Unit Plan 26 (Grade 7 Science): Engineering Genetic Solutions
Grade 7 unit evaluating trait-focused designs in agriculture and conservation using criteria, constraints, data, and models to recommend solutions.
Focus: Evaluate design solutions that change or manage traits in populations (e.g., crops, livestock, wildlife) using criteria, constraints, data, and models in agricultural and conservation contexts.
Grade Level: 7
Subject Area: Science (Life Science — Traits & Human Impact; Engineering Design)
Total Unit Duration: 5 sessions (one week), 50–60 minutes per session
I. Introduction
In this unit, students act as design reviewers for trait-focused solutions in agriculture and conservation. Building on their understanding of artificial selection and traits, they examine design problems where humans want certain traits (e.g., disease resistance, drought tolerance, stronger populations) and must choose among competing solutions. Students use criteria and constraints, analyze test data, and develop simple models (simulations) to generate data for improving designs. By the end, they produce a design review recommendation supported by evidence, aligned with MS-ETS1-2–4.
Essential Questions
- How can we clearly describe a trait-related design problem in agriculture or conservation using criteria and constraints?
- How do scientists and engineers evaluate competing design solutions using data and systematic processes instead of guesses?
- How can models and simulations help us generate data to improve trait-focused designs before using them in real ecosystems?
- Why is it important to think about tradeoffs, genetic diversity, and long-term impacts when choosing trait-focused solutions?
II. Objectives and Standards
Learning Objectives — Students will be able to:
- Describe a trait-focused design problem (e.g., improving crop resilience or supporting a threatened population) using clear criteria and constraints.
- Use a decision matrix or similar tool to evaluate and compare competing design solutions based on how well they meet criteria and respect constraints.
- Analyze data from tests or simulations (tables, graphs) to determine similarities and differences in how well designs perform.
- Develop and use a model or simulation (e.g., trait tokens, cards, beads, or simple digital/graph models) to generate data on how a design might affect traits and population outcomes over repeated trials.
- Construct a Design Review Recommendation that explains which solution they recommend and how they would improve it, using evidence from their evaluations, data analysis, and models (aligned with MS-ETS1-2–4).
Standards Alignment — 7th Grade (NGSS-based custom)
- MS-ETS1-2 — Evaluate competing design solutions using a systematic process to determine how well they meet the criteria and constraints of a problem.
- MS-ETS1-3 — Analyze data from tests to determine similarities and differences among several design solutions to identify which best meets the criteria.
- MS-ETS1-4 — Develop a model to generate data for iterative testing and modification of a proposed object, tool, or process so that an optimal design can be achieved.
Success Criteria — Student Language
- I can clearly describe a design problem involving traits and name its criteria and constraints.
- I can compare design solutions using a decision matrix or rating system instead of just picking my favorite.
- I can analyze test data (tables or graphs) to decide which design worked better and why.
- I can build and run a simple model or simulation to generate data on how a trait-focused design might affect a population or yield over time.
- I can write or present a design recommendation that uses data, criteria/constraints, and model results to justify my choice and suggest improvements.