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Sensor Type Picker

Run the Sensor Type Picker MicroSim fullscreen

About This MicroSim

Picking a sensor looks like a shopping problem and is actually a comprehension problem. The method is one question:

What physical property does this project actually need to measure?

Answer that in plain words — brightness, temperature, distance — and the sensor picks itself. Skip it, and you end up choosing by what sounds related.

The wrong answers here are deliberately plausible. A motion sensor and a distance sensor both sound right for "notice someone approaching", and the difference only shows up when you say what each one reports: one says something warm moved, the other says the nearest thing is 40 cm away. A robot that must stop before a wall needs the number; a display that wakes when someone enters does not.

One scenario is deliberately a trap about reading carefully. "Water a plant when the room air is very dry" says air, so the answer is humidity — even though a soil moisture sensor is the better engineering choice for the actual plant. The feedback says so. Noticing that the stated requirement and the sensible requirement differ is a skill worth having.

Each icon draws the property rather than the packaged part, so the picture is itself a hint: a droplet for humidity, a pulse bouncing off a wall for distance.

How to Use

  • Read the scenario and say the property out loud before clicking. That is the whole method.
  • Click a sensor. Correct answers get a green tick and the reason; wrong ones name what your pick actually measures and which property the scenario needed.
  • New scenario advances; the score tracks your pass through all eight.
  • Start over resets the score and returns to the first scenario.

Lesson Plan

Bloom level: Apply (L3) -- select

Learning objective: Given a short project scenario, the learner selects the sensor type best suited to the required measurement.

Before the sim (5 min). Put one project idea on the board and ask which sensor it needs. Then ask why — and make people say the property out loud. That second question is the skill.

With the sim (15 min). Run all eight. The motion-versus-distance pair is where the arguing happens, which is where the learning is.

After the sim (20 min). Have each student write a scenario for their own project idea, swap, and pick each other's sensor. Scenarios that cannot be answered are the useful outcome: they were not specific about what to measure, which means the project is not scoped yet.

Check for understanding. Ask: "When is a motion sensor the wrong choice for detecting a person?" When you need to know how far away they are, or when you need to detect someone standing still. Both come from the same question: what does it actually measure?

Embedding This MicroSim

Paste this into any page of the book, adjusting the relative path to docs/sims/ for the page's depth:

<iframe src="../../sims/sensor-type-picker/main.html" width="100%" height="522" scrolling="no"></iframe>

Specification

The full specification below is extracted from Chapter 17: Sensors, Displays, Motors, and Robot Chassis.

Type: microsim
**sim-id:** sensor-type-picker<br/>
**Library:** p5.js<br/>
**Status:** Specified

Purpose: Let a mentor rehearse the same question-driven selection process from the worked scenario above against several project ideas.

Bloom Taxonomy: Apply (L3)
Bloom Taxonomy Verb: select

Learning objective: Given a short project scenario, the learner selects the sensor type best suited to the required measurement.

Canvas layout: Left (60%) shows a project-scenario card with a one-sentence description ("Turn on a porch light automatically at dusk"); right (40%) shows six sensor icons the learner can select from.

Interactive controls: "New Scenario" button cycling through eight project prompts; six clickable sensor icons (light, temperature, humidity, motion, sound, distance); a feedback panel that appears after a selection.

Default parameters: Opens on the "porch light at dusk" scenario with no sensor selected.

Behavior: Clicking a sensor icon shows immediate feedback — green check and a one-sentence reason if correct, amber note explaining what property of the scenario points to a different sensor if incorrect — before advancing to the next scenario.

Implementation notes: p5.js canvas with an array of scenario objects, each carrying its correct sensor answer and a feedback string; click detection on six fixed icon regions.

References