Teaching Lesson 6

Watch this video on preparing students for their presentations

This lesson introduces the important concepts of data collection and analysis.
Students will learn how to add instruments to a model and visualize the data in a graph.  Since these are common tasks do not involve creativity or problem solving, direct instruction can be used to show students the procedure for adding monitors and graphs.  Next students will create their own experiments, run experiments, then collect and analyze their data.  Use the Experimental design form as a guide during this phase. We find that when developing questions to ask of a model, it is helpful to give some examples and do a class brainstorming of possible questions to ask the model.

Finally, consider setting up a time when students can demonstrate their projects to fellow students, parents, and/or community members. 

Review the activities from Lesson 6 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 6."

Learning Objectives

The student will:

- Learn the difference between qualitative vs. quantitative results (LO33)

- Learn how to instrument a model with a line graph (LO34)

- Learn experimental design using a computer model (LO35)

- Conduct experiments using a model as an experimental test bed  (LO36)

- Record and analyze results (LO37)

- Ask questions that arise from observations of your model’s behavior (LO38)

Teaching Summary

Getting started – 5 minutes

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


                Activity 1: Instrumenting your model – 10 minutes

2.     Review qualitative vs. quantitative data

3.     Add a line graph

4.     Test your model


                Activity 2: Running experiments – 30 minutes

5.     Designing experiments

6.     Running experiments

7.     Collecting and analyzing data


                Wrap-up – 5 minutes

8.     What patterns did you uncover? What conditions or settings led to each pattern? When you run the model with the same input setting (for transmission rate and recovery rate) do you always get the same result or outcome?   Why or why not?

Assessment questions (suggestions):
  • Is the epidemic model a model of a complex adaptive system?  Why or why not?
  • What variables were included in your epidemic model?
  • What was included and what was missing from your model?  Name two things that happen in real life that are not included in this model.
  • How would you change the model to one in which sick agents get healthy again after colliding into a healthy agent?
  • How would you modify the model to help you study a real-world disease?

NRC 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.

1B: Ask question to identify and/or clarify evidence and/or the premise(s) of an argument.

1C: Ask questions to determine relationships between independent and dependent variables and relationships in models.

1F: Ask questions that can be investigated within the scope of the classroom, outdoor environment, and based on observations and scientific principles.


Practice 2: Developing and using models

2C: Use and/or develop a model of simple systems with uncertain and less predictable factors.

2E: Develop and/or use a model to predict and/or describe phenomena.

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

3A: Plan an investigation individually and collaboratively, and in the design: identify independent and dependent variables and controls, what tools are needed to do the gathering, how measurements will be recorded, and how many data are needed to support a claim.

3B: Conduct an investigation and/or evaluate and/or revise the experimental design to produce data to serve as the basis for evidence that meet the goals of the investigation.

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.


Practice 4: Analyzing and interpreting data

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.

4E: Apply concepts of statistics and probability (including mean, median, mode, and variability) to analyze and characterize data, using digital tools when feasible.

4F: Consider limitations of data analysis (e.g., measurement error), and/or seek to improve precision and accuracy of data with better technological tools and methods (e.g., multiple trials).

4G: Analyze and interpret data to determine similarities and differences in findings.


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.

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

6D: Apply scientific ideas, principles, and/or evidence to construct, revise and/or use an explanation for real-world phenomena, examples, or events.

6E: Apply scientific reasoning to show why the data or evidence is adequate for the explanation or conclusion.



Practice 7: Engaging in argument from evidence

7C: Construct, use, and/or present an oral and written argument supported by empirical evidence and scientific reasoning to support or refute an explanation or a model for a phenomenon or a solution to a problem.


Practice 8: Obtaining, evaluating, and communicating information

8E: Communicate scientific and/or technical information (e.g. about a proposed object, tool, process, system) in writing and/or through oral presentations.

NRC 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 affect relationships.

1D: Graphs, charts, and images can be used to identify patterns in data.


2. Cause and Effect:

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.


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


Connections to other fields


Provide examples of interdisciplinary applications of computational thinking.


Data representation


Use visual representation of problem state, structure and data.


Modeling & simulation


Describe how a simulation can be used to solve a problem.


Modeling & simulation


Analyze the degree to which a computer model accurately represents the real world.


Modeling & simulation


Interact with content-specific models and simulations to support learning and research.


Modeling & simulation


Use modeling and simulation to represent and understand natural phenomena.


Modeling & simulation


Analyze data and identify patterns through modeling and simulation.


Data collection & analysis


Collect and analyze data that are output from multiple runs of a computer program.


Data collection & analysis


Use data analysis to enhance understanding of complex natural and human systems.




Use various debugging and testing methods to ensure program correctness.




Apply analysis, design and implementation techniques to solve problems.

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 groups that are underrepresented 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 6: Adding Instrumentation and Running Experiments with Your Model

(URG) Students are given “agency” as the creators of their own models, and as researchers seeking to answer a question or understand a phenomenon. The models that students build are their own creations.


(FEM) Careful planning of partners for the on-computer activity is a strategy that encourages participation for the girls in science.


(DIS)(GAT) There is ample opportunity to extend level of content and time for practice by compacting areas already mastered and to allow more time for students to complete previous experiments and customizations.  This differentiation strategy of pacing can benefit a wide range of students, including those with learning disabilities and/or gifted and talented students.


(GAT) The teacher can promote autonomy and the forging of authentic connections to the epidemiology content and to the practice of computer modeling.


(GAT) Grouping students of similar interests and ability, incorporating standards from a higher grade, and providing opportunities for self-directed projects are successful strategies for gifted and talented students.