Teaching Assistant Effectiveness Evaluation Survey
Gathers student feedback on a teaching assistant's clarity, responsiveness, fairness, and classroom presence across lectures, labs, or office hours. Includes a best-worst trade-off on what matters most in a TA and an AI follow-up that digs into the specific moment behind each student's overall rating, surfacing concrete strengths and fixable gaps departments can act on.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
In what setting did you most often interact with this TA?
- Discussion or recitation section
- Lab session
- Office hours only
- Grading/feedback only, no in-person contact
- A mix of several of these
How much do you agree with each statement about this TA?
- Explains concepts in a way that's easy to understand
- Responds to questions (in person or by email) in a reasonable amount of time
- Gives feedback on assignments that helps me improve
- Creates a respectful, welcoming environment for questions
- Is well-prepared for sections, labs, or office hours
- +1 more
In the last month, how many times did you attend this TA's office hours or a session they led outside of required class time?
How would you rate the clarity and usefulness of the written or verbal feedback this TA gives on your work?
Of the qualities below, which matters most to you in a teaching assistant, and which matters least?
- Deep knowledge of the course material
- Clear, organized explanations
- Fast, reliable responses to questions
- Fairness and consistency in grading
- Approachability and patience
- Enthusiasm for the subject
- Availability outside of scheduled hours
Overall, how effective has this TA been in supporting your learning this term?
Anchor on the respondent's overall effectiveness rating and ask them to describe one specific, recent moment with this TA (a session, an email exchange, a graded assignment) that shaped that score. If the rating was low, probe what the TA could have done differently in that moment; if high, probe what specifically made it work so the department can reinforce that behavior. If the respondent gives a vague or purely abstract answer, ask for a concrete example before moving on.
Is there anything specific you'd suggest to help this TA improve, or anything you want the department to know?
What is your current class standing?
- First-year
- Second-year
- Third-year
- Fourth-year or beyond
- Graduate student
- Prefer not to say
That's everything — thank you for your feedback! Your responses will be combined with others and shared with the department to help recognize strong TAs and target support where it's needed.
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
- Includes a dedicated AI follow-up interview that anchors on each student's overall effectiveness rating and digs into the specific moment behind it, surfacing concrete strengths and fixable gaps
- Combines structured measurement (matrix agreement statements, rating and opinion-scale questions) with a best-worst (max-diff) trade-off to identify what students value most in a TA
- Captures context often missed in static forms, such as setting of interaction (lecture, lab, office hours), frequency of office-hour attendance, and class standing, so departments can segment results
- Closes with an open-ended improvement question and an automated report, giving departments both quantitative scores and actionable qualitative detail per TA
SurveyMonkey
Teaching Assistant Evaluation: TA Survey ExamplesThis is a directly comparable, fielding-ready template covering TA evaluation from students, with example questions on clarity and support. It's a static questionnaire built for broad, quick deployment rather than adaptive probing, and it doesn't appear to include a trade-off exercise on what matters most in a TA. SurveyMonkey's strength here is ease of setup and its large template library for academic use cases.
What it does well
- Well-established, easy-to-launch template built specifically for TA evaluation
- Backed by a large, familiar survey platform with broad institutional adoption
- Likely includes standard rating-style questions covering common TA evaluation criteria
Where it falls short
- Static question set with no adaptive AI follow-up to probe the reasoning behind a rating
- No visible best-worst/trade-off mechanism to reveal which TA qualities matter most to students
- No published methodology on how questions were designed or scored, unlike QuestionPunk's transparent prompts and automated per-response quality scoring
Frequently asked questions
What questions are in the “Teaching Assistant Effectiveness Evaluation Survey” template?
The template includes 11 ready-to-use questions, starting with: “Thanks for taking a few minutes to reflect on your experience with your teaching assistant this term. Your honest feedba…” · “In what setting did you most often interact with this TA?” · “How much do you agree with each statement about this TA?”. The full set is previewed above, and every question is editable.
How long does this survey take to complete?
Respondents typically finish the 11 questions in about 6 minutes.
Can I customize this template?
Yes — every question, answer option, and the ordering is editable before you launch. You can add or remove questions, or ask the AI editor to rework the survey around your research goal.
Is this template free to use?
Yes. Open it in the editor and start customizing right away — no account required to try it, and the free plan covers launching your survey.
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.
Related templates
More studies on similar topics.
360 Peer Evaluation & Working Relationship Survey
A structured peer-review template for 360-degree feedback cycles — covering core collaboration competencies, an overall effectiveness rating, and a best-worst prioritization of development areas — plus an AI follow-up interview that reconstructs the concrete incident behind the rating so feedback isn't just a number. Built for HR, people ops, and team leads running peer review cycles.
View templateProfessor Teaching Effectiveness Evaluation Survey
Measures how students experience a professor's teaching across clarity, engagement, feedback, availability, and fairness, plus overall satisfaction and likelihood to recommend. Built for academic departments and course coordinators running end-of-term reviews, with an AI follow-up interview that digs into the specific moment or assignment that most shaped a student's rating.
View templateStudent 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.
View templateHigh School Teacher Evaluation & Classroom Effectiveness Survey
Gathers student feedback on a teacher's instructional clarity, fairness, availability, and classroom management, then uses an AI follow-up interview to unpack a specific moment behind the overall rating. Built for department chairs, instructional coaches, and school administrators running end-of-term teacher evaluations.
View templateAI Tutor Effectiveness and Student Learning Outcomes Survey
Measures how effectively an AI tutor supports student comprehension, engagement, and skill growth, for educators and edtech teams evaluating tutoring tools, with an AI follow-up interview that reconstructs a recent tutoring session to surface what helped or confused the student.
View templatePost-Lecture Feedback & Teaching Quality Survey
Captures how clear, well-paced, and engaging a lecture was, plus which specific aspects (pacing, examples, Q&A, materials) students most want improved. An AI follow-up interview digs into a concrete moment the student found confusing or engaging, turning vague 'it was fine' ratings into specifics an instructor can act on.
View template