Learning evidence
A measurement protocol, device limitations, data permission, graph and fair-comparison note
LEARNING PATHWAY
Sport, Data and Technology
Connects accelerometers, steps, reaction, heart rate, sampling, graphs, personal progress and data privacy to safe sports-technology projects.
CENTRAL QUESTION
How do you measure and interpret movement, training and wearable data without turning it into an unsupported health claim?
Completion evidence for this pathway is a measurement protocol, device limitations, data permission, graph and fair-comparison note. Page count or time spent alone does not demonstrate competence.
The intended capstone is an analysis of a movement task measured with micro:bit or simulation, including calibration and repeated tests. It should connect the lessons in one artefact and retain failed tests as evidence.
Capstone
An analysis of a movement task measured with micro:bit or simulation, including calibration and repeated tests
Return trigger
When the device, movement, sensor position, training condition or sharing purpose changes.
LESSON MAP
12 items from concept to evidence
Algorithms for Counting Steps and Repetitions
Lesson · Step and repetition algorithms transform noisy motion signals into events using thresholds, timing, state and reference counts. This lesson includes a work
Open page →Basic Movement Analysis with Video
Lesson · Basic video movement analysis uses a known scale, camera geometry, frame timing and tracked points to estimate position or speed. This lesson includes a wo
Open page →Fair Comparisons and Personal Progress
Lesson · Fair comparison controls relevant conditions and compares a learner with an appropriate baseline rather than ranking unlike people. This lesson includes a
Open page →How Wearable Technology Measures Activity
Lesson · Wearables combine sensors, algorithms and user profiles to estimate activity, but outputs depend on placement, model assumptions and context. This lesson i
Open page →Measuring Reaction Time
Lesson · Reaction-time measurement combines a random stimulus, precise timing, false-start control, repeated trials and cautious interpretation. This lesson include
Open page →Motion Sensors and Accelerometers
Lesson · Motion sensors estimate acceleration and orientation along axes, while gravity, placement, vibration and sampling affect the signal. This lesson includes a
Open page →Presenting Sports Data in Tables and Graphs
Lesson · Sports data should be presented with clear units, scales, conditions and uncertainty so a graph supports rather than distorts comparison. This lesson inclu
Open page →Privacy and Health Boundaries in Sports Technology
Lesson · Sports technology should minimise personal data and distinguish educational measurement from health or performance diagnosis. This lesson includes a worked
Open page →Project: A Reaction-Time Game
Project · This project builds an accessible reaction-time game with unpredictable starts, false-start handling, repeated trials and multimodal feedback. This lesson
Open page →Project: A Step Counter with micro:bit
Project · This project creates a micro:bit step counter as an algorithm experiment, using reference counts, state logic, error analysis and privacy-safe local output
Open page →Project: An Accessible Score and Training Dashboard
Project · This project creates an accessible score and training dashboard that shows local educational data without ranking, diagnosis or unnecessary identity. This
Open page →Sports Technology Quiz
Quiz · A 12-question interactive assessment for Sport, Data and Technology, with explanations and a newly shuffled option order on every start. This lesson includ
Open page →FOUR-WEEK PLAN
Place lessons in a production cycle
No week closes with reading alone. Use one session for concept and example, a second for practice, and a short third session for testing and explanation. Do not accelerate when a prerequisite is missing.
| Week | Focus | Evidence to produce |
|---|---|---|
| 1 | Algorithms for Counting Steps and Repetitions, Measuring Reaction Time, Project: A Reaction-Time Game | A measurement protocol, device limitations, data permission, graph and fair-comparison note |
| 2 | Basic Movement Analysis with Video, Motion Sensors and Accelerometers, Project: A Step Counter with micro:bit | An analysis of a movement task measured with micro:bit or simulation, including calibration and repeated tests |
| 3 | Fair Comparisons and Personal Progress, Presenting Sports Data in Tables and Graphs, Project: An Accessible Score and Training Dashboard | Error log and second version |
| 4 | How Wearable Technology Measures Activity, Privacy and Health Boundaries in Sports Technology | Quiz result, misconception and next application |
COMMON TRAPS
They look fast but weaken learning
- Treating a device value as a medical diagnosis
- Ranking different people with one number
- Failing to record measurement conditions
- Treating a small difference as real improvement
DEEPENING
Deepening evidence in Sport, Data and Technology
The pathway's distinctive question is: How do you measure and interpret movement, training and wearable data without turning it into an unsupported health claim? A first response may be a definition, but completion requires a measurement protocol, device limitations, data permission, graph and fair-comparison note. If input, method, limits and review date are unclear, the result is not traceable even when it looks strong.
Start with two different activities among Fair Comparisons and Personal Progress, How Wearable Technology Measures Activity, Motion Sensors and Accelerometers, Project: A Step Counter with micro:bit. In one, explain the concept in your own words; in the other, perform an application, measurement or user test. The two activities should not close with the same type of evidence. This distinction shows that Sport, Data and Technology has been tested through different forms of production.
Later connect Privacy and Health Boundaries in Sports Technology, Presenting Sports Data in Tables and Graphs, Algorithms for Counting Steps and Repetitions, Basic Movement Analysis with Video to the capstone: An analysis of a movement task measured with micro:bit or simulation, including calibration and repeated tests Keep failed tests as well as successful ones. For every error, record conditions, expected result, actual result, possible cause and the single change made.
Check these traps separately: Treating a device value as a medical diagnosis; Ranking different people with one number; Failing to record measurement conditions; Treating a small difference as real improvement. Reading a trap is insufficient; find an example from your own work and state which evidence made the problem visible.
Return rule: When the device, movement, sensor position, training condition or sharing purpose changes. Do not delete the previous record; add a date, changed tool or source, new evidence and the next mini trial. Progress is therefore tracked through the quality of explanation, application and correction—not the number of pages completed.
MICRO QUIZ
Test the reasoning behind the module
1. How do you measure and interpret movement, training and wearable data without turning it into an unsupported health claim?
The answer must produce evidence, not only a definition: A measurement protocol, device limitations, data permission, graph and fair-comparison note.
2. What should happen to the first failed test?
Keep it with conditions, expected result, actual result and the correction.
3. Does reading a source prove that practice occurred?
No. Sources define method and limits; practice evidence must be produced separately.
4. When should the module be reopened?
When the device, movement, sensor position, training condition or sharing purpose changes.
5. What does the capstone connect?
An analysis of a movement task measured with micro:bit or simulation, including calibration and repeated tests
OFFICIAL / PRIMARY SOURCES
Verify technical detail in current sources
WHO physical activity fact sheet
Primary or institutional source for method and technical limits.
Open source →WHO physical activity guidelines
Primary or institutional source for method and technical limits.
Open source →micro:bit accelerometer projects
Primary or institutional source for method and technical limits.
Open source →