PHYSICS-07 · Undergraduate · ENGLISH · PLANNED
Predict, observe a demonstration, draw a model, then test its limits.
Study time is an initial activity estimate, excluding extra reading, revision and extended laboratory or project work. Videos and question bodies have not been produced by adding this outline.
Complete these courses or demonstrate equivalent knowledge. These are recommended prerequisites, not enforced enrolment locks.
YouTube playlist: ARK Physics | Experimental and Computational Physics
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Sign in to save this learning pathPractical outcome: Plan an investigation with a calibration and uncertainty budget.
Explain researchable questions using a visual and one worked real-world example. Context: Plan an investigation with a calibration and uncertainty budget. End with a prediction or check-for-understanding.
Explain controlled variables using a visual and one worked real-world example. Context: Plan an investigation with a calibration and uncertainty budget. End with a prediction or check-for-understanding.
Explain calibration curves using a visual and one worked real-world example. Context: Plan an investigation with a calibration and uncertainty budget. End with a prediction or check-for-understanding.
Explain resolution limits using a visual and one worked real-world example. Context: Plan an investigation with a calibration and uncertainty budget. End with a prediction or check-for-understanding.
Use the first concepts from this section. Plan an investigation with a calibration and uncertainty budget. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain repeated trials using a visual and one worked real-world example. Context: Plan an investigation with a calibration and uncertainty budget. End with a prediction or check-for-understanding.
Explain linear fitting using a visual and one worked real-world example. Context: Plan an investigation with a calibration and uncertainty budget. End with a prediction or check-for-understanding.
Explain residual analysis using a visual and one worked real-world example. Context: Plan an investigation with a calibration and uncertainty budget. End with a prediction or check-for-understanding.
Explain reporting uncertainty using a visual and one worked real-world example. Context: Plan an investigation with a calibration and uncertainty budget. End with a prediction or check-for-understanding.
Plan an investigation with a calibration and uncertainty budget. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Researchable questions, Controlled variables, Calibration curves and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Compare a simulated falling object with an analytic limit.
Explain units in code using a visual and one worked real-world example. Context: Compare a simulated falling object with an analytic limit. End with a prediction or check-for-understanding.
Explain time stepping using a visual and one worked real-world example. Context: Compare a simulated falling object with an analytic limit. End with a prediction or check-for-understanding.
Explain euler integration using a visual and one worked real-world example. Context: Compare a simulated falling object with an analytic limit. End with a prediction or check-for-understanding.
Explain energy drift using a visual and one worked real-world example. Context: Compare a simulated falling object with an analytic limit. End with a prediction or check-for-understanding.
Use the first concepts from this section. Compare a simulated falling object with an analytic limit. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain convergence tests using a visual and one worked real-world example. Context: Compare a simulated falling object with an analytic limit. End with a prediction or check-for-understanding.
Explain monte carlo uncertainty using a visual and one worked real-world example. Context: Compare a simulated falling object with an analytic limit. End with a prediction or check-for-understanding.
Explain reproducible seeds using a visual and one worked real-world example. Context: Compare a simulated falling object with an analytic limit. End with a prediction or check-for-understanding.
Explain visualising trajectories using a visual and one worked real-world example. Context: Compare a simulated falling object with an analytic limit. End with a prediction or check-for-understanding.
Compare a simulated falling object with an analytic limit. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Units in code, Time stepping, Euler integration and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Write a reproducible report from a supplied experiment dataset.
Explain laboratory notebooks using a visual and one worked real-world example. Context: Write a reproducible report from a supplied experiment dataset. End with a prediction or check-for-understanding.
Explain data provenance using a visual and one worked real-world example. Context: Write a reproducible report from a supplied experiment dataset. End with a prediction or check-for-understanding.
Explain outlier decisions using a visual and one worked real-world example. Context: Write a reproducible report from a supplied experiment dataset. End with a prediction or check-for-understanding.
Explain error bars using a visual and one worked real-world example. Context: Write a reproducible report from a supplied experiment dataset. End with a prediction or check-for-understanding.
Use the first concepts from this section. Write a reproducible report from a supplied experiment dataset. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain model comparison using a visual and one worked real-world example. Context: Write a reproducible report from a supplied experiment dataset. End with a prediction or check-for-understanding.
Explain parameter fitting using a visual and one worked real-world example. Context: Write a reproducible report from a supplied experiment dataset. End with a prediction or check-for-understanding.
Explain reproducible figures using a visual and one worked real-world example. Context: Write a reproducible report from a supplied experiment dataset. End with a prediction or check-for-understanding.
Explain limits of inference using a visual and one worked real-world example. Context: Write a reproducible report from a supplied experiment dataset. End with a prediction or check-for-understanding.
Write a reproducible report from a supplied experiment dataset. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Laboratory notebooks, Data provenance, Outlier decisions and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Combine all three sections: Write a reproducible report from a supplied experiment dataset. Submit an original worked artifact, evidence or tests, assumptions and a reflection. Rubric: correctness, method, practical usefulness, reproducibility and communication. Self-reviewed, not certified.
6–12 balanced questions sampling every section of Experimental and Computational Physics, including interpretation, calculation or reasoning, a misconception and a new application. Passing is 70%; review errors before retrying.
Diagrams, virtual experiments, data analysis and simulations. Guided work leads to an independent attempt, with a diagram, explanation, trace, calculation or tested artifact. Question bodies are generated when opened in your private learning path. Programming submissions are stored as text; the platform does not execute submitted code.
Use original or appropriately licensed datasets. Practical science activities use models or supplied data unless a qualified supervisor has reviewed a physical experiment.
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