student-feedback-analyzer

name	student-feedback-analyzer
description	Analyse student feedback and assessment results: identify patterns, surface knowledge gaps, evaluate teaching effectiveness, and generate improvement recommendations

Student Feedback Analyzer Skill

When to activate

• End-of-lesson or end-of-course feedback surveys have been collected
• Assessment results are in and you need to understand what students did and didn't learn
• You have qualitative feedback (open-ended responses) and need themes extracted
• Planning next year's course or next unit and want data to drive decisions
• A student cohort is underperforming and you need to diagnose why
• Peer review of teaching effectiveness requires structured evidence

When NOT to use

• Individual student grading or marking — that requires your subject expertise and knowledge of the student
• Predictive analytics requiring statistical modeling tools — use Python/R for that level of analysis
• Privacy-protected data with personally identifiable information — anonymize before passing to Claude

Privacy note

Never paste student feedback with names or other personally identifiable information unless it is already anonymized. Aggregated, anonymized responses are safe to analyze.

Instructions

End-of-course feedback analysis

Analyze this student feedback from [course name / lesson name].

COURSE/LESSON: [title]
INSTRUCTOR: [your name or anonymous]
COHORT: [number of students, grade level or professional context]
RESPONSE RATE: [N responses out of N enrolled]
COLLECTION METHOD: [survey / exit ticket / focus group / written reflection]

QUANTITATIVE RATINGS (paste your data):
[For each rating question: question text + average score + score distribution]
Example:
- "Overall course quality" — avg: 4.2/5 — distribution: 5★: 40%, 4★: 35%, 3★: 18%, 2★: 5%, 1★: 2%
- "Clarity of instruction" — avg: 3.8/5 — distribution: [paste]
- "Relevance to my goals" — avg: 4.5/5 — distribution: [paste]
- [Continue for all rated questions]

OPEN-ENDED RESPONSES (paste all responses — anonymized):
"What worked well?"
[Paste all responses]

"What could be improved?"
[Paste all responses]

"What had the most impact on your learning?"
[Paste all responses]

"Any other comments?"
[Paste all responses]

Analyze and generate:
1. Overall feedback summary (2-3 sentences, honest assessment)
2. Strongest elements (what students consistently valued)
3. Consistent criticisms (patterns in negative feedback — not one-offs)
4. Knowledge or skill gaps implied by the feedback
5. Actionable improvement recommendations (specific, not vague)
6. One surprising finding (something you might not have expected)
7. Suggested changes for the next iteration

---

Assessment results analysis

Analyze assessment results to identify learning gaps.

ASSESSMENT: [quiz / exam / project / assignment / standardized test]
COURSE/UNIT: [name]
COHORT: [N students]
ASSESSMENT DATE: [date]

OVERALL PERFORMANCE:
- Class average: [X]% / [X]/[total points]
- Median: [X]%
- Standard deviation: [X]
- Score distribution: [e.g., 90-100%: 8 students, 80-89%: 12, 70-79%: 6, below 70%: 4]

QUESTION-LEVEL ANALYSIS (paste for each question):
Question 1: [topic/concept tested] — correct: [X]% — avg score: [X/X]
Question 2: [topic] — correct: [X]% — avg: [X/X]
[Continue for all questions]

LEARNING OBJECTIVE MAPPING:
Objective 1: [text] — assessed by Q[numbers] — average performance: [X]%
Objective 2: [text] — assessed by Q[numbers] — average performance: [X]%
[Continue]

NOTABLE PATTERNS IN WRONG ANSWERS:
[If you tracked common error types, list them: "40% of students chose B instead of D on Q7 — what does that tell us?"]

Generate:
1. Summary of overall performance (was this assessment appropriate difficulty?)
2. Learning objectives that were mastered (≥ 80% average)
3. Learning objectives that are gaps (< 70% average)
4. Specific misconceptions suggested by the wrong answer patterns
5. Questions about your teaching: which concepts need to be retaught or re-explained?
6. Recommendations for reteaching: what approach would address the identified gaps?
7. Differentiation insight: are some students consistently struggling while others thrive? (don't name students)

---

Qualitative feedback theme extraction

Extract themes from open-ended student feedback.

CONTEXT: [what you asked / what course]
TOTAL RESPONSES: [N]

RAW RESPONSES (anonymized):
[Paste all text responses, one per line or separated by "---"]

Analyze for:

POSITIVE THEMES:
- Identify the top 3-4 things students consistently praised
- Quote 1-2 representative responses per theme
- Estimated frequency: what % of responses mentioned this theme?

IMPROVEMENT THEMES:
- Identify the top 3-4 consistent complaints or suggestions
- Quote 1-2 representative responses per theme
- Estimated frequency
- Distinguish: is this a preference complaint (they wanted more lecture) vs. a genuine gap (they didn't understand X)?

OUTLIER FEEDBACK:
- Responses that don't fit the patterns — highly positive outliers, highly negative outliers, unusual suggestions
- Are any of these actionable even if one person said it?

EMOTIONAL REGISTER:
- Was feedback generally positive, mixed, or negative in tone?
- Any signs of disengagement, frustration, or confusion beyond what the rating scores show?

ACTIONABILITY RATING:
For each theme: [Easy to act on / Requires curriculum change / Structural constraint / Not actionable]

---

Teaching effectiveness analysis

Analyze my teaching effectiveness based on these data sources.

INSTRUCTOR: [you / anonymous]
COURSE: [name, level]
TERM/PERIOD: [when]

DATA SOURCES (provide what you have):
- Student feedback survey ratings: [paste averages per question]
- Assessment performance data: [class averages on key assessments]
- Attendance/completion rates: [X% attendance / X% assignment completion]
- Open-ended feedback themes: [paste or summarize]
- Any peer observation notes: [paste if applicable]

SELF-ASSESSMENT:
What did you feel went well? [your own reflection]
What felt off or difficult? [your reflection]
What would you do differently? [your initial thoughts]

TEACHING GOALS FOR THIS COURSE:
[What were you explicitly trying to do — e.g., "more active learning, fewer lectures" / "improve exam pass rates" / "increase engagement with real-world examples"]

Generate:
1. Strengths analysis: what the evidence says you did well
2. Development areas: what the evidence suggests to improve
3. Alignment check: are students experiencing what you intended to create?
4. Comparison of your self-assessment to student evidence: where do you agree/disagree with your own students?
5. 3 specific development priorities for your next iteration
6. If applicable: professional development recommendation (workshop, coaching, peer observation focus)

---

Rapid exit ticket analysis

For fast in-class feedback processing:

Quickly analyze these exit ticket responses. I have 5 minutes between classes.

LESSON TOPIC: [topic]
LEARNING OBJECTIVE: [what students should be able to do]
NUMBER OF RESPONSES: [N]

EXIT TICKET QUESTION 1 (recall/knowledge):
[Paste all responses]

EXIT TICKET QUESTION 2 (application):
[Paste all responses]

EXIT TICKET QUESTION 3 (muddiest point — "what's still unclear?"):
[Paste all responses]

Give me:
1. % who demonstrated mastery on Q1 and Q2 (rough estimate)
2. Top 2-3 things still unclear from Q3
3. What I should address at the START of next class (under 2 minutes)
4. Who might need individual follow-up (based on pattern, not names)

Under 150 words. I need to read this fast.

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Cohort comparison analysis

Compare performance and feedback across two versions or cohorts of the same course.

COURSE: [name]
COHORT A: [description — term, format, cohort characteristics]
COHORT B: [description]

QUANTITATIVE COMPARISON:
| Metric | Cohort A | Cohort B | Difference |
|---|---|---|---|
| Avg assessment score | [X]% | [X]% | [+/-X%] |
| Feedback rating (overall) | [X]/5 | [X]/5 | [+/-] |
| Completion rate | [X]% | [X]% | [+/-] |
| [Other metric] | [X] | [X] | [+/-] |

WHAT CHANGED BETWEEN COHORTS:
[List any teaching changes, curriculum changes, format changes, or cohort differences]

QUALITATIVE COMPARISON (themes):
Cohort A strongest feedback themes: [list]
Cohort B strongest feedback themes: [list]
Cohort A biggest complaints: [list]
Cohort B biggest complaints: [list]

Analyze:
1. Did the changes you made improve learning outcomes? (what evidence supports this)
2. What else might explain the differences (cohort characteristics, external factors)
3. What to keep from the changes
4. What to revert or try differently

---

Course improvement report

Generate a course improvement report based on all available data.

COURSE: [name and version]
INSTRUCTOR: [name]
TERM: [period]

DATA SUMMARY:
- Student feedback (overall rating): [X/5]
- Assessment average: [X]%
- Completion rate: [X]%
- Top positive feedback themes: [list]
- Top improvement feedback themes: [list]
- Key knowledge gaps identified: [list]

TEACHING CHANGES I MADE THIS TERM (vs. prior version):
[List any deliberate changes]

MY REFLECTION:
[Your honest assessment of what worked and what didn't]

Generate a structured course improvement report:

## Performance Summary
[2-3 sentences on overall outcomes]

## What Worked (Keep)
[Evidence-based list of elements to retain]

## What Didn't Work (Change)
[Evidence-based list with specific replacement strategies]

## Knowledge Gap Remediation Plan
[For each identified gap: how to address it in the next iteration]

## 3 Priority Changes for Next Iteration
[Specific, actionable changes ranked by expected impact]

## Open Questions
[Things the data raised that you still don't know how to answer]

Example

User: End-of-course survey from a 20-person corporate training on "Stakeholder Communication." Quantitative: overall quality 4.1/5, content relevance 4.6/5, pacing 3.4/5 (lowest), activities 4.3/5, instruction clarity 3.9/5. Open-ended "what could improve": 8 responses mention "too much content for the time," 5 mention "wanted more practice scenarios," 2 mention "the morning sessions were too long," 1 mentions slides were hard to read. Open-ended "what worked best": 12 mention the case study exercise, 7 mention the small group discussions, 3 mention specific examples from their industry.

Expected output: Summary — the content was highly relevant but the pacing was the clear weakness. Strongest elements: the case study and small group discussion. Top criticisms: content density relative to time (40% of respondents), insufficient practice (25%). Actionable recommendations: cut 20% of content to leave breathing room (the relevance rating shows the right content is there — the problem is volume), add a second practice scenario to replace some lecture time, break the morning into two shorter blocks with a break. One insight: the pacing issue and the desire for more practice are the same problem — when the schedule is too tight, practice gets cut. Solving one solves both.

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