs to be focused on to improve a problem. Students can compare their data and planned use of funds with peers, clearly communicating or predict outcomes based on data collected. (CA CCCS for ELA/Literacy SL.3.1, SL.4.1, SL.5.1) Alternatively, students could record the temperature at noon each day to show that temperatures are higher in certain months of the year. If temperatures are not recorded on non-school days or are recorded incorrectly, the data would be incomplete and ideas being communicated could be inaccurate. Students may also record the day of the week on which the data was collected, but this would have no relevance to whether temperatures are higher or lower. In order to have sufficient and accurate data on which to communicate the idea, students might use data provided by a governmental weather agency. (CA NGSS: 3-ESS2-1)
Use data to highlight and/or propose relationships, predict outcomes, or communicate ideas.
Descriptive Statement:
The accuracy of data analysis is related to how the data is represented. Inferences or predictions based on data are less likely to be accurate if the data is insufficient, incomplete, or inaccurate or if the data is incorrect in some way. Additionally, people select aspects and subsets of data to be transformed, organized, and categorized. Students should be able to refer to data when communicating an idea, in order to highlight and/or propose relationships, predict outcomes, highlight different views and/or communicate insights and ideas. For example, students can be provided a scenario in which they are city managers who have a specific amount of funds to improve a city in California. Students can collect data of a city concerning land use, vegetation, wildlife, climate, population density, services and transportation (HSS.4.1.5) to determine and present what area needs to be focused on to improve a problem. Students can compare their data and planned use of funds with peers, clearly communicating or predict outcomes based on data collected. (CA CCCS for ELA/Literacy SL.3.1, SL.4.1, SL.5.1) Alternatively, students could record the temperature at noon each day to show that temperatures are higher in certain months of the year. If temperatures are not recorded on non-school days or are recorded incorrectly, the data would be incomplete and ideas being communicated could be inaccurate. Students may also record the day of the week on which the data was collected, but this would have no relevance to whether temperatures are higher or lower. In order to have sufficient and accurate data on which to communicate the idea, students might use data provided by a governmental weather agency. (CA NGSS: 3-ESS2-1)
Standard Identifier: 6-8.DA.9
Grade Range:
6–8
Concept:
Data & Analysis
Subconcept:
Inference & Models
Practice(s):
Developing and Using Abstractions, Testing and Refining Computational Artifacts (4.4, 6.1)
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)
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 Identifier: 9-12.DA.11
Grade Range:
9–12
Concept:
Data & Analysis
Subconcept:
Inference & Models
Practice(s):
Developing and Using Abstractions, Testing and Refining Computational Artifacts (4.4, 6.3)
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.
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 Identifier: 9-12S.DA.9
Grade Range:
9–12 Specialty
Concept:
Data & Analysis
Subconcept:
Inference & Models
Practice(s):
Developing and Using Abstractions (4.4)
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.
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.
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