KHAEMENES ACADEMY · PHYSICS 12 · WEEK 01 · DAY 04

Measurement & Video-Data Investigation

Collect or analyze a safe motion dataset and document uncertainty from raw observation through model.

Explicit physics instruction

This investigation can use a supplied video dataset or a safe household-scale motion clip. The goal is data lineage: raw positions and times must remain available before graphing or fitting.

Choose a reference point and coordinate direction before measuring. Record frame/time resolution and spatial scale so uncertainty can be estimated.

Graph position versus time, calculate at least one slope, and compare model prediction with observation using a residual or uncertainty statement.

Vocabulary: reference frame; calibration; raw data; model fit; reproducibility.

Worked example with reasoning

A video scale shows 1.00 m across 200 pixels, so 50 pixels corresponds to 0.25 m. If frame timing is 0.10 s, a 0.25 m displacement over one interval corresponds to 2.5 m/s average velocity.

Guided question

Which two calibration quantities must be known before pixel positions can become physical motion data?

Independent work

  1. Preserve a raw time-position table.
  2. Document spatial and time calibration.
  3. Graph position vs time and calculate one velocity.
  4. State one uncertainty and one method revision.

Investigation / Data Lab

Preserve raw motion data, calibration, graph, calculations, uncertainty, and one revision.

Misconception check

A graph is processed evidence; it should not replace the raw measurements that produced it.

Formative understanding check

Why preserve calibration and raw positions?

Reveal response

They allow another person to reproduce the conversion and test the model.

Mentor evidence conference

Ask Archaemenes to audit units, assumptions, coordinate choices, and evidence limits.

Teacher guidance

Foundation: Use supplied data if independent collection is difficult.

Core: Complete calibration, graph, slope, and uncertainty analysis.

Extended: Compare linear and nonlinear fits using residual evidence.