# An Investigation of Students’ Use of a Computational Science Simulation in an Online High School Physics Class

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## Abstract

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## 1. Introduction

## 2. Literature Review

#### 2.1. Models and Simulations in Science Education

#### 2.2. Work with Data in the Context of Simulations

#### 2.3. Using Simulations in Online Science Classes

#### 2.4. Framework for Using a Computational Science Simulation

## 3. Research Questions

**RQ1:**How do students’ explanations change over the course of three lessons that involve using a simulation and modeling its output?**RQ2:**What do students perceive the strengths and weaknesses of using the simulation to be?**RQ3:**How do students approach modeling the classroom data set to account for its variability?

## 4. Methods

#### 4.1. Participants and Context

#### 4.2. Simulation Design

#### 4.3. Lesson Sequence

#### 4.3.1. Lesson 1: Tinker with the Simulation

In this activity, students accessed the simulation (presented in Figure 2; see Rosenberg, 2016, [40] for a link to the simulation).Think about when a classmate of yours peels an orange, or, in science class, when a yucky smell is introduced or encountered in an investigation. Classmates who are close to the source of this smell initially report its presence. Gradually, the smell makes its ways throughout the room, and everyone has sensed it. What’s going on? How exactly does smell move through the room? Can we describe these phenomena based on our prior experiences - or do we need to conduct an experiment? Can the way that smell moves through the room be represented in a graphical depiction or even a formula? Is there some “speed of smell?”

Students responded to questions that prompted them to generate ideas about what they understood about how temperature, pressure, and volume are related and what they thought would happen in the simulation both before and after tinkering with it.In our model, you are free to think of all molecules as solid billiard balls that can collide with each other as they move randomly in straight lines through the room. The collisions between the billiard balls are “elastic,” so the total velocity (and mass) of all of the molecules is specified to be the same throughout the simulation remains the same, as well as the total kinetic energy of all the molecules. The green billiard balls on the right represent the air molecules throughout the room, the gray wall represents the closed perfume jar, and the blue billiard balls on the left represent tiny molecules of perfume floating in the air which (when the jar is opened, and the wall is removed) will eventually make their way toward the “gas sensor” also known as your nose. All of the molecules have the same mass.

#### 4.3.2. Lesson 2: Collect Data from the Simulation

This simulation is not programmed with any gas laws or thermodynamic equations. It is simply modeling the motion of gas particles as though they were rigid billiard balls colliding with one another. Different pieces of information are available to us, from the total kinetic energy, in electron volts, to the pressure, volume, and temperature. We could use this simulation to examine the relationship between temperature and pressure. We could also examine the relationship between kinetic energy and temperature; since the volume, which here represents the volume of the container (including the volume behind the barrier); to calculate the volume, a depth 1 molecule deep is used. This simulation could be much more complex with greater depth!

#### 4.3.3. Lesson 3: Generate a Model-based Explanation Using a Class Dataset

Imagine you want to “model” a relationship, such as how the number of likes of your photos on your social network is related to the number of times you post each day. One way you could do this is through a “line of best fit,” or a linear model. A line of best fit is a simple but powerful model: This model is simply a straight line through a scatterplot of data. If you look at the individual data points, you may notice that some are above the line, and some are below it; overall, the distance from the line to each point above the line and the distance from the line to each point below it will equal zero; as a result, if you have data points that are way higher than the other points, or way lower, they can affect where the line is. Something we can use to determine how well a line fits is called the coefficient of determination, or R^{2}. The closer R^{2}is to 1 (which means the lines fits perfectly through every point!), the better. The R^{2}for the line above is 0.60.

#### 4.4. Data Sources and Collection

#### 4.5. Data Analysis

## 5. Findings

#### 5.1. Findings for RQ1: Development of Student Responses Over the Course of the Lesson Sequence

#### 5.1.1. Students Who Began with Sophisticated Conceptual Understanding

Because the temperature of a substance is proportional to its kinetic energy. Also, the kinetic energy of a substance is proportional to its mass and velocity. And velocity is what we are interested in if we want to know how quick a gas will reach another place. If the perfume has a lower temperature, the air will transfer its temperature with collisions, causing the perfume to go at a higher speed.

#### 5.1.2. Students Who Showed Improvement in Sophistication

#### 5.1.3. Students Who Showed Little Improvement in the Sophistication of Their Responses

#### 5.2. Findings for RQ2: Strengths and Weaknesses of the Computational Science Simulation

#### 5.3. Findings for RQ3: How Students Approached Modeling the Data to Account for its Variability

^{2}. I used the 6th degree of the polynomial function, this is the most accurate for the scatter plot since a polynomial of the 6th degree is more ‘flexible’ than other functions.”

This response indicates that the student balanced between modeling as much of the variability as possible (using a 4th-degree polynomial function), but still considered the practicality of their model in terms of being able to make meaningful predictions about phenomena (either phenomena represented within the simulation or the wider world outside of the simulation). Student 8 demonstrated through the creation of their model (see Figure 3) a similar consideration between modeling the variability in the data and choosing a model that was interpretable in light of the data.I used this 4th power polynomial function because it had a much higher R^{2}value than the linear, exponential, and lower degree functions. Though some of the higher degree functions had better R^{2}values, it only increased from 0.809 to 0.815 which is pretty insignificant. Any degree function lower than 4 and the R^{2}values began to drop significantly, close to around 0.7 and below. Having a polynomial function with a higher degree of 4 makes it really laborious to manipulate and use to predict values.

^{2}value relative to other candidate models and how well it visually fit the data. This student wrote, “I chose the exponential function because it had the second closest R

^{2},” explaining, “I didn’t choose the first highest, the polynomial function, because the trend line started dipping up in the end and the data points didn’t follow the trend.”

## 6. Discussion

#### 6.1. Limitations

#### 6.2. Recommendations for Future Research

#### 6.3. Implications for Practice

## 7. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Use of a computational science simulation as manipulating and analyzing simulation output to explain and predict phenomena.

**Figure 2.**The Lab Interactive simulation that we designed for this study based on an existing simulation about the diffusion of molecules.

**Figure 3.**Student 8’s model of the relationship between experiment time and temperature using the class dataset.

Research Questions and Sub Questions | Embedded Assessment Questions (Lesson Collected) |
---|---|

RQ1: How do students’ explanations change over the course of three lessons that involve using a simulation and modeling its output? | When the temperature of the room is increased, the perfume particles reach the gas sensor more quickly. Explain why you think this happens. (Lesson 1) |

Based on your observations of the simulation, explain why the perfume particles reach the gas sensor more quickly when the temperature of the room is increased. (Lesson 1) | |

Earlier, we asked you why when the temperature of the room is increased the perfume particles reach the gas sensor more quickly. Please explain why this happens now, knowing what you know from working through data from the simulation. (Lesson 3) | |

RQ2: What do students perceive the strengths and weaknesses of using the simulation to be? | What are some of the benefits of this model of a gas? And what are some of its weaknesses? (Lesson 1) |

How does your model of data help you explain what is going on? Why? (Lesson 3) | |

RQ3: How do students approach modeling the classroom data set to account for its variability? | Explain why you selected this mathematical model as the best fit to the data. (Lesson 3) |

Time Collected | Group 1 (Student 2) | Group 2 (Student 7) | Group 3 (Student 8) |
---|---|---|---|

Lesson 1: Before exploring the simulation | This happens because a higher temperature means that the gas particles vibrate more quickly (have more kinetic energy) so they move faster and can get across the room in less time. | Temperature is directly related to energy: as temperature increases, the energy of the system increases. This results in an increase in kinetic energy, and since mass is constant, the velocity will increase throughout the whole system, reducing the time it takes to travel across a fixed distance. | I think that when the temperature rises, the molecules have more energy and move faster, hitting each other more often. |

Lesson 1: After the exploring the simulation | This seems to be the case because the molecules do move less when they are cold, so it takes longer for the smell to work its way across the chamber. When they are hot the move much more quickly and are able to bounce around from one side of the room to the other quite easily. | Higher temperature → more energy → more collisions in a given amount of time → gets across the fixed distance faster | When the temperature is increased there is more energy in the particles, so they move faster and hit each other and other surfaces more often, so the gas reaches the sensor sooner. |

Lesson 3: After completing the lesson sequence. | This happens because the particles are travelling at a greater velocity. Since velocity is distance over time, and they have to cover the same distance, the time must decrease to factor in this increased velocity. Though there are particles in the way of the perfume and the sensor, they are physically able to travel faster, so assuming the bounces that they go through are random in each scenario, this should slow each temperature particles equally, and so it comes down to the velocity of the particles. | The model shows us that as temperature increases the time for the particles to reach the sensor decreases. Not only does this model show us this visually, but it also gives us an equation to predict the time it would take a particle to reach the sensor given a temperature. We know that this equation fits our data and model due to the r squared value. The R squared value is a sort of measurement of the accuracy of our equation on a scale from 0-1. The closer the r squared is to one, the better our equation. | I now know through the simulation that the particles move faster and bump around more often the higher the temperature. The more they bump around, the faster particles travel and take up more space. |

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## Share and Cite

**MDPI and ACS Style**

Rosenberg, J.M.; Lawson, M.A.
An Investigation of Students’ Use of a Computational Science Simulation in an Online High School Physics Class. *Educ. Sci.* **2019**, *9*, 49.
https://doi.org/10.3390/educsci9010049

**AMA Style**

Rosenberg JM, Lawson MA.
An Investigation of Students’ Use of a Computational Science Simulation in an Online High School Physics Class. *Education Sciences*. 2019; 9(1):49.
https://doi.org/10.3390/educsci9010049

**Chicago/Turabian Style**

Rosenberg, Joshua M., and Michael A. Lawson.
2019. "An Investigation of Students’ Use of a Computational Science Simulation in an Online High School Physics Class" *Education Sciences* 9, no. 1: 49.
https://doi.org/10.3390/educsci9010049