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Deeper Learning Competencies
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Critical Thinking & Problem Solving
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- I can use evidence from my team’s pandemic simulation (e.g., symptom reports, case maps, and basic graphs) to describe patterns and propose a first set of mitigation steps that match the clues we observed.
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- I can apply basic statistical and probability ideas (e.g., comparing distributions and making claims from graphs) to justify which transmission/differential-diagnosis options are more likely, and I can explain how our proposed mitigation aligns with the strongest evidence.
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- I can evaluate competing claims by identifying cause-and-effect relationships and constraints (environment/behavior/system tradeoffs), then use a computer simulation or spread model to test how changes to assumptions affect outcomes and refine my protocol and recommendations.
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- I can independently develop, critique, and improve a solution to the complex outbreak challenge by specifying qualitative and quantitative criteria, running simulations to compare alternatives, and defending my decisions with clear reasoning about probability, evidence quality, and system interactions.
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Deeper Learning Competencies
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Effective Communication
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- I can share my team’s epidemiology ideas clearly in a 1-page or poster format by using basic scientific terms and labeling (e.g., virus features, spread evidence, and mitigation steps).
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- I can explain how my group’s claims are supported by data from our simulation or charts by pointing to specific numbers/graphs and describing cause-and-effect relationships in the outbreak scenario.
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- I can communicate our differential diagnosis protocol, patient treatment plan, and prevention/mitigation strategy by connecting viral anatomy/replication, strain differences, and vaccine action to our modeling results using clear, organized reasoning.
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- I can present and defend our recommendations to public health audiences by answering questions with evidence-based explanations, comparing alternative interpretations/tradeoffs, and revising my message to improve accuracy and real-world feasibility.
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Deeper Learning Competencies
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Collaboration
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- I can contribute to my team’s work by completing my assigned parts of the epidemiology portfolio (e.g., parts of the differential diagnosis protocol, treatment plan, or prevention/mitigation sections) and sharing my evidence and reasoning with the group.
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- I can coordinate with my team by explaining how my parts connect to our shared goals, listening to teammates’ ideas, and revising my work based on peer feedback to improve scientific accuracy and feasibility for our public health briefing.
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- I can take shared leadership by helping our team plan, divide tasks strategically, and facilitate decisions (including tradeoffs) using evidence from our models, visuals, and simulations; I can also resolve misunderstandings by clarifying roles and expectations.
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- I can independently strengthen collaboration by consistently driving productive group processes—seeking diverse perspectives, integrating feedback to refine models and recommendations, and ensuring our final protocol and presentation are coherent, defensible, and ready to answer questions from public health professionals.
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Deeper Learning Competencies
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Content Expertise
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- I can use basic evidence (graphs, simple counts, or provided data visualizations) to describe patterns in viral spread (e.g., who increases in proportion over time) and connect those patterns to a simple claim about traits and transmission
- I can correctly label key virus model parts and explain, in my own words, how viral structure and genetic information relate to protein functions and infection outcomes.
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- I can analyze basic statistical/graphical evidence to explain how changes in transmission conditions (like contact rates, interventions, or environment-like factors in the simulation) lead to measurable shifts in cases or traits in a population model
- I can build and justify models of viral anatomy/replication and use them to support a claim about why different strains vary in behavior and how the immune response (and vaccination) changes infection probability.
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- I can evaluate multiple lines of evidence from our simulation, models, and outbreak materials to make cause-and-effect explanations for how changing environmental and system conditions affect emergence, increases/decreases, and predicted outcomes during an outbreak
- I can use computer/simulation outputs to support and critique our differential diagnosis and treatment plan by linking model assumptions to biological explanations (structure → function, replication → spread, strain differences → risk).
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- I can synthesize evidence across viral biology and spread modeling to construct a scientifically accurate, testable explanation that accounts for constraints and tradeoffs in our epidemiology protocol, mitigation, and prevention recommendations
- I can compare alternative claims using graphical/statistical reasoning, clearly identify what evidence is strong vs
- weak (within the project’s boundaries), and revise my models to improve how well they predict realistic outbreak dynamics and vaccine-driven immune responses.
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Deeper Learning Competencies
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Self Directed Learning
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- I can use teacher-provided checklists and feedback to complete my parts of the 1-pager and team products by revising only the specific items I was asked to improve (e.g., labeling, missing steps, or minor edits).
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- I can track my learning during the project by setting short goals, using peer/partner and expert feedback to make targeted revisions, and explaining what I changed and why in my team notes.
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- I can independently identify gaps in my own understanding (like evidence quality, model assumptions, or how data supports a claim) and revise my virus/anatomy or spread model accordingly, using basic graphs/statistics from our activities when appropriate.
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- I can monitor and direct my learning across multiple project components by proposing and validating improvements to our differential diagnosis protocol, treatment plan, and mitigation/prevention strategies, justifying changes with evidence and reflecting on tradeoffs and constraints.
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