Teaching Lesson 5

This lesson will not only help deepen the concepts that are important in a chemical reaction (stirring, heat, complex ions), but also deepen the understanding of how to add more elements to a model so it becomes more sophisticated and a step closer to reality. There are many options to make the model more sophisticated and to code in observations found in the actual reaction, from the effect of mixing to the blue hue of the solution forming to the effect of heat on the rate of the reaction. From the video or live demonstration you might notice something else that you want to add to the model, for example shaking off the silver deposit so that it precipitates to the bottom.

Review the activities from Lesson 5 as well as the material below. Reflect on how you would teach this in your class. Post your reflection to your portfolio in "Pedagogy->Module 4" under the heading Lesson 5.

Lesson Objectives

The student will:

- Review findings on the rate of reaction from the last lesson [LO16]

- Learn how agent movement can impact outcome in simulations [LO17]

- Learn to simulate kinetic energy in an agent-based model [LO18]

- Run experiments to determine how kinetic energy impacts the rate of chemical reaction [LO19]

- Become familiar with rate of a reaction and kinetic energy effect [LO20]


Teaching Summary

Getting started – 10 minutes

1.     Availability of reactants as a limiting factor      


Activity #1: Factors impacting the rate of reaction – 15 minutes

2.     Mixing – the simulation of movement

3.     Step size – the simulation of kinetic energy


Activity #2: Running experiments – 15 minutes

4.     Impacts of mixing or step size


Wrap-up – 10 minutes

5.     Sharing results and conclusions

Assessment questions (suggested):

      What are some factors that impact the rate of reaction?

      How would you design an experiment to determine the impact of one of the factors?

      How was an increase in kinetic energy simulated in this lesson?

      Describe a feedback loop in the chemical reaction studied.

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.

1D: Ask questions to clarify and/or refine a model, an explanation, or an engineering problem.

1E: Ask questions that require sufficient and appropriate empirical evidence to answer.

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

1G: Ask questions that challenge the premise(s) of an argument or the interpretation of a data set.


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

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

4D: Analyze and interpret data to provide evidence for phenomena.

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.

4H: Analyze data to define an optimal operational range for a proposed object, tool, process or system that best meets criteria for success.


Practice 5: Using mathematics and computational thinking

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.

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

6G: Undertake a design project, engaging in the design cycle, to construct and/or implement a solution that meets specific design criteria and constraints.

6H: Optimize performance of a design by prioritizing criteria, making tradeoffs, testing, revising, and re-testing.


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 Disciplinary Core Ideas

PS1.A: Structure and Properties of Matter

   Substances are made from different types of atoms, which combine with one another in various ways. Atoms form molecules that range in size from two to thousands of atoms.

   Each pure substance has characteristic physical and chemical properties (for any bulk quantity under given conditions) that can be used to identify it.

   Gases and liquids are made of molecules or inert atoms that are moving about relative to each other.

   In a liquid, the molecules are constantly in contact with others; in a gas, they are widely spaced except when they happen to collide. In a solid, atoms are closely spaced and may vibrate in position but do not change relative locations.

   Solids may be formed from molecules, or they may be extended structures with repeating subunits (e.g., crystals).

PS1.B: Chemical Reactions

   Substances react chemically in characteristic ways. In a chemical process, the atoms that make up the original substances are regrouped into different molecules, and these new substances have different properties from those of the reactants.

   The total number of each type of atom is conserved, and thus the mass does not change. Some chemical reactions release energy, others store energy.

NRC Crosscutting Concepts

1. Patterns:

1A: Macroscopic patterns are related to the nature of microscopic and atomic-level structure.

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


2. Cause and Effect:

2B: Cause and effect relationships may be used to predict phenomena in natural or designed systems.


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.

3C: Proportional relationships (e.g., speed as the ratio of distance traveled to time taken) among different types of quantities provide information about the magnitude of properties and processes.

3E: Phenomena that can be observed at one scale may not be observable at another scale.


4. Systems and Systems models

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.


5. Energy and Matter:

5A: Matter is conserved because atoms are conserved in physical and chemical processes.


6. Structure and Function

6A: Complex and microscopic structures and systems can be visualized, modeled, and used to describe how their function depends on the shapes, composition, and relationships among its parts; therefore, complex natural and designed structures/systems can be analyzed to determine how they function.


7. Stability and Change:

7A: Explanations of stability and change in natural or designed systems can be constructed by examining the changes over time and forces at different scales, including the atomic scale.


CSTA K-12 Computer Science Standards




Use abstraction to decompose a problem into sub problems.




Discuss the value of abstraction to manage problem complexity.




Decompose a problem by defining new functions and classes.




Evaluate ways that different algorithms may be used to solve the same problem.


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


Evaluate the kinds of problems that can be solved using modeling and simulation.


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.


Data collection & analysis


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




Implement a problem solution in a programming environment using looping behavior, conditional statements, logic, expressions, variables and functions.




Use various debugging and testing methods to ensure program correctness.




Apply analysis, design and implementation techniques to solve problems.