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Skill
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Students will be able to restate the New Orleans transit access problem by identifying affected riders, constraints, and needed data. - restate
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- I can restate the New Orleans transit access problem by naming key groups of riders affected (for example, students, workers, seniors) and describing how access changes based on where people live
- I can identify at least one major constraint (like cost or travel time) and list one type of needed data (like ridership or location).
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- I can restate the transit access problem with clear, specific details by describing which riders are affected and where gaps in access appear across neighborhoods
- I can identify multiple constraints that matter for planning (such as budget limits and route coverage) and match each constraint to the data needed to evaluate it (such as travel distance, service routes, and demand).
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- I can restate the transit access problem using a structured, evidence-based statement that includes affected riders, the conditions in New Orleans neighborhoods, and the planning constraints that drive decisions
- I can justify what data is needed and why (for example, explaining how ridership patterns and neighborhood density relate to travel access) using information from the simulation/map or provided sources.
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- I can restate the transit access problem as a precise planning question with clearly defined affected riders, geographic scope, constraints, and data requirements
- I can synthesize patterns from data sources (such as the simulation/map, neighborhood locations, and budget or distance measures) to explain how these factors interact and to refine what data would best support a cost-effective, equitable route-model approach.
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Content knowledge
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Students will be able to analyze neighborhood and ridership patterns in New Orleans to identify underserved transit areas. - analyze
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- I can describe neighborhood transit access patterns in New Orleans by identifying areas with lower ridership and weaker service coverage on a provided map or dataset
- I can point to specific locations and explain what makes them underserved using basic neighborhood and ridership information.
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- I can analyze ridership and neighborhood location data to identify trends in underserved transit areas, such as where coverage gaps cluster or where ridership is consistently low
- I can use simple visual evidence (charts or annotated maps) to connect patterns to possible causes in the transit system.
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- I can investigate multiple pattern signals (ridership levels, distance/travel time proxies, and neighborhood density) to determine which underserved areas are most impacted
- I can justify my conclusions with data comparisons and clear reasoning about how the patterns relate to transit access.
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- I can synthesize neighborhood and ridership patterns from the full dataset to produce a prioritized, data-supported map of underserved transit areas
- I can evaluate how different pattern features interact (e.g., density vs
- coverage) and explain my reasoning in a way that informs realistic service-change decisions.
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Content knowledge
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Students will be able to model transit route coverage with polynomial functions using New Orleans travel and budget data. - model
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- I can use provided New Orleans neighborhood and transit/budget data to identify a measurable coverage goal (such as service radius or route reach) and represent it with a simple polynomial relationship (e.g., a linear or quadratic equation)
- I can substitute data values to generate basic route coverage outputs for a few locations.
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- I can build a polynomial route model that matches travel and/or cost patterns by selecting polynomial type and fitting coefficients to multiple neighborhood data points
- I can use digital tools to graph the polynomial route coverage and compare model outputs to the given data with at least one clear metric (such as error between predicted and observed coverage).
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- I can refine my polynomial model by testing how different polynomial choices and parameters affect coverage accuracy across neighborhoods while meeting budget constraints
- I can analyze patterns in where the model performs well or poorly (coverage gaps) and justify my refinements using evidence from the data and graphs.
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- I can develop and validate a strong polynomial route coverage model that supports cost-effective service decisions across the full set of neighborhoods
- I can independently run multiple scenario comparisons, report results with supporting calculations and visualizations, and explain how the model’s structure captures realistic travel/coverage trade-offs for equitable planning.
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Skill
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Students will be able to use digital graphing and mapping tools to compare multiple polynomial route options. - use
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- I can use digital graphing and mapping tools to plot one polynomial route option on a New Orleans neighborhood map and label the route clearly
- I can adjust basic polynomial settings (such as coefficients or degree) and update the display to see how the curve changes.
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- I can compare two or more polynomial route options by graphing each on the same digital map and using consistent scales and labels
- I can identify which option covers more of a target area (such as underserved neighborhoods) by visually checking alignment with mapped features and simple coverage measures.
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- I can compare multiple polynomial route options using a digital mapping/graphing workflow that includes criteria (e.g., coverage range, distance along the route, and cost-related constraints)
- I can generate and interpret side-by-side visuals (overlays, labeled curves, or dashboard-style comparisons) to explain where each model performs well and where it underperforms in different neighborhoods.
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- I can independently design a clear comparison using digital graphing and mapping tools that evaluates several polynomial route options against meaningful, user-centered planning criteria
- I can produce a polished set of visuals that supports a decision by showing the trade-offs between coverage quality and constraint limits across neighborhoods.
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Skill
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Students will be able to check computational outputs and calculations against New Orleans transit benchmarks to evaluate model accuracy. - check
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- I can compare my model’s basic computed values (like predicted route coverage, distance, or costs) to the provided New Orleans benchmark numbers and describe whether my results are close or far
- I can identify at least one specific output (table, graph point, or map value) that matches or conflicts with the benchmark.
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- I can check my computational outputs by recomputing key steps (such as polynomial evaluations and coverage calculations) and verifying they produce the same results as my program or spreadsheet
- I can measure differences from the New Orleans benchmarks (for example, within an acceptable tolerance) and explain what those differences suggest about model accuracy.
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- I can validate my model accuracy using multiple benchmark checks (such as trends across neighborhoods, average performance by area, and error on several route segments)
- I can revise my computations or assumptions when mismatches appear, and I can document how the corrected outputs improve agreement with the benchmarks.
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- I can thoroughly evaluate model accuracy against New Orleans benchmarks by calculating error metrics, running consistency checks across scenarios, and using results to justify whether the model is reliable for transit-planning decisions
- I can present a clear evidence-based conclusion that links specific output checks to equity and service usefulness, and I can defend my accuracy reasoning with benchmark comparisons.
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Disposition
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Students will be able to explain how polynomial model assumptions affect fairness, accuracy, and usefulness for different New Orleans neighborhoods. - explain
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- I can describe what a polynomial route model assumes (such as a smooth curved pattern of coverage) and explain how those assumptions might change how well the model represents travel for different neighborhoods
- I can use one or two examples from my data or map to point to where the model seems to fit better or worse for particular areas.
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- I can explain how specific polynomial model assumptions (like the degree of the polynomial or how I treat distance/coverage) affect accuracy and usefulness for different New Orleans neighborhoods
- I can support my explanation with evidence from model outputs (graphs, mapped coverage gaps, or comparisons) and describe how those differences could affect fairness in who benefits from the proposed service changes.
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- I can analyze how model assumptions influence both accuracy and equity by comparing where the polynomial model matches real patterns and where it breaks down across neighborhoods
- I can justify which assumption(s) drive the differences and explain how changing the model choice would likely lead to more reliable or fairer route planning for underserved communities.
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- I can critically evaluate how polynomial model assumptions shape fairness, accuracy, and usefulness across neighborhoods by explaining the full chain from assumption → mathematical behavior → predicted coverage → community impact
- I can use multiple pieces of evidence (data trends, mapping/graph comparisons, and partner-sourced context) to propose specific, defensible adjustments to improve fairness and reliability of the model for decision-making.
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Skill
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Students will be able to propose improvements to transit route recommendations using evidence from user feedback and model testing. - propose
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- I can propose a simple transit route improvement by using one piece of evidence from user feedback or model testing to explain a change to coverage, stops, or travel time
- I can state the reason for my suggestion using the specific result I observed.
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- I can propose a transit route improvement using multiple evidence points from user feedback and model testing (such as where coverage gaps appear and who benefits)
- I can justify how my proposed change addresses cost limits and improves access for specific neighborhoods.
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- I can propose a targeted transit route improvement by comparing at least two route-model scenarios and using user feedback to select the option that performs best for equity and efficiency
- I can describe measurable results (e.g., improved coverage or reduced travel barriers) and explain why my recommendation is more effective than the original.
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- I can propose a well-supported set of transit route improvements using evidence from iterative model testing and synthesized user feedback
- I can refine my recommendations based on new findings, clearly linking each change to improved outcomes (coverage, reliability, and cost) across neighborhoods and explaining the trade-offs I considered.
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