This lesson links the complex adaptive systems and modeling and simulation concepts to a topic students are very interested in - contagion!  Use their interest in the topic to motivate the creation of a model in which a "disease" gets passed from one agent to another.  It is important to remember that your role as a teacher is to help students build self-sufficiency as problem solvers.

We found it helpful to review and reinforce these concepts during Lesson 5:
Q: Why is rolling a 1 2 3 4 on a 10-sided die happen 40% of the time?
A: The probability of rolling a 1 on a 10-sided die is 1 out of 10 or 10%, so when you add up the chances of rolling each of the numbers (1, 2, 3, or 4) you get 10% + 10% + 10% + 10% = 40%.  So the chance (or probability) of rolling a 1, 2, 3, or 4 on a 10-sided die is 40%.
Q: Why is a collision block used for transmission whereas a separate procedure block is needed for recovery?  
A: Transmission, or spread of disease, occurs when one agent bumps into another agent, thus in this case we can use the collision block.  Recovery is not dependent on a collision.  Instead each infected agent has a slight chance of recover at each tick of the clock.  So, we need to put these commands outside of a collision block.  A procedure block can be used to create a separate set of instructions for recovery that can be called whenever needed.

Assignment:
Review the activities from Lesson 5 as well as the materials below.  Reflect on how you would teach this lesson in your class.  Post your reflection to your portfolio in "Pedagogy->Module 1" under the heading "Lesson 5."

Learning Objectives

The student will:

-   Learn about epidemiology and how it can be modeling as a complex system (LO27)

-    Create a simple model in which agents pass a contagion from one toanother (LO28)

-    Learn the CS concepts of procedures and variables (LO29)

-    Create and use sliders to set variables and initial conditions (LO30)

-    Create procedures and call procedures (LO31)

-    Use the random function to simulate probabilistic outcomes. (LO32)

Teaching Summary

Getting started – 5 minutes (Review)

1.     Review of the previous day’s lesson and concepts and connection to today’s lesson.

            Activity 1: Introduction of Epidemiology - 25 minutes (New Learning)

2.     Epidemiology and the Spread of Disease

            Activity 2: Modeling the Spread of Disease - 25 minutes (Guided Practice and Discovery)

3.     New concepts and commands.

4.     Altering Colliding Turtles to an Epidemic Model and Adding Widgets

5.     Customizing your model [Adding in Recovery]

6.     Test your model

            Wrap-up - 5 minutes (Reflection)

7.     What does this model tell you? Can it be trusted?

8.     What other things move through a population like a disease? [Rumors, ideas, fads, etc.] ?

Assessment questions (suggestions):

• Is the epidemic model a model of a complex adaptive system?  Why or why not?
• What variables were used in your epidemic model?
• How would you test whether how much an agent travels impacts the spread of disease?
• How many times do I need to run each experiment at each setting?
  

NGSS Scientific and Engineering Practice Standards

Practice 1: Asking questions and defining problems 1A: Ask questions that arise from careful observation of phenomena, models, or unexpected results. 1C: Ask questions to determine relationships between independent and dependent variables and relationships in models. Practice 2: Developing and using models 2A: Evaluate limitations of a model for a proposed object or tool. 2B: Develop or modify a model—based on evidence – to match what happens if a variable or component of a system is changed. 2C: Use and/or develop a model of simple systems with uncertain and less predictable factors. 2D: Develop and/or revise a model to show the relationships among variables, including those that are not observable but predict observable phenomena. 2E: Develop and/or use a model to predict and/or describe phenomena. 2F: Develop a model to describe unobservable mechanisms. 2G: Develop and/or use a model to generate data to test ideas about phenomena in natural or designed systems, including those representing inputs and outputs, and those at unobservable scales. Practice 3: Planning and carrying out investigations 3D: Collect data to produce data to serve as the basis for evidence to answer scientific questions or test design solutions under a range of conditions. 3E: Collect data about the performance of a proposed object, tool, process or system under a range of conditions. Practice 4: Analyzing and interpreting data 4A: Construct, analyze, and/or interpret graphical displays of data and/or large data sets to identify linear and nonlinear relationships. 4B: Use graphical displays (e.g., maps, charts, graphs, and/or tables) of large data sets to identify temporal and spatial relationships. 4D: Analyze and interpret data to provide evidence for phenomena. Practice 5: Using mathematics and computational thinking 5A: Use digital tools (e.g., computers) to analyze very large data sets for patterns and trends. 5B: Use mathematical representations to describe and/or support scientific conclusions and design solutions. 5C: Create algorithms (a series of ordered steps) to solve a problem. 5D: Apply mathematical concepts and/or processes  (e.g., ratio, rate, percent, basic operations, simple algebra) to scientific and engineering questions and problems. Practice 6: Constructing explanations and designing solutions 6A: Construct an explanation that includes qualitative or quantitative relationships between variables that predict(s) and/or describe(s) phenomena. 6B: Construct an explanation using models or representations. 6D: Apply scientific ideas, principles, and/or evidence to construct, revise and/or use an explanation for real-world phenomena, examples, or events. 6F: Apply scientific ideas or principles to design, construct, and/or test a design of an object, tool, process or system. 6G: Undertake a design project, engaging in the design cycle, to construct and/or implement a solution that meets specific design criteria and constraints.

 

NGSS Crosscutting Concepts

1. Patterns: 1B: Patterns in rates of change and other numerical relationships can provide information about natural and human designed systems. 1C: Patterns can be used to identify cause and effect relationships. 1D: Graphs, charts, and images can be used to identify patterns in data. 2. Cause and Effect: 2A: Relationships can be classified as causal or correlational, and correlation does not necessarily imply causation. 2B: Cause and effect relationships may be used to predict phenomena in natural or designed systems. 2C: Phenomena may have more than one cause, and some cause and effect relationships in systems can only be described using probability. 3. Scale, Proportion, and Quantity 3A: Time, space, and energy phenomena can be observed at various scales using models to study systems that are too large or too small. 3B: The observed function of natural and designed systems may change with scale. 3D: Scientific relationships can be represented through the use of algebraic expressions and equations. 3E: Phenomena that can be observed at one scale may not be observable at another scale. 4. Systems and Systems models 4A: Systems may interact with other systems; they may have sub-systems and be a part of larger complex systems. 4B: Models can be used to represent systems and their interactions—such as inputs, processes and outputs—and energy, matter, and information flows within systems. 4C: Models are limited in that they only represent certain aspects of the system under study.

 

CSTA K-12 Computer Science Standards

CT	Abstraction	2-12	Use abstraction to decompose a problem into sub problems.
CT	Abstraction	3B-10	Decompose a problem by defining new functions and classes.
CT	Algorithms	3A-3	Explain how sequence, selection, iteration and recursion are the building blocks of algorithms.
CT	Connections to other fields	2-15	Provide examples of interdisciplinary applications of computational thinking.
CT	Data representation	2-8	Use visual representation of problem state, structure and data.
CT	Data representation	3A-12	Describe how mathematical and statistical functions, sets, and logic are used in computation.

Responsiveness to varied student learning needs:

In Project GUTS, we integrate teaching strategies found to be effective with learners with various backgrounds and characteristics such as economically disadvantaged students (EDS), students from underrepresented groups in STEM (URG) , students with disabilities (DIS), English Language learners (ELL), girls and young women (FEM), students in alternative education (ALT), and gifted and talented students (GAT). In each lesson we describe the accommodations and differentiation strategies that are integrated in the activities to support a wide range of learners.

Module 1 Lesson 5 Activity #1: Introduction to Epidemiology

(EDS) In the Introduction to Epidemiology we elicit students’ prior knowledge of getting a communicable disease and build on their funds of knowledge as a resource for further questioning and investigating.

(EDS) We validate the sense of place [aspects of the neighborhood and school] to keep the students engaged and make a connection of science and neighborhood by using the example of Community Associated MRSA.   We refer to the cultural context of school and community, neighborhoods when discussion the model of CA-MRSA and what was included in the model.

(URG)(DIS) We use technology to present information in multiple modes of representations. In the StarLogo Nova modeling and simulation environment students can present information as code blocks, text, graphical display of the simulation, and as data in tables and graphs.

(FEM)  We chose a curriculum topic, epidemiology, that had relevancy and real-world application, a strategy to interest and engage girls and students from underrepresented groups in STEM.

(ALT) We focus on career connections, the work of epidemiologists,  a life skills strategy that is promoted in alternative education.

Module 1 Lesson 5 Activity #2: Modeling the Spread of Disease (Contagion model)

(EDS)(URG)(ELL) We ask students to apply what they know, specific to their cultural and/or socio-economic context, when addressing issues related to the spread of disease and the realism of models.  We can ask what is assumed in this model, what is realistic to the students, and what is not. This practice can also highlight home culture connection to science by capitalizing on funds of knowledge from the students’ homes and communities.

(URG)(DIS) We use technology to present information in multiple modes of representations. In the StarLogo Nova modeling and simulation environment students can present information as code blocks, text, graphical display of the simulation, and as data in tables and graphs.

(URG) We can connect science to locally meaningful issues that could promote community involvement and social activism. When discussing what else spreads like a disease, note that other trends and phenomena are studied as contagions.  Examples include gossip, gun violence, fads and fashions, etc.

(ELL) The “place-based” nature of this lesson establishes connections between school science and the students’ community and lives.

(FEM)  We chose a curriculum topic, epidemiology, that had relevancy and real-world application, a strategy to interest and engage girls and students from underrepresented groups in STEM. Careful planning of partners for the on-computer activity is a strategy that encourages participation for the girls in science.