| Lab Name |
Relationship of the Carbon Dioxide Absorption to Plant Growth
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| Subject Area |
Mathematics, and Science
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| Grade |
6 - 11
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| Topic |
- Statistics
- Graphs
- Correlation
- Number System
- Geometry
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| Experiment Title |
Carbon Dioxide Absorption and Plant Growth.
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| Hardware |
- COSMOS Toolkit: Computer Node
- COSMOS Toolkit: Mobile Node
- COSMOS Toolkit: IoT Nodes with sensors (i.e., temperature, humidity, polluting dust, luminocity, CO2)
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| Software |
- COSMOS Toolkit: Framework
- Chronograf
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| Number of Sessions to teach the topic |
- Few sessions to introduce the experiment
- 2 weeks gathering the data
- 1 - 2 sessions organizing, analyzing, interpreting and discussion of the data
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| Educational standards to be addressed |
- 6.EE.9
Use variables to represent two quantities in a real-world problem that change in relationship to one another; write an equation to express one quantity, thought of as the dependent variable, in terms of the other quantity, thought of as the independent variable.
Analyze the relationship between the dependent and independent variables using graphs and tables, and relate these to the equation. For example, in a problem involving motion at constant speed, list and graph ordered pairs of distances and times, and write the equation d = 65t to represent the relationship between distance and time.
- 7.RP.2
Recognize and represent proportional relationships between quantities:
- Decide whether two quantities are in a proportional relationship, e.g., by testing for equivalent ratios in a table or graphing on a coordinate plane and observing whether the graph is a straight line through the origin.
- Identify the constant of proportionality (unit rate) in tables, graphs, equations, diagrams, and verbal descriptions of proportional relationships.
- Represent proportional relationships by equations. For example, if the total cost is proportional to the number n of items purchased at a constant price p, the relationship between the total cost and the number of items can be expressed as t = pn.
- 7.EE.3
Solve multi-step real-life and mathematical problems posed with positive and negative rational numbers in any form (whole numbers, fractions, and decimals), using tools strategically.
Apply properties of operations to calculate with numbers in any form; convert between forms as appropriate; and assess the reasonableness of answers using mental computation and estimation strategies
- 8.EE.5
Graph proportional relationships, interpreting the unit rate as the slope of the graph. Compare two different proportional relationships represented in different ways. For example, compare a distance-time graph to a distance-time equation to determine which of two moving objects has greater speed.
Solve real-world and mathematical problems involving area, surface area, and volume.
- 6.G.1
Find the area of right triangles, other triangles, special quadrilaterals, and polygons by composing into rectangles or decomposing into triangles and other shapes; apply these.
- 7.G.6
Solve real-world and mathematical problems involving area, volume and surface area of two- and three-dimensional objects composed of triangles, quadrilaterals, polygons, cubes, and right Prisms techniques in the context of solving real-world.
Summarize and describe distributions.
- 6.SP.4
Display numerical data in plots on a number line, including dot plots, histograms, and box plots.
- 6.SP.5
Summarize numerical data sets in relation to their context, such as by:
- Reporting the number of observations.
- Describing the nature of the attribute under investigation, including how it was measured and its units of measurement.
- Giving quantitative measures of center (median and/or mean) and variability (interquartile range and/or mean absolute deviation), as well as describing any overall pattern and any striking deviations from the overall pattern with reference to the context in which the data were gathered.
- Relating the choice of measures of center and variability to the shape of the data distribution and the context in which the data were gathered.
Use random sampling to draw inferences about a population.
- 7.SP.1
Understand that statistics can be used to gain information about a population by examining a sample of the population; generalizations about a population from a sample are valid only if the sample is representative of that population. Understand that random sampling tends to produce representative samples and support valid inferences
- 7.SP.2
Use data from a random sample to draw inferences about a population with an unknown characteristic of interest. Generate multiple samples (or simulated samples) of the same size to gauge the variation in estimates or predictions. For example, estimate the mean word length in a book by randomly sampling words from the book; predict the winner of a school election based on randomly sampled survey data. Gauge how far off the estimate or prediction might be. Draw informal comparative inferences about two populations.
- 7.SP.3
Informally assess the degree of visual overlap of two numerical data distributions with similar variabilities, measuring the difference between the centers by expressing it as a multiple of a measure of variability. For example, the mean height of players on the basketball team is 10 cm greater than the mean height of players on the soccer team, about twice the variability (mean absolute deviation) on either team; on a dot plot, the separation between the two distributions of heights is noticeable.
- 7.SP.4
Use measures of center and measures of variability for numerical data from random samples to draw informal comparative inferences about two populations. For example, decide whether the words in a chapter of a seventh-grade science book are generally longer than the words in a chapter of a fourth-grade science book.
Summarize, represent, and interpret data on two categorical and quantitative variables.
- S-ID.5
Summarize categorical data for two categories in two-way frequency tables. Interpret relative frequencies in the context of the data (including joint, marginal, and conditional relative frequencies). Recognize possible associations and trends in the data.
- S-ID.6
Represent data on two quantitative variables on a scatter plot, and describe how the variables are related:
- Fit a function to the data; use functions fitted to data to solve problems in the context of the data. Use given functions or choose a function suggested by the context. Emphasize linear, quadratic, and exponential models.
- Informally assess the fit of a function by plotting and analyzing residuals.
- Fit a linear function for a scatter plot that suggests a linear association.
Interpret linear models.
- S-ID.7
Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data.
- S-ID.8
Compute (using technology) and interpret the correlation coefficient of a linear fit.
- S-ID.9
Distinguish between correlation and causation.
Apply geometric concepts in modeling situations.
- G-MG.1
Use geometric shapes, their measures, and their properties to describe objects (e.g., modeling a tree trunk or a human torso as a cylinder).
- G-MG.2
Apply concepts of density based on area and volume in modeling situations (e.g., persons per square mile, BTUs per cubic foot).
- G-MG.3
Apply geometric methods to solve design problems (e.g., designing an object or structure to satisfy physical constraints or minimize cost; working with typographic grid systems based on ratios).
Build a function that models a relationship between two quantities.
- F-BF.1 40
Write a function that describes a relationship between two quantities. Determine an explicit expression, a recursive process, or steps for calculation from a context.
- S-ID.6 62
Represent data on two quantitative variables on a scatter plot, and describe how the variables are related. Fit a function to the data; use functions fitted to data to solve problems in the context of the data. Use given functions or choose a function suggested by the context. Emphasize linear, quadratic, and exponential models.
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| COSMOS concepts to be used for the lab |
Using the Sensors and the COSMOS Toolkit framework, this experiment will allow correlating environmental measurements using IoT devices/nodes.
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| K12 Educational Goals (How the educational goals are achieved through teaching using the experiment, how the topic is connected to the COSMOS concepts used) |
In using this experiment, a lot of Math standards can be covered depending on the teachers focus. As part of real-world application, the data gathered could apply number system using rational and irrational numbers in gathering information from the Chronograf and the CSV (comma-separated values) files. Functions, Statistics and Geometry could be applied by looking at correlations of variables involved like length, height, weight of the plant to the wavelength of light and area of the leaves as students organize, analyze and present the data.
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| Short Description and Walk-through of the experiment |
- Experimental Group A
The set-up will include 4 - 5 plants that will be planted in separate pots. This group of plants will be exposed to the natural sunlight.
- Experimental Group B
The set-up will include 4-5 plants that will be planted in separate pots. This group of plants will not be exposed to the sunlight instead the source of light will be coming from the bulbs of colors green, blue, red, white.
- Constant Variables:
sunlight, amount of water, fertilizer, humidity, size of the pots.
- NOTE:
This experiment will be done in two weeks from start to finish. In Experiment B, the light or RGB sensor will be measuring the brightness of the light produced by the bulb that are emitted to the plants. Since Chlorophyll absorbs the light energy required to convert carbon dioxide and water into glucose. Leaves with more chlorophyll are better able to absorb the light required for photosynthesis. Students will find out the effects of these various lights to plant growth and will compare the results in Experiment A. After two weeks of gathering data, the students with their notes and graphic organizers will discuss and present their findings and interpretations of the data.
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| Testbed mapping of the experiment |
The experiment can be extended by executing it into a testbed (COSMOS / ORBIT / Witest) through the following steps:
We set up both experimental groups and instead of the Toolkit and the sensors, we will use the sensors that are attached to the testbed nodes
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