Objective:

Students will simulate the uneven distribution of Earth’s natural resources and practice computational thinking by breaking down the processes that impact resource availability, connecting their work to how computational models could solve real-world problems.

Materials Needed:

• Paper

• Markers, and other craft supplies for creating models

• Worksheets with information about Earth’s resources and geoscience processes

Steps:

• Introduction: 

  • Begin by discussing Earth's natural resources and how geoscience processes (like volcanic activity, sedimentation, and tectonic movements) lead to uneven distribution. 

  • Ask students how scientists might use technology to visualize and track these resources.

• Group Activity: 

  • In groups, students select a specific resource (e.g., petroleum, metals) and break down the natural processes that impact its distribution using computational thinking. 

  • They create paper-based models to represent these processes, highlighting key steps like extraction and environmental impact.

• Presentation and Discussion: 

  • Groups present their models, explaining the computational steps involved in resource distribution. 

  • As a class, discuss how technology could automate this analysis and visualize data in a more efficient way. 

  • Students identify how computer models could represent the same data they have created manually.

Real-World Application: 

Lead a discussion about the economic and environmental trade-offs of resource extraction and how technology is used in the real world to balance economic gain with environmental preservation. Discuss how computational models help governments and companies make informed decisions.

Equity and Access:

Provide pre-prepared materials or simplified models for students who may need extra support. Pair students with varying levels of experience to ensure collaboration and peer learning.

CS Practice(s):

• Recognizing and Defining Computational Problems: Students break down the complex processes of resource distribution into smaller steps, mirroring how computational models are designed.

• Developing and Using Abstractions: Students use visual models to abstract the processes behind resource distribution, mirroring how data is represented digitally in computational models.

Standard(s):

• CA NGSS MS-ESS3-1

• CA CS 6-8.DA.7

• CA CS 6-8.IC.20

Objective:

Students will use Pencil Code, p5.js, or spreadsheet software to create data visualizations of natural hazards and analyze the effects of changing variables to predict future catastrophic events.

Materials Needed:

• Computers 

• Access to real-world datasets on natural hazards

Steps:

• Introduction: 

  • Begin by discussing different types of natural hazards (e.g., earthquakes, hurricanes) and how technology helps forecast these events. 

  • Explain that students will use computational tools to visualize real-world hazard data and explore ways to predict future events.

• Group Activity: 

  • In pairs, students access datasets on natural hazards and input the data into a computational tool. 

  • They create visual representations like graphs or maps, analyzing the frequency, location, and magnitude of each hazard. 

  • Students then adjust variables to see how changes in the data affect future predictions.

• Testing and Refining: 

  • Students test different variables in their models (e.g., location, time, severity) to explore how reliable predictions can be made. 

  • They refine their code to improve the accuracy of their predictions and compare their results with real-world technological solutions like satellite monitoring systems.

Real-World Application: 

Lead a class discussion on the trade-offs of using automated systems for hazard prediction, such as the accuracy of satellite data versus human monitoring, and the economic impact of such technologies.

Equity and Access:

Provide access to pre-made code templates for students who need extra support, and ensure that datasets are accessible to all reading levels.

CS Practice(s):

• Testing and Refining Computational Artifacts: Students adjust variables in their simulations to improve the accuracy of hazard predictions.

• Creating Computational Artifacts: Students create visual data models in computational tools based on real-world data.

Standard(s):

• CA NGSS MS-ESS3-1

• CA CS 6-8.DA.8

• CA CS 6-8.DA.9