MS-ETS1-4
The standard
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.
Next Generation Science Standards
What this standard means
Students need to build or use a model that lets them test a design, collect data, and make changes based on results. The model can be physical, digital, a drawing with measurements, or a small-scale version. The point is not one perfect build. It is using evidence to improve a solution over several rounds.
Mastery looks like clear test criteria, fair tests, recorded data, and specific design changes tied to that data. Students often get stuck by changing too many variables at once, choosing changes by opinion, or stopping after the first version works a little.
Ways to teach it
- Have teams build paper bridges, test load with pennies, record failure points, then revise one feature and retest.
- Ask students to write: What data convinced your team to change the design, and what did you keep the same?
- Give an exit ticket with a test table and ask students to identify the best next design change.
- Show bike helmet testing videos, then have students name the model, the data collected, and the design change it could support.
Plan a lesson for MS-ETS1-4
Generate a complete lesson plan aligned to this standard, with objectives, activities, and materials. Free, no account needed.
Related standards
- 3-5-ETS1-3
Plan and carry out fair tests in which variables are controlled and failure points are considered to identify aspects of a model or prototype that can be improv...
- MS-ETS1-3
Analyze data from tests to determine similarities and differences among several design solutions to identify the best characteristics of each that can be combin...
- K-2-ETS1-1
Ask questions, make observations, and gather information about a situation people want to change to define a simple problem that can be solved through the devel...
- 3-5-ETS1-2
Generate and compare multiple possible solutions to a problem based on how well each is likely to meet the criteria and constraints of the problem.