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Post-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.

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샘플 질문

템플릿에 포함된 내용을 미리 확인해 보세요. 모든 질문은 설문 공개 전에 자유롭게 수정할 수 있습니다.

질문 11개 · 약 6분
Q01
메시지

Hi! We'd love your honest feedback on the lecture you just attended (or watched). Your responses are completely confidential and anonymized. It takes about 7 minutes and helps the instructor improve future sessions.

Q02
객관식필수

In the past month, how many of this course's lectures have you attended in person or watched live?

  • All of them
  • Most of them
  • About half
  • A few
  • None
Q03
의견 척도필수

How likely are you to recommend this lecture to a classmate who's deciding whether to attend?

척도: 0 – 10
최소:Not at all likely최대:Extremely likely
Q04
매트릭스필수

How much do you agree with each statement about this lecture?

5개 행 × 5개 열
  • The pace of the lecture was appropriate
  • The instructor explained concepts clearly
  • The examples used helped my understanding
  • The slides or visual materials supported learning
  • The instructor encouraged questions and discussion
열: Strongly disagree · Disagree · Neutral · Agree · Strongly agree
Q05
평점 척도

How would you rate the instructor's responsiveness when you asked questions during or after the lecture?

범위: 1 – 5
최소:Poor최대:Excellent
Q06
최선·최악 선택형(MaxDiff)필수

If the instructor could only change a few things about this lecture, which would matter most to you?

  • More real-world examples
  • More time for questions and discussion
  • Clearer explanations of core concepts
  • Better structured or paced content
  • More engaging delivery style
  • Improved slides or visual aids
  • More opportunities for hands-on practice
세트별 최선·최악 선택최선:Would improve the lecture most최악:Would improve the lecture least
Q07
AI 인터뷰

Ask the respondent to describe a specific moment in the lecture where they felt most engaged or most lost, anchoring on whichever statement they rated lowest in the pacing/clarity/examples battery. Get concrete detail: what was being explained at that point, what confused them or made it click, and what the instructor could have done differently right there. If they flagged a top improvement area in the trade-off question, ask them to describe what a fixed version of that lecture would actually look like.

Q08
장문형

Is there anything else about this lecture you'd like to share with the instructor or course organizers?

Q09
객관식

What is your current year or level of study?

  • First year
  • Second year
  • Third year
  • Fourth year or above
  • Graduate student
  • Prefer not to say
Q10
객관식

What is your field of study or department? (Template note: replace the list below with your institution's actual departments before launching.)

  • (Replace with Department A)
  • (Replace with Department B)
  • (Replace with Department C)
  • Other
  • Prefer not to say
Q11
메시지

Thanks so much for the feedback! Your responses are combined with other students' answers into a summary report the instructor uses to improve future lectures — individual answers are never shared with a name attached.

포함된 기능

  • AI 후속 질문

    정형화된 설문이 놓치는 세부 내용을, 주관식 답변에 맞춰 AI가 심층 질문으로 끌어냅니다.

  • 주의력 확인 장치

    성의 없는 답변과 저품질 응답자를 걸러내는 내장 안전장치입니다.

  • AI가 작성한 문안

    문구, 질문 순서, 분기 로직까지 AI가 연구 목표에 맞춰 작성합니다.

  • 자동 리포트

    응답이 모이면 주요 주제, 인용문, 이해하기 쉬운 요약이 자동으로 작성됩니다.

다른 서비스와 비교

다른 설문 도구의 가장 유사한 템플릿을 검토했습니다. 그 도구들이 잘하는 점과, 이 템플릿이 한발 더 나아가는 지점을 정리했습니다.

이 템플릿을 선택하는 이유

  • Combines quick quantitative pulses (pacing/clarity opinion scale, agreement matrix, instructor responsiveness rating) with a max-diff question that forces students to prioritize which single change matters most
  • Includes an AI follow-up interview that asks the student to describe one specific confusing or engaging moment, turning generic 'it was fine' answers into concrete, actionable detail for the instructor
  • Bookends the survey with friendly chat-style intro/outro messages and closes with an open long-text field so students can add anything the structured questions missed
  • Captures context (attendance frequency, year of study, field/department) so instructors can see if feedback patterns differ by student segment

SurveyMonkey

Teaching Assistant Evaluation: TA Survey Examples

This is a ready-to-use template on an established survey platform, aimed at evaluating teaching assistants rather than lecture-specific instructor performance, so it's a related but not identical use case. It likely covers standard rating and open-text questions typical of SurveyMonkey's education templates, with the platform's usual reporting and distribution tools. No AI-driven probing or per-response scoring is part of the offering.

잘하는 점

  • Backed by a well-known, mature survey platform with broad distribution and analytics tooling
  • Purpose-built for academic/teaching evaluation context, so question framing is likely relevant to instructors
  • Ready to deploy without configuration, useful for a quick baseline TA evaluation

아쉬운 점

  • Static question set with no adaptive AI follow-up to probe vague answers like 'it was fine'
  • No voice AI interview option or guided screen-share tasks
  • No transparent, published prompt/methodology for how responses are interpreted or scored

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