ThinkWithPrompt by SynotticThinkWithPrompt

Write measurable learning objectives for a course

Turns vague course goals into observable, measurable objectives aligned to the right level of thinking and to assessment.

Learning & TeachingFew-ShotStandardEducationWriteFill-in Template

The prompt

Write measurable learning objectives for my course or module. Course topic: [TOPIC] Learners: [WHO - e.g. "new team leads, 0-6 months in role"] What the business wants people to do differently: [ON-THE-JOB BEHAVIOR OR RESULT] My current goals (rough is fine): [e.g. "understand coaching", "be aware of data privacy"] Format and length: [e.g. "2-hour virtual workshop", "20-minute e-learning"] Rules: - Use the ABCD structure where it helps: Audience, Behavior, Condition, Degree. - Every objective starts with one observable verb. Ban "understand", "know", "learn", "be aware of", "appreciate". - Match verbs to the level the job actually requires (Bloom's: remember, apply, analyze, evaluate, create). Do not inflate everything to "evaluate". - Distinguish terminal objectives (end of course) from enabling objectives (steps toward them). - Keep the count realistic for the time available; flag if I am trying to cover too much. Return: 1. A terminal objective for each major outcome, with 2-4 enabling objectives under it. 2. A table: Objective | Bloom's level | How it would be assessed | Practice activity that fits. 3. A rewrite of each of my original goals showing what changed and why. 4. Any objective that training alone cannot achieve (e.g. needs a process or tool change), flagged clearly.

Highlighted [brackets] are blanks to replace with your own details.

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What to fill in

  • TOPIC
  • WHO - e.g. "new team leads, 0-6 months in role"
  • ON-THE-JOB BEHAVIOR OR RESULT
  • e.g. "understand coaching", "be aware of data privacy"
  • e.g. "2-hour virtual workshop", "20-minute e-learning"

How this prompt is built

This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.

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