Classroom Resource

Computer Science Principles Unit Post AP Chapter 1 Lesson 2: Good and Bad Data Visualizations

Subject Area

Digital Literacy and Computer Science

Grade(s)

9, 10, 11, 12

Overview

This is a pretty fun lesson that has two main parts. First students warm up by reflecting on the reasons data visualizations are used to communicate about data. This leads to the main activity in which students look at some collections of (mostly bad) data visualizations, rate them, explain why a good one is effective, and also suggest a fix for a bad one.

In the second part of the class, students compare their experiences and create a class list of common faults and best practices for creating data visualizations. Finally, students review and read the first few pages of "Data Visualization 101: How to design charts and graphs" to see some basic principles of good data visualizations and see how they compare with the list the class came up with.

Students will be able to:
- identify an effective data visualization and give justification.
- collaborate to investigate and evaluate a data visualization.
- suggest an appropriate visualization for some data.
- evaluate a data visualization for the effectiveness of communication.
- identify a poor data visualization and give justification.

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Digital Literacy and Computer Science (2018) Grade(s): 09-12

DLCS18.HS.32

Use data analysis tools and techniques to identify patterns in data representing complex systems.

Unpacked Content

UP:DLCS18.HS.32

Vocabulary

  • datamining

Knowledge

Students know:
  • how to identify patterns in data.
  • how to select and apply data analysis tools and techniques.
  • use data analysis tools and techniques to identify patterns in data representing complex systems.

Skills

Students are able to:
  • evaluate data sets.
  • select and apply data analysis tools and techniques.
  • use technology to mine data.

Understanding

Students understand that:
  • data can be important in a problem
  • solving process.
  • tools exists to aid in the processing of complex data sets.
  • it can be more efficient to allow a program to identify patterns in a complex data set.
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

UP:DLCS18.HS.37

Vocabulary

  • model
  • simulations
  • hypotheses
  • phenomena
  • target system

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.
Link to Resource
https://curriculum.code.org/csp-18/post-ap/2/

CR Resource Type

Lesson/Unit Plan

Resource Provider

Code.org
Accessibility
License

License Type

Custom

Custom License Type (URL)

https://code.org/tos

Approved Date

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