Q01
Message
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Student Feedback on Teacher Effectiveness Survey
Gathers student feedback on a teacher's clarity, fairness, engagement, and support, plus which qualities matter most to learning. An AI follow-up interview digs into the specific moment behind each student's overall recommendation score, surfacing concrete examples instead of vague praise or complaints.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
11 questions · ~6 min
Q02
MatrixRequired
How much do you agree with each statement about this teacher?
5 rows × 5 columns
- Explained concepts clearly
- Encouraged questions and participation
- Provided timely feedback on assignments
- Was fair and consistent in grading
- Made the subject engaging
Columns: Strongly disagree · Disagree · Neutral · Agree · Strongly agree
Q03
Opinion ScaleRequired
How likely are you to recommend this teacher to another student?
Scale: 0 – 10
Min:Not at all likelyMax:Extremely likely
Q04
Multiple Choice
In the past month, how often did you seek extra help from this teacher (office hours, email, or after class)?
- Never
- Once
- A few times
- Weekly or more
Q05
Rating ScaleRequired
How clear was the feedback you received on your assignments or exams?
Range: 1 – 5
Min:Not clear at allMax:Extremely clear
Q06
Best–Worst Trade-off (MaxDiff)Required
From this list of teaching qualities, which matters most to your learning and which matters least?
- Explains concepts clearly
- Is approachable and available for help
- Grades fairly and consistently
- Gives timely feedback
- Makes class engaging
- Adapts teaching to student needs
- Communicates expectations clearly
- Shows enthusiasm for the subject
Pick best & worst per setBest:Matters most to my learningWorst:Matters least to my learning
Q07
Multiple Choice
Which part of this teacher's class helped you learn the most?
- Lectures or explanations
- Class discussions
- Homework or practice problems
- Group work
- One-on-one help
Q08
AI Interview
Reconstruct the specific moment or example behind the respondent's overall recommendation score for this teacher: what happened, in what class or assignment, and how it made them feel about the teacher. If the score was low, probe what specifically fell short and whether it was a one-time issue or a pattern. If high, probe what the teacher did that other teachers typically don't.
Q09
Long Text
What one change would most improve this teacher's class for future students?
Q10
Multiple Choice
What is your current grade level or year?
- Freshman/9th grade
- Sophomore/10th grade
- Junior/11th grade
- Senior/12th grade
- Prefer not to say
Q11
Message
All done — thank you! Your responses will be reviewed alongside other students' feedback to help this teacher and the school improve teaching quality.
What’s included
AI follow-ups
Adaptive probes on open-ended answers that pull out detail a static form would miss.
Attention checks
Built-in safeguards against rushed answers and low-quality respondents.
AI-drafted copy
Wording, ordering, and branching written by the AI — tuned to your research goal.
Auto report
Themes, quotes, and a plain-English summary write themselves once responses come in.
How it compares
We reviewed the closest templates from other survey tools. Here’s what they do well — and where this template goes further.
Why this template
- Uses an AI follow-up interview to reconstruct the specific moment behind each student's recommendation score, turning vague praise or complaints into concrete examples
- Combines structured measurement (agreement matrix on clarity/fairness/engagement/support, clarity-of-feedback rating, recommend-likelihood scale) with a max-diff exercise to rank which teaching qualities matter most to learning
- Captures behavioral context (how often students sought extra help, which part of class helped most, grade level) alongside open-ended improvement suggestions for richer, more actionable reports
- Automated per-response quality scoring and auto-generated reports mean staff don't have to manually sift open-text comments to find the useful ones
Jotform
Math Department Student Feedback For Teachers Form TemplateA subject-specific (math department) static form for collecting student feedback on teachers, built on Jotform's drag-and-drop form builder. It's a ready-to-use template but narrowly scoped to one department rather than general teacher effectiveness. No mechanism is shown for probing into why a student rated something the way they did.
What it does well
- Easy to customize within Jotform's broad form-builder ecosystem
- Fielding-ready template with existing question set
- Fits naturally into schools already using Jotform for other forms
Where it falls short
- Static question list only — no adaptive follow-up to dig into a specific low or high rating
- Scoped to a math department context rather than teacher effectiveness broadly
- No automated scoring of response quality or auto-generated analysis report
SurveySparrow
Teacher Feedback Form TemplateA conversational-style teacher feedback template designed for both online and offline classes, reflecting SurveySparrow's chat-like form format. It's a fielding-ready template but relies on fixed questions rather than dynamic probing. Good for general rollout across class formats, but comment fields likely stay generic without follow-up.
What it does well
- Conversational one-question-at-a-time format may boost completion rates
- Explicitly supports both online and offline class contexts
- Ready to deploy without additional setup
Where it falls short
- No adaptive AI interview to surface the specific moment behind a student's rating
- No stated per-response quality scoring or automated report generation
- Prompt/question logic isn't published for transparency
Typeform
Free Teacher Feedback Form TemplateA free, general-purpose teacher feedback form using Typeform's signature clean one-question-per-screen design. It's a fielding-ready static template, well-suited for quick deployment, but it doesn't drill down into individual student experiences beyond the questions asked upfront. Analysis of open comments would be manual.
What it does well
- Polished, on-brand user experience with strong completion-friendly design
- Free to use, lowering the barrier to try it
- Simple to embed or share via link
Where it falls short
- No adaptive follow-up interview to reconstruct the reasoning behind a score
- No automated per-response quality scoring or auto-generated reporting
- No voice-based interview option for richer qualitative input
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.