Modeling Multivariable Systems: Design a Moisture-Sensitive Plant Watering System
Activity: Design an automatic watering system for a plant. The water must be triggered by a soil moisture sensor. Keep in mind that different plants will need different amounts of water to survive. Choose a plant to monitor and research how much water will optimize growth.
Inspiration: The ITP blog on Arduino and analog sensors.
Explanation construction: Identify how the structure of the root system and the shoot system of your plant contributes to the amount of water needed. (e.g., surface area to volume ratio; length of roots). Identify how the soil contributes to the amount of water needed to optimize growth.
Identify variables: What variables affect plant growth? How will you control these variables while testing the effects of water.
Observe changes in the plant each day by measuring growth of height or width, counting number of leaves, observing coloration, etc.
Predict which biome this plant would thrive in. Provide evidence for your argument citing types of soil, temperature, and rainfall that are optimal for your plant. Identify variables in your design that could be changed to improve plant growth.
Next Steps: Design experiments to test for variation in other variables.
Data Visualization Extension: Graph and store live data from multiple sources using processing. Identify relationships between temperature, water, and soil nutrients.
Applied NGSS Learning Objectives and Outcomes
MS-ETS1-1: Define the criteria and constraints of a design problem with sufficient precision to ensure a successful solution, taking into account relevant scientific principles and potential impacts on people and the natural environment that may limit possible solutions.
Asking Questions and Defining Problems: Specifying relationships between variables and clarifying arguments and models.
MS-ETS1-2: Evaluate competing design solutions using a systematic process to determine how well they meet the criteria and constraints of the problem.
Engaging in argument from evidence: Constructing a convincing argument that supports or refutes claims for either explanations or solutions about the natural and designed world.
MS-ETSI-3: Analyze data from tests to determine similarities and differences among several design solutions to identify the best characteristic of each that can be combined into a new solution to better meet the criteria for success.
Analyzing and interpreting data: Extending quantitative analysis to investigations, distinguishing between correlation and causation, and basic statistical techniques of data and error analysis.
Optimizing the design solution: identify characteristics of design that performed best in each test to inform redesign.
MS-ETS1-4 Develop a model to generate data for iterative testing and modification of a proposed object, tool, or process such that an optimal design can be achieved.
Developing and using models: Develop a model to generate data to test ideas about designed systems, including those representing inputs and outputs.