Heart, Hustle, and the 5K Journey

Learning Goals & Products

Learning Goals

Students will be able to analyze biometric data from cardiovascular exercise to explain how heart rate, blood pressure, oxygen saturation, and recovery time change during 5K training and what those changes reveal about homeostasis.

Students will be able to investigate how the heart, blood vessels, lungs, and muscles work together during aerobic and anaerobic activity to construct evidence-based explanations of cardiovascular and muscular function.

Students will be able to collect and interpret evidence from sheep heart dissection, microscopy, and readings to connect cell structure, mitosis, meiosis, and inherited conditions such as familial hypercholesterolemia to cardiovascular health.

Students will be able to create and interpret scatter plots and tables from training data to identify patterns, relationships, and anomalies in exercise variables and support claims with mathematical evidence.

Students will be able to evaluate running culture and the Americans with Disabilities Act to explain how access, identity, and inclusion shape participation in fitness spaces and community 5K events.

Students will be able to design and refine evidence-based training plans and data-collection methods for a 5K investigation that account for reliability, fairness, and individual variation.

Products

individual

5K Cardiovascular Investigation Notebook and Training Journal

Each student maintains a notebook documenting questions, hypotheses, training logs, biometric data, graphs, source notes, reflections, and personal analysis across the project. The journal is also a core product, capturing the student’s 5K journey, running culture research, ADA/access analysis, and revisions over time.

team

Community 5K Event and Data Presentation

Teams synthesize their evidence into a public 5K event and a community-facing presentation or exhibit with data visualizations, methodological rationale, limitations, and recommendations for inclusive participation and cardiovascular health. The product should connect biometric findings, muscle and cardiovascular science, genetics, and reflections on access in running culture.

Rubric

Competency Progression Rubric Competency-first rubric

Category	Learning Goal	Stage 1	Stage 2	Stage 3	Stage 4
Deeper Learning Competencies	Collaboration	I can help my group assign roles and share materials fairly during our 5K training tasks, while listening to others’ ideas and using a simple group plan to complete steps on time.	I can work with my group to co-design and adjust our investigation (e.g., biometric station questions, data-collection routines, and training-log templates), building consensus and using feedback to resolve disagreements respectfully.	I can collaborate independently by leading portions of the project (such as coordinating data checks, explaining aerobic vs anaerobic patterns, or preparing parts of our exhibition), integrating different viewpoints to strengthen our evidence-based claims and recommendations.	I can strengthen collaboration across the whole team and community audience by negotiating shared decisions, supporting peers who need access or adaptation, and refining our solution and communication so the final exhibit clearly reflects our collective reasoning and goals.
Deeper Learning Competencies	Critical Thinking & Problem Solving	I can identify and describe relationships in my 5K biometric data (like heart rate, oxygen saturation, blood pressure, and recovery time) and explain whether they appear to change more during aerobic vs anaerobic intervals using basic evidence from my tables or plots.	I can construct a claim about how energy use and homeostasis shift during aerobic and anaerobic conditions, using my data patterns (graphs/scatter plots) as evidence and comparing trends across training sessions or stations to justify my reasoning.	I can revise my explanation by testing alternative interpretations of my data (for example, why recovery time changes or why individual responses differ), and I can connect evidence to scientific concepts such as cycling of matter/flow of energy under different conditions.	I can generate and refine a solution-oriented explanation or recommendation that uses my evidence and a model/representation (data reasoning with graphs and/or a simple mechanism) to support how training choices and access changes could reduce cardiovascular risk and broaden inclusive participation.
Deeper Learning Competencies	Effective Communication	I can clearly explain key ideas from my data (heart rate, blood pressure, oxygen saturation, and recovery time) using science vocabulary and a labeled table or graph, and I can describe how aerobic and anaerobic effort may differ I can present my claims in a short, understandable statement for a classmate or audience.	I can use my biometric evidence to support a more complete explanation by connecting patterns in my scatter plots and training log to homeostasis during exercise and recovery I can revise my wording and include specific references to what surprised me in the data, so my audience can follow my reasoning.	I can construct and revise an evidence-based explanation that links structure and function (heart/lungs/blood vessels/muscles) to aerobic vs anaerobic energy use and flow of matter/energy ideas from our investigations I can communicate my claims with accurate visuals (graphs with axes/trends) and cite what in my dataset supports each part of the explanation after feedback.	I can communicate a sophisticated, audience-ready narrative that synthesizes biometric data, training over time, and genetics/health context (e.g., inherited risk) into a coherent argument about cardiovascular health I can adapt my explanation for diverse audiences at the 5K exhibition (including access/ADA considerations), anticipate questions, and refine my message to strengthen clarity and accuracy.
Deeper Learning Competencies	Content Expertise	I can collect and organize my 5K biometric data (heart rate, blood pressure, oxygen saturation, and recovery time) into tables and graphs, and I can use the results to make a basic claim about how my body responds during aerobic vs anaerobic intervals.	I can explain how the cardiovascular and muscular systems work together to maintain homeostasis during exercise by using evidence from my data and appropriate science vocabulary, and I can revise my explanation when I get feedback.	I can construct and refine an evidence-based explanation of aerobic and anaerobic energy use and how matter/energy flow relates to exercise conditions, using my graphs/scatter plots plus at least one additional biology connection (e.g., cellular respiration structure-function, inherited heart conditions, or genetics).	I can design, evaluate, and refine a science-based proposal to reduce cardiovascular disease risk and improve access/inclusion in running, using my data, pattern evidence, and a model or simulation-informed argument, and I can justify how my proposal would work and how I know.
Deeper Learning Competencies	Self Directed Learning	I can use teacher and peer feedback to revise one part of my cardiovascular explanations (for example, a paragraph or claim) by correcting inaccuracies and adding a specific reference to my biometric data from the stations.	I can independently monitor my progress by setting short learning goals, revising my training-log patterns and energy-use explanation (aerobic vs anaerobic) using evidence from my tables/graphs, and clearly explaining what I changed and why.	I can refine my model-based and data-based explanations by independently testing and revising claims about homeostasis and energy flow during exercise, using scatter plots and comparisons over time, and I can justify revisions with multiple data points and feedback.	I can take ownership of my learning by independently identifying the most credible evidence across my portfolio (data, graphs, and case study connections like inherited risk), proposing refinements to my explanations/solutions, and systematically reflecting on how my understanding and training choices evolved with clear next steps for improvement.
Deeper Learning Competencies	Academic Mindset	I can set personal learning goals for my 5K biometric and humanities work (e.g., what data patterns I want to notice and what questions I want to answer) and use a checklist to plan my next steps and keep track of my progress.	I can use feedback from peers/teachers and my own reflections in my journal or vlog to monitor my understanding of homeostasis and aerobic vs anaerobic responses, and I can revise my training explanations and data displays when I notice gaps or errors.	I can independently refine my questions and evidence-based explanations by comparing my biometric data across training sessions (including scatter plots/tables) and connecting what I find to structure-function ideas and energy use during exercise.	I can sustain a growth-focused learning cycle by proactively seeking additional resources, testing how well my claims hold up against new evidence (including genetics/risk and access/ADA considerations), and then revising my portfolio for clarity, accuracy, and community usefulness during the public 5K exhibition.