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Deeper Learning Competencies
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Critical Thinking & Problem Solving
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- I can interpret the “client need” prompt and turn it into a clear, step-by-step plan for one city-design task (like a placement or measurement change) using area, perimeter, scale, or coordinates
- I can follow math steps to check that my design choice matches the given constraints, and I can explain what I did using basic math language.
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- I can model a real-city design problem by choosing an appropriate strategy (area/perimeter, scale drawing, or geometric structure) and applying it to revise my city plan in a way that improves accessibility, safety, or navigation
- I can justify my choices by connecting calculations to the client need and point out one assumption or reason my solution fits (or may not fit) the constraints.
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- I can solve multi-step city-design problems by combining tools and representations (scale drawings, structured reasoning about shapes, and mathematical models) to meet several constraints across different quadrants
- I can evaluate multiple possible design options, critique peer reasoning, and revise my work using precise measurements and feedback, showing why the new solution is more effective.
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- I can independently and strategically develop, test, and improve a complete, mathematically supported city design that addresses complex client goals with consistent reasoning across 2D and 3D representations
- I can construct viable arguments using accurate calculations (including scale and geometric relationships), critique and refine others’ approaches, and clearly explain the structure behind my model to defend decisions with mathematical evidence.
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Deeper Learning Competencies
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Collaboration
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- I can participate in team decisions by sharing my ideas about where buildings/landmarks should go on the quadrant grid and listening to my teammates’ suggestions during the city planning process
- I can follow agreed-upon roles and help with tasks (e.g., measuring, labeling ordered pairs, or placing cards) using tools like rulers or graph paper.
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- I can collaborate to solve design problems by proposing and testing at least one math-based option (such as adjusting a placement for area/perimeter/scale) and explaining how it affects the city map
- I can use teammate feedback to revise our plan and maintain clear, accurate work (labeled ordered pairs, measurements, and precise markings).
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- I can lead collaborative planning by coordinating teammates’ contributions and using structure (patterns in quadrant placement and scale/measurement relationships) to improve our design efficiently
- I can present my reasoning with evidence, critique others’ ideas respectfully, and help the team reach consensus before finalizing placements and measurements.
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- I can independently and collaboratively refine our city design by integrating multiple perspectives, then strategically modeling solutions and revising with precision using peer and community partner feedback
- I can organize teamwork (roles, timelines, and conflict resolution), defend design choices with clear math evidence, and ensure our final map/3D model clearly matches the team’s coordinated decisions.
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Deeper Learning Competencies
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Effective Communication
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- I can clearly communicate my city-design ideas by labeling my quadrant locations with ordered pairs and using basic measurement words (e.g., scale, length, area) on my map or 1-pager so others can follow my plan.
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- I can explain the key math behind at least two design choices (coordinates, scale drawings, area/perimeter, or proportional street layouts) using labeled diagrams and correct vocabulary, and I can answer questions by linking my explanation to what is shown on my work.
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- I can present a well-organized design board and guide others through my reasoning, including how my placements and measurements solve the community problem; I can use precise details (units, calculations, and accurate labels) and critique feedback to improve my model.
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- I can independently communicate and defend my city design with strong mathematical evidence, connecting coordinate placement, scale, and area/perimeter/proportional reasoning to real goals like safety, accessibility, and navigation, and I can refine my explanations after peer and community partner critique.
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Deeper Learning Competencies
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Content Expertise
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- I can plot and label ordered pairs in all four quadrants to place city features (buildings/landmarks) accurately on my coordinate grid
- I can use a ruler/measuring tools to create a basic scaled map and show my measurements and area or perimeter calculations with correct units.
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- I can use proportional reasoning, scale drawings, and coordinate structure to design layouts that make sense (e.g., streets, zones, and access routes) across all four quadrants
- I can calculate area and perimeter for common polygon “lots,” explain how my measurements connect to the scale, and correct errors when feedback shows placements are off.
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- I can model real design needs mathematically by revising my city plan using area, perimeter, and proportional reasoning to improve navigation and space use
- I can construct or refine geometric shapes (with ruler/tech) that meet given conditions, justify whether choices are consistent with constraints, and show a clear chain of reasoning in my calculations and labels.
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- I can independently create and defend a mathematically precise city design by using structure in coordinates and geometric properties to optimize space and accessibility
- I can accurately compute from and reproduce scale drawings at a new scale, solve complex area/perimeter problems for composite shapes, and critique alternative placements using precise evidence before finalizing my map, 3D model, and before/after revisions.
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Deeper Learning Competencies
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Academic Mindset
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- I can describe how my city design choices help a neighborhood feel more welcoming and navigable (e.g., easier access, safer routes) and I can explain my thinking using basic math terms from the project.
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- I can use feedback from peers or community partners to make focused improvements to my scaled city map and placements, and I can justify how my changes better meet community needs using area, perimeter, and coordinate reasoning.
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- I can independently evaluate whether my design is accurate and functional (including structure, precision, and scale) and I can revise my plans by modeling real-world impacts, explaining tradeoffs with clear math evidence.
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- I can sustain a strong mindset of improvement by proactively seeking feedback, monitoring my progress against design criteria, and making sophisticated revisions to strengthen both mathematical accuracy (scale/measure/area/perimeter) and the welcoming, efficient experience of my city.
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Deeper Learning Competencies
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Self Directed Learning
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- I can use a checklist and examples to make a plan for my city-map and 3D model steps, choose appropriate tools (ruler, protractor, measuring materials, or technology), and complete required labeled coordinates, measurements, and units with support.
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- I can use peer and teacher feedback to revise my drafts by identifying one specific math issue (like area, perimeter, scale length, or proportional placement) and correcting it while keeping my work organized, precise, and properly labeled on my quadrant grid and scale drawings.
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- I can independently monitor my progress during the project by checking my own measurements and geometric placements against the design constraints, using structure (patterns on the coordinate plane and consistent scale) to catch errors, and revising multiple parts of my city with clear, mathematically supported explanations.
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- I can direct my own learning by testing alternative designs, critiquing my reasoning and the reasoning of others, and using community feedback to make informed, precision-focused revisions that improve my map/model, showing a before-and-after record of how my math evidence led to better city decisions.
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