Standards - Digital Literacy & Computer Science

2018 - Digital Literacy & Computer Science Course of Study Document
Digital Literacy and Computer Science (2018) Grade(s): 4

DLCS18.4.18

Create a simple digital model of a system, individually and collaboratively, and explain what the model shows and does not show.

Examples: Create a model of the water cycle and indicate that it shows how precipitation forms but does not indicate how pesticides get into rivers.

Unpacked Content

Knowledge

Students know:
  • strategies for creating a simple digital model of a system.
  • how to explain what the model shows and does not show.
  • strategies for working with others.

Skills

Students are able to:
  • create a simple digital model of a system, individually and collaboratively, and explain what the model shows and does not show.

Understanding

Students understand that:
  • digital models are used when it is either not physically possible to reproduce an event or system or it is too cost prohibitive to reproduce an event or system.

Vocabulary

  • digital model
  • system
Digital Literacy and Computer Science (2018) Grade(s): 4

DLCS18.4.19

Use data from a simulation to answer a question collaboratively.

Unpacked Content

Knowledge

Students know:
  • strategies for using data from a simulation to answer a question collaboratively.
  • strategies for working with others.

Skills

Students are able to:
  • use data from a simulation to answer a question.
  • collaboratively work with others.

Understanding

Students understand that:
  • data from a simulation can be used to answer a question collaboratively.

Vocabulary

  • data
  • simulation

Aligned Learning Resources

Sports
Music & Sound
Digital Literacy and Computer Science (2018) Grade(s): 5

DLCS18.5.25

Analyze the concepts, features, and behaviors illustrated by a simulation.

Examples: Object motion, weather, ecosystem, predator/prey.

Unpacked Content

Knowledge

Students know:
  • that analyzing the concepts, features, and behaviors illustrated by a simulation can be a predictor of real
  • life expectations.

Skills

Students are able to:
  • analyze the concepts, features, and behaviors illustrated by a simulation.

Understanding

Students understand that:
  • simulations have connections to real
  • life events.
  • simulations can predict possible real
  • life concepts, features, or behaviors.

Vocabulary

  • analyze
  • concept
  • features
  • behavior
  • simulation

Aligned Learning Resources

Interactives
Digital Literacy and Computer Science (2018) Grade(s): 5

DLCS18.5.26

Connect data from a simulation to real-life events.

Unpacked Content

Knowledge

Students know:
  • how to connect real
  • life events to data from a simulation.

Skills

Students are able to:
  • connect data from a simulation to real
  • life events.

Understanding

Students understand that:
  • data from simulations relates to real
  • life events.
  • simulations can be accurate predictors of real
  • life possibilities.

Vocabulary

  • data
  • simulation

Aligned Learning Resources

Sports
Interactives
Intro to Cubic Units: Can You Visualize It?
Digital Literacy and Computer Science (2018) Grade(s): 6

DLCS18.6.26

Explain why professionals may use models as logical representations of physical, mathematical, or logical systems or processes.

Example: Students will discuss why an engineer may build a model of a building before actually constructing the building.

Unpacked Content

Knowledge

Students know:
  • it can difficult, expensive, or impossible to create a system or process true
  • to
  • scale, therefore professionals often use models or simulations to test theories, plans, or designs.

Skills

Students are able to:
  • identify reasons a system or process cannot be easily replicated.
  • identify situations in which it is best to use a model or simulation.

Understanding

Students understand that:
  • models and/or simulations are used to save time and money during testing phases of projects.

Aligned Learning Resources

Computer Science Discoveries Unit 4 Chapter 1 Lesson 4: User Interfaces (18-19)
Digital Literacy and Computer Science (2018) Grade(s): 6

DLCS18.6.27

Explain how simulations serve to implement models.

Unpacked Content

Knowledge

Students know:
  • that simulations and models are both representations of a system or process.
  • simulations are often digital representations whereas models are often physical representations.

Skills

Students are able to:
  • explain that simulations may be used to save time and/or money in representing a process or system.

Understanding

Students understand that:
  • typically, simulations are digital representations of a process, while models are physical representations of a process.

Aligned Learning Resources

Computer Science Discoveries Unit 4 Chapter 1 Lesson 3: User-Centered Design Micro Activity (18-19)
Computer Science Discoveries Unit 4 Chapter 1 Lesson 4: User Interfaces (18-19)
Digital Literacy and Computer Science (2018) Grade(s): 7

DLCS18.7.26

Categorize models based on the most appropriate representation of various systems.

Unpacked Content

Knowledge

Students know:
  • models can be identified based on the purpose of their function.
  • that predictive models will forecast a possible outcome based on historical data.
  • that cluster and classification models identify similar traits in data and groups like items.
  • that decision models simulate the outcomes of decisions so that the user is aware of possible risks associated with each option.

Skills

Students are able to:
  • identify models based on the purpose of their function.
  • use predictive models to forecast a possible outcome based on historical data.
  • use cluster and classification models to identify similar traits in data and groups like items.
  • use decision models to simulate the outcomes of decisions so that the user is aware of possible risks associated with each option.

Understanding

Students understand that:
  • models should be selected based on the purpose of their function.
  • predictive models forecast a possible outcome based on historical data.
  • cluster and classification models identify similar traits in data and groups like items.
  • decision models simulate the outcomes of decisions so that the user is aware of possible risks associated with each option.
Digital Literacy and Computer Science (2018) Grade(s): 7

DLCS18.7.27

Identify data needed to create a model or simulation of a given event.

Examples: When creating a random name generator, the program needs access to a list of possible names.

Unpacked Content

Knowledge

Students know:
  • an event is comprised of a set of steps or processes that can be identified and then simulated.

Skills

Students are able to:
  • abstract the sequence of activities that make up an event.

Understanding

Students understand that:
  • dissecting the parts of an event can aid in understanding of and the simulation of the event.

Aligned Learning Resources

Computer Science Discoveries Unit 2 Chapter 1 Lesson 2: Websites for Expression (18-19)
Computer Science Discoveries Unit 4 Chapter 1 Lesson 3: User-Centered Design Micro Activity (18-19)
Computer Science Discoveries Unit 4 Chapter 1 Lesson 5: Feedback and Testing (18-19)
Computer Science Discoveries Unit 5 Chapter 2 Lesson 10: Problem Solving with Big Data (18-19)
Computer Science Discoveries Unit 5 Chapter 2 Lesson 15: Project - Solve a Data Problem (18-19)
Computer Science Discoveries Unit 6 Chapter 1 Lesson 1: Innovations in Computing (18-19)
Computer Science Discoveries Unit 6 Chapter 1 Lesson 8: The Program Design Process (18-19)
Computer Science Discoveries Unit 6 Chapter 1 Lesson 9: Make a Game (18-19)
Computer Science Discoveries Unit 6 Chapter 2 Lesson 15: Circuits and Physical Prototypes (18-19)
Computer Science Discoveries Unit 6 Chapter 2 Lesson 16: Prototype an Innovation (18-19)
Visual Learning
What's the Chance?
Balancing Act
Digital Literacy and Computer Science (2018) Grade(s): 8

DLCS18.8.25

Create a model that represents a system.

Example: Food chain, supply and demand.

Unpacked Content

Knowledge

Students know:
  • that systems or processes exist that may be too large to be easily observable and by creating a model of the system or process, one can then use the model in the problem-solving process.

Skills

Students are able to:
  • observe systems or processes in the real world that may require the creation of a model for the purposes of testing.

Understanding

Students understand that:
  • models serve as representations of systems or processes in the problem-solving process.

Aligned Learning Resources

Computer Science Discoveries Unit 5 Chapter 1 Lesson 8: Create a Representation (18-19)
Design Thinking
Digital Literacy and Computer Science (2018) Grade(s): 8

DLCS18.8.26

Create a simulation that tests a specific model.

Examples: Demonstrate that pressure changes with temperature in a controlled environment; demonstrate that rocket design affects the height of a rocket’s launch; demonstrate that the amount of water changes the height of a plant.

Unpacked Content

Knowledge

Students know:
  • what information is important to the simulation.

Skills

Students are able to:
  • create a simulation that tests a specific model.

Understanding

Students understand that:
  • simulations are used to save time and money.
  • simulations serve to recreate processes you otherwise may not be able to.

Aligned Learning Resources

Down on the Farm: A VEX IQ Learning Activity
Digital Literacy and Computer Science (2018) Grade(s): 09-12

DLCS18.HS.37

Evaluate the ability of models and simulations to test and support the refinement of hypotheses.

Unpacked Content

Knowledge

Students know:
  • how to explain the use of models and simulations to generate new knowledge and understanding related to the phenomena or target system that is being studied.
  • how to explain the ability of models and simulations to test and support the refinement of hypotheses related to phenomena under consideration.
a.
  • that modeling and simulations are way to extrapolate and interpolate unrest situation and scenarios to help formulate, test and refine hypotheses.
b.
  • how to form a hypothesis.
  • how to test a hypothesis.
  • how to create a model or simulation.
c.
  • that simulations or models can be created to test a hypothesis but not provide the information expected or intended.
  • that it is vital to verify the data being generated by a model or simulation.

Skills

Students are able to:
  • use a diagram or program to represent a model to express key properties of a phenomena or target system.
  • research existing models and simulations and how they are used to test and refine hypotheses.
  • explain how existing models and simulations are used to test and support the refinement of hypotheses.
a.
  • create a model or simulation to formulate, test, and refine a hypothesis.
  • utilize a model or simulation to formulate, test, and refine a hypothesis.
b.
  • form a model of a hypothesis.
  • test the hypothesis by collecting and analyzing data from a simulation.
c.
  • examine a model or simulation to determine the correctness of the generated data.
  • examine a flawed model or simulation and identify areas in which it is providing incorrect data.

Understanding

Students understand that:
  • a simulation is based on a model and enables observation of the system as key properties change.
  • the accuracy of models and simulations are limited by the level of detail and quality of information used and the software and hardware used.
  • models and simulations are an effective and cost efficient way to understand phenomena and test and refine hypotheses.
a.
  • models and simulations are way to extrapolate and interpolate unrest situation and scenarios to help formulate, test and refine hypotheses.
  • models and simulations can be the only cost- ot time-effective way to test a hypothesis.
b.
  • Models and simulations can save money, are safer, usually requires less time, and do not have the environmental impact that a full experiment or operational test may induce.
c.
  • while a process may operate without errors, that does not guarantee that the process is providing accurate data to meet your needs.

Vocabulary

  • model
  • simulations
  • hypotheses
  • phenomena
  • target system

Aligned Learning Resources

Computer Science Principles Unit 4 Chapter 1 Lesson 2: Finding Trends With Visualizations
Computer Science Principles Unit 4 Chapter 1 Lesson 3: Check Your Assumptions
Computer Science Principles Unit 5 Chapter 2 Lesson 12: Loops and Simulations
Computer Science Principles Unit Post AP Chapter 1 Lesson 1: Introduction to Data
Computer Science Principles Unit Post AP Chapter 1 Lesson 2: Good and Bad Data Visualizations
Computer Science Principles Unit Post AP Chapter 1 Lesson 3: Making Data Visualizations
Computer Science Principles Unit Post AP Chapter 1 Lesson 4: Discover a Data Story
Computer Science Principles Unit Post AP Chapter 1 Lesson 5: Cleaning Data
Computer Science Principles Unit Post AP Chapter 1 Lesson 7: Practice PT - Tell a Data Story
Software Engineering
Modeling & Simulation: Nodes and Graphs

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