Objective: 

Students will create flowcharts to explore the factors leading to the growth of cities in the Northeast due to industrialization, immigration, and geography, using conditional thinking to model cause-and-effect relationships.

Materials Needed: 

• Large chart paper

• Markers

• Pre-cut shapes for flowchart symbols (rectangles for processes, diamonds for decisions)

• Printed historical data on immigration and city growth in the Northeast

Steps:

• Introduction: 

  • Students explore the rapid growth of cities in the Northeast from 1800 to the mid-1800s, focusing on the role of industrialization, immigration (e.g., the Irish migration due to the Great Irish Famine), and geography. 

  • Introduce flowcharts as a tool to model these historical processes, explaining that students will use conditionals to identify the causes and outcomes of various factors.

• Group Activity: 

  • Divide students into small groups and provide them with flowchart symbols. 

  • Each group will create a flowchart representing the factors that led to the growth of a specific Northeastern city (e.g., New York or Boston). 

  • They will start with a central event, like the introduction of factories, and use conditional decision points (e.g., “Did the city have access to waterways?”) to show how geography and immigration shaped city growth. 

  • As students create their flowcharts, they must identify conditionals that illustrate cause-and-effect relationships, such as how the influx of immigrants influenced the labor force and urban expansion.

• Discussion: 

  • Once the flowcharts are complete, each group will present their model and explain the conditional decisions they used to demonstrate the causes and effects of city growth. 

  • Facilitate a class discussion on how using conditionals helps break down the complex historical factors into simpler, logical steps.

Equity and Access: 

Provide pre-labeled flowchart templates for students who need additional support and pair students with different levels of understanding to foster peer collaboration.

Real-World Application: 

Relate this activity to how modern urban planners use similar tools (flowcharts and conditionals) to model and predict city growth based on factors like geography, economy, and population trends.

CS Practice(s):

• Recognizing and Defining Computational Problems: Students break down the complex historical process of city growth into manageable steps using conditionals in a flowchart.

• Developing and Using Abstractions: Students abstract the relationships between geography, industrialization, and immigration into a logical sequence of decisions and outcomes.

Standard(s):

• CA HSS 8.6.1

• CA HSS 8.6.3

• CA CS 6-8.AP.10

Objective: 

Students will use programmable robots to model the development of transportation networks in the early 1800s, decomposing the problem into smaller subproblems such as navigating geographic obstacles and optimizing routes.

Materials Needed: 

• Robots (e.g., LEGO Mindstorms or Sphero)

• Computers or tablets with programming software

• Large floor map of the Northeastern U.S.

• Markers, tape or objects to represent rivers, mountains, and cities

Steps:

• Introduction: 

  • Begin by discussing the challenges of building transportation networks in the early 1800s, such as overcoming geographic obstacles (e.g., rivers, mountains) and optimizing routes between cities. 

  • Explain that today, students will use programmable robots to simulate the process of designing a transportation network, and they will decompose this large problem into smaller subproblems to make it more manageable.

• Coding Activity: 

  • In small groups, students will break down the task of creating a transportation network into smaller parts, such as (1) navigating around or across rivers, (2) moving over or through mountains, and (3) choosing the most efficient route between cities. 

  • Each group will assign one subproblem to a team member, and after individually coding solutions for their subproblem, the group will combine their code into a full program that simulates the entire transportation network. 

  • For example, one student may code the robot to cross a river, while another focuses on the robot’s navigation through mountainous terrain.

• Testing and Refining: 

  • Once the subproblems are individually solved, students will combine their code and test the robot’s performance on the floor map. 

  • If the robot fails to navigate the entire network smoothly, students will return to their subproblems, refine their solutions, and adjust how their pieces of code integrate into the overall program. 

  • This iterative process mirrors how engineers refine transportation designs and software developers improve code.

• Discussion: 

  • After completing the activity, each group will present how they decomposed the problem and solved individual subproblems. 

  • Lead a discussion on how breaking down complex tasks into smaller, manageable parts helps solve real-world problems, both in engineering and in programming.

Equity and Access: 

Provide pre-labeled subproblems for students needing additional support, and encourage collaboration by pairing students with varying levels of coding experience.

Real-World Application: 

Relate this activity to how modern engineers and programmers break down large projects into smaller tasks, such as designing different components of a transportation system or developing various parts of a computer program.

CS Practice(s):

• Testing and Refining Computational Artifacts: Students test and refine their robot's performance by adjusting code to overcome geographic obstacles, reflecting the iterative process of engineering.

• Collaborating around Computing: Students work together to design and program their robots, sharing ideas and problem-solving strategies to simulate the development of transportation networks.

Standard(s):

• CA HSS 8.6.2

• CA CS 6-8.AP.13