Analyze course evaluations to improve teaching and courses
Finds themes, patterns, and fixes in student course evaluation scores and comments, with caveats about bias.
The prompt
Help me make sense of student course evaluations.
Course(s) and term(s): [DETAILS]
Response rate and enrollment: [e.g. "34 of 90 responded"]
Numeric results: [PASTE item scores, and prior terms if available]
Open-ended comments: [PASTE]
Changes I made this term: [DETAILS]
Purpose: [my own improvement / annual review / tenure or promotion dossier / department review]
Steps:
1. Summarize numeric results and compare with prior terms; note when differences are too small or the response rate too low to mean much.
2. Code the comments into themes (e.g. clarity, workload, feedback speed, organization, assessment fairness, instructor availability) with counts and short representative quotes.
3. Separate actionable feedback from comments about things I cannot change (required content, time of day).
4. Flag comments that look biased (about appearance, accent, gender, or identity) so they can be set aside, not acted on.
5. Recommend 3 specific course changes, each tied to a theme.
6. If the purpose is a review dossier, draft a 200-word reflective statement on what I learned and what I changed.
Return the theme table (Theme | Count | Example | Action), the recommendations, and the statement if requested. Do not overstate trends from small samples.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Research & Summaries prompts
What to fill in
- DETAILS
- e.g. "34 of 90 responded"
- PASTE item scores, and prior terms if available
- PASTE
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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