Kinesiology data analysis
Do now
Analyze your motion data and write a CER about fatigue and range of motion.
- Hand in
- Labeled graph of force or angle versus trial number plus a CER claiming how fatigue affected range of motion, citing specific data values.
- Where
- Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not.
You get two school days for every day you were absent, so this deadline moves with you.
Your has 12 numbers in it. How does graphing them show you the exact moment fatigue changed your , and how steep that change was?
Analyze your motion data and write a CER about fatigue and .
- • You can graph and describe a fatigue trend.
- • You can write a CER supported by your own data.
- What can a line graph show you about your data that a column of numbers cannot?
- In a CER, what is the difference between 'evidence' and just stating your claim again?
- 1Graph your force or angle versus trial number.
- 2Describe the trend and identify where fatigue changed performance.
- 3Complete the PLTW online analysis questions on .
- 4Write a CER claiming how fatigue affected , using your data as evidence.
- 5Submit your labeled graph and data-based CER.
What did this day actually feel like?
Kinesiology data analysis
Graph of force or angle against trial number, then a CER claiming what the trend shows about fatigue.
My prediction from Tuesday was directionally right and wrong about the shape. The decline was not steady, it dropped fast and then leveled off. Being wrong in a specific way is much more useful than being vaguely right.
AT HOME, THE NIGHT BEFORE WED MAR 3 Submit motion-data evidence Raw table, labeled graph, fatigue CER, and a limitations line about the sensor placement problem.
Turned in: motion data packet → recorded in Class Records
Fiction. There is no such student. The lessons, labs and dates are the real planned course; the student, the classmates and the conversations are invented.
The same day, drawn.
Graph of force or angle against trial number, then a CER claiming what the trend shows about fatigue.
Fiction. There is no such student. The lessons, labs and dates are the real planned course; the student, the classmates and the conversations are invented.
🛠 Get unstuck · pick your level
🔑 Today's words · 5
Tap a word in the lesson for a plain meaning and one example. Recycled into next week's Do-Now.
Do the work · 80-minute blockfirst 5 min = hook▸
💡 Big idea: Graphing physiological data reveals a trend the raw table hides, so a CER that cites your specific trial values proves fatigue changed , while a general statement proves nothing.
- 0-8Intro: graphing conventions and CER structure review
- 8-20Build labeled graph from Wednesday
- 20-40PLTW online analysis questions on
- 40-55Identify trend and fatigue-onset from graph
- 55-75Write data-based CER with specific trial numbers as evidence
- 75-80Submit labeled graph and CER; preview Friday evidence packet
- • Yesterday you collected raw numbers. Today you turn them into a story.
- • First you graph. Force or angle on the Y-axis, trial number on the X-axis. A proper graph has a title, labeled axes with units, and a scale that uses the full space.
- • Then you write a CER. Your claim names what happened to . Your evidence cites specific trial numbers from your table. Your reasoning connects the data to the physiology of fatigue.
- • A vague CER gets no credit. Use your numbers.
- • A line graph of force or angle versus trial number should show a plateau or decline as fatigue sets in; the slope of that decline quantifies fatigue rate.
- • is the study of human movement; (ROM) is the extent of movement at a measured in degrees.
- • A CER using collected data must cite specific trial numbers or values as evidence, not general statements about what usually happens.
Unit 1.2 Motion Data: Muscle strength, fatigue, physiology sensors, range of motion, joint testing, kinesiology taping. · data analysis
Day 4 of this lesson. Open this exact section in myPLTW (find it in Clever, Microsoft sign-in), then do the work below.
Do this: Complete the data-analysis task in Lesson 1.2 Muscles and Motion on myPLTW; finish all analysis prompts before writing your CER.
Mark the data-analysis task complete after submitting your motion-data CER.
Lab task is done; today the analysis task should show complete and your CER should be submitted.
myPLTW completion status plus submitted CER.
The official PLTW activity stays inside myPLTW. If myPLTW will not open, use F1 and E1-E3 on this page to complete today's local evidence decision, then make up the official activity when access returns. Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not.
Check things off as you work, then submit. This tells Mr. Mendoza how you're doing so he can help the class. It does not replace turning in your producible through the submission route shown below.
Use the code Mr. Mendoza gave you, not your name. Saved on this device.
Unit 1.2 Motion Data: Muscle strength, fatigue, physiology sensors, range of motion, joint testing, kinesiology taping. · Kinesiology data analysis
Complete the data-analysis task in Lesson 1.2 Muscles and Motion on myPLTW; finish all analysis prompts before writing your CER.
Lab task is done; today the analysis task should show complete and your CER should be submitted.
This is how Mr. Mendoza sees the class keeping pace with PLTW. Be honest, it only helps if it is accurate.
🎯 Analyze your motion data and write a CER about fatigue and .
- Graph your force or angle versus trial number.
- Describe the trend and identify where fatigue changed performance.
- Complete the PLTW online analysis questions on .
- Write a CER claiming how fatigue affected , using your data as evidence.
- Submit your labeled graph and data-based CER.
CER: Labeled graph of force or angle versus trial number plus a CER claiming how fatigue affected , citing specific data values.
Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not. Use the checklist just below and upload by 11:29 PM for full credit. Absent with an excused absence? You get two school days for every day you were absent, so this deadline moves with you.
| Task | Who |
|---|---|
| Graph your force or angle versus trial number. | _______ |
| Describe the trend and identify where fatigue changed performance. | _______ |
| Complete the PLTW online analysis questions on . | _______ |
| Write a CER claiming how fatigue affected , using your data as evidence. | _______ |
| Submit your labeled graph and data-based CER. | _______ |
Working solo? Put your own name in "Who" for every row.
- You can graph and describe a fatigue trend.
- You can write a CER supported by your own data.
- 1Do thisAnalyze your motion data and write a CER about fatigue and range of motion.
- 2Use this resource
- 3Submit thisCER: Labeled graph of force or angle versus trial number plus a CER claiming how fatigue affected range of motion, citing specific data values.
- 4Submit it here
- 1Open the drop folder.
- 2Sign in with your district Microsoft account, not a personal one.
- 3Upload the file, named Lastname_Firstname__Assignment Title.
- 4Your own upload panel says Uploaded with a green check: that is your receipt.
Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not. Human Anatomy & Physiology (Human Body Systems) › Unit 1.2 Motion Data: Muscle strength, fatigue, physiology sensors, range of motion, joint testing, kinesiology taping. › CEROpen the drop folder
Learn it · deck, reading, and vocabulary▸
The deck carries the prior idea forward, lets you inspect an analogy, maps the rule to biology, and ends with the same evidence decision and exit ticket used on this page.
Generated from this lesson's canonical data with a red-team citation check.
A conclusion is only as good as the data under it, so you zero your sensor and run consistent trials in order to tell a real fatigue trend apart from a single noisy reading.
Graphing physiological data reveals a trend the raw table hides, so a CER that cites your specific trial values proves fatigue changed , while a general statement proves nothing.
A detective board holds observations, possible explanations, and one next question.
- Which notes are direct observations?
- Which notes are explanations?
- What new evidence would separate the explanations?
Keep observations separate from explanations, then collect the evidence that can distinguish the options.
Biomedical investigations use controlled procedures and validated measurements, not intuition alone.
- • Board notes map to E1-E3.
- • Possible explanations map to the decision options.
- • The next question maps to the evidence-based action.
Driving question: Your has 12 numbers in it. How does graphing them show you the exact moment fatigue changed your , and how steep that change was?
What you already know: A conclusion is only as good as the data under it, so you zero your sensor and run consistent trials in order to tell a real fatigue trend apart from a single noisy reading.
New idea: Graphing physiological data reveals a trend the raw table hides, so a CER that cites your specific trial values proves fatigue changed , while a general statement proves nothing.
Visual or model: F1. F1. A lesson illustration or teaching diagram for Kinesiology data analysis. Use it with E1-E3; it is a model or context image, not experimental or patient data. What to notice: Trace the labeled structure, movement, or system relationship that connects form to function.
- Observe or measure the relevant feature in data analysis.
- Organize the observation with a stable evidence ID.
- Apply this rule: Keep observations separate from explanations, then collect the evidence that can distinguish the options.
- Choose the option the evidence supports and state the limit of the conclusion.
Real biomedical example: Your has 12 numbers in it. How does graphing them show you the exact moment fatigue changed your , and how steep that change was?
What the evidence supports: E1-E3 and F1 support the daily take-home when the response meets the stated success criteria.
What it cannot prove: The package does not support claims beyond this lesson's or any real patient diagnosis.
- • fatigue: A state of physical or mental tiredness in which muscles or the body produce less force or focus and need rest to recover.
- • EMG: Electromyography, a test that records the electrical signals muscles produce when they contract, used to study muscle and nerve function.
- • : The full distance and direction a can move, measured in degrees from its fully bent to its fully straightened position.
- • : Bending a so the angle between two body parts decreases, such as curling the forearm toward the shoulder.
- • extension: A movement that increases the angle of a and straightens a body part, such as opening the arm to straighten the elbow.
- • biomechanics: The study of how forces, motion, and structure act on living bodies, applying physics to muscles, bones, and joints.
- • : The scientific study of human movement, including how muscles, bones, and the nervous system work together to produce motion.
Use it now: Choose one decision option. Cite E1 and E3, then explain how the rule connects the evidence to your choice.
Go further, optional: The source links below are optional enrichment. Every fact required for today's local evidence decision appears in this lesson package.
A line graph of force or angle versus trial number should show a plateau or decline as fatigue sets in; the slope of that decline quantifies fatigue rate.
Limit: E1 supplies context or an observation; it does not by itself establish the explanation.
Graphing physiological data reveals a trend the raw table hides, so a CER that cites your specific trial values proves fatigue changed , while a general statement proves nothing.
Limit: E2 is a teaching statement or comparison and must be checked against the task evidence.
You can graph and describe a fatigue trend.
Limit: E3 supports only the result or product criterion named here; it cannot justify a broader clinical or causal claim.
PLTW-HAP-2027-03-02 · Simulated classroom evidence scenario
Your role: anatomy and physiology consultant
Decision: Your team must decide what the evidence from data analysis supports before submitting the claim-evidence-reasoning response named on the lesson page.
- • Select the option best supported by E1-E3.
- • Select a reasonable alternative and name the evidence it would require.
- • Delay the claim because the evidence does not distinguish the options.
Response: State one choice, cite at least two evidence IDs, explain the rule that connects them, and add one limitation. Submit it as the claim-evidence-reasoning response.
Claim ceiling: The supplied lesson evidence can support an observation, pattern, classroom mechanism, or next-step decision about data analysis. It cannot by itself prove causation, establish a real clinical diagnosis, or justify action outside this classroom task.
Mean = sum of values / number of values. Median = middle ordered value. Range = maximum - minimum.
For 2, 4, 4, and 10: mean = 20 / 4 = 5, median = 4, and range = 10 - 2 = 8.
Mean, median, and range keep the measurement unit. Order the values before finding the median.
Calculate the requested summary for today's supplied values, then write what it reveals and what it hides.
Students often think Students write a CER that says fatigue 'usually lowers performance' and think that counts as evidence.. The trap: General statements about what normally happens are not evidence, because a data-based CER has to cite YOUR specific trial numbers or values, otherwise you are quoting common sense instead of your experiment.
Parallel scenario (not today's task): A test subject squeezed a hand dynamometer as hard as possible once every five seconds for eight trials while the grip force in kilograms was recorded. The question was whether fatigue changed the force output, when the change began, and how steep it was.\n\nGraph: a line graph with grip force (kilograms) on the y-axis and trial number on the x-axis, showing a flat plateau for the first few trials followed by a clear downward slope.\n\nClaim: Muscle fatigue reduced grip force output over the repeated squeezing trials.\n\nEvidence: The force stayed close to 44 kilograms for trials 1 through 3, then dropped to 40 kilograms at trial 4 and continued falling to 31 kilograms by trial 8, a total decline of about 13 kilograms. The steepest single drop, about 5 kilograms, happened between trial 4 and trial 5.\n\nReasoning: One low reading by itself could just be a bad squeeze or a measurement slip, but the steady downward slope from trial 4 onward shows a real trend rather than random error. As the muscle repeatedly contracted, its stores of ATP and phosphocreatine ran low and metabolic byproducts built up, so the fibers could no longer generate the same peak force. The graph pins the change to trial 4, where the plateau ends and the line begins to fall, and the sharp segment between trials 4 and 5 shows that is where fatigue set in fastest. This is why turning the numbers into a graph, instead of just reading the table, makes the exact moment and steepness of the change visible.
This model shows the level of evidence and organization needed to complete: A labeled graph of a measured variable versus trial number, plus a claim-evidence-reasoning argument that cites specific data values to explain how fatigue changed performance across repeated trials.
- Write one defensible claim.
- Choose specific evidence that supports the claim.
- Explain the scientific rule that connects the evidence to the claim.
Keep the structure. Replace the question, facts, measurements, and evidence. Then recheck units, vocabulary, and whether the conclusion goes beyond the evidence.
Also due today: Submit graph and CER as a single combined document.
- CER:
- Claim, Evidence, Reasoning: make a claim, back it with evidence, explain your reasoning.
- SOP:
- Standard Operating Procedure, the exact steps to follow (especially in a lab).
- Tracker:
- Your PLTW progress log where you record completed evidence.
- myPLTW:
- The PLTW course site where you do the online activities. Find it in Clever with your Microsoft sign-in, right next to Schoology.
Tap the speaker to hear a term. Add two of these to your notebook glossary with a definition and an example in your own words.
Pick just 2 or 3 words from today and make them yours: write what each one means in your own words, name the context clue or evidence that helped, then give one example from what you actually did in Kinesiology data analysis. Try your own words first; the glossary is there if you get stuck. This is voluntary and counts as extra credit, so keep it short.
Saved on this device. Show Mr. Mendoza or add these to your notebook glossary to claim the extra credit.
Hand-picked readings, videos, and interactives for this lesson, all free and from authoritative open organizations (NIH, CDC, OpenStax, Khan Academy, PhET, HHMI, and more).
A fillable, Cornell-style notebook for Unit 1: Road to Rehabilitation. Type your notes, cues, and summaries right in the PDF, or print it and write by hand. Each lesson page has a cue column, a notes column, and a summary box, plus dated lab-record pages you can turn in.
HBS Unit 1 notebook: Road to Rehabilitation Fillable PDFCornell notes + lab recordsOpenVetted readings and references for this unit. Use them to prepare, to catch up if you were absent, or to go deeper on today's target.
Check yourself · commit, then reveal▸
Claim ceiling for this check: The supplied lesson evidence can support an observation, pattern, classroom mechanism, or next-step decision about data analysis. It cannot by itself prove causation, establish a real clinical diagnosis, or justify action outside this classroom task.
A classmate's CER says: 'Fatigue lowered my range of motion because muscles get tired during exercise.' What is wrong with the evidence, and how do you fix it?
Write an answer and pick a confidence to unlock the key.
Fast retrieval with instant answers, not the commit-then-reveal check above. Try each from memory first: write what you remember about the earlier units, then check yourself here.
Go further and get help▸
What today's skills lead to. These are real health-science careers this course builds toward. Tap one to see, on the US Department of Labor's O*NET site, what the job actually involves, what it pays, and how fast it is growing.
Today is individual work you can do from home: complete the same target above, then submit your CER.
Open the drop folderTurn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not.
Class still runs. Complete the online activity above (it's self-guided). Need the concept taught without a teacher? Use this authoritative explainer:
Khan Academy: Joints and Movement- CompleteEvery required part of the artifact is present, nothing left blank.
- AccurateThe science and the data are correct and match the evidence.
- Scientific reasoningYou explain your claim with evidence and reasoning (CER), not just an answer.
- Professional communicationClear, organized, labeled, and written the way a clinician or scientist would.
- SubmittedTurned in the right way, on the class site or handed to Mr. Mendoza in class, and confirmed. Not in Schoology: that is where the report-card grade appears later.