교육·학술

Captures how families experienced your school's inquiry-to-decision journey — from first contact through tours, interviews, and the admissions decision — with an AI follow-up that digs into the single moment that most shaped their impression of the school.

Measures student perceptions of application requirements, deadlines, and status communications across admissions, enrollment, and financial-aid processes. Use insights to identify friction points and improve institutional communication.

Measures how satisfied students are with instruction, advising, facilities, and support services this term, and what the college should prioritize improving next. An AI follow-up interview digs into the story behind each student's recommendation score, surfacing specific incidents and unmet needs that closed-ended ratings miss. Built for academic affairs and student experience teams.

Captures how students research schools, manage the application workload, and weigh factors like cost, location, and program strength — for high school counseling offices, admissions consultants, and ed-tech tools. An AI follow-up interview digs into the single biggest obstacle each student hit during the process.

Captures how smooth (or frustrating) the school registration process was for parents and guardians — from completing forms to submitting documents — with an AI follow-up that digs into the single biggest obstacle families hit. Built for school offices and district enrollment teams who want to fix friction before the next enrollment cycle.

학생과 가족이 개학 후 학교 복귀를 얼마나 원활하게 경험했는지를 측정합니다 — 첫날 이전의 소통, 물류, 정서적 준비도, 스트레스 요인 — 내년 전환을 계획하는 교육구, 학교 또는 학부모회를 위한 조사입니다. AI 후속 인터뷰는 만족도 점수를 넘어, 일이 잘 진행되었거나 어긋난 구체적인 순간을 재구성합니다.

Captures how residents, students, and local business owners experience neighborhood change linked to growing student populations — rents, displacement, local business shifts, and town-gown relations — with an AI follow-up that digs into specific, lived examples behind the numbers instead of vague impressions.

Measures how smoothly students moved through course registration — from system usability to getting into desired classes — for registrars, advising offices, and student success teams. An AI follow-up interview digs into the specific moment registration broke down for each student, surfacing fixable friction that a satisfaction score alone can't explain.

Measures student perceptions of messaging timeliness, channel preference, and support quality in online or fintech cohort programs to identify actionable communication improvements.

Measures student perceptions of fairness, privacy, and acceptability of proctoring practices in remote exams. Designed for higher-education institutions seeking to evaluate and improve remote assessment policies.

온라인 및 하이브리드 강좌에서 학생의 참여도, 만족도, 인지된 학습 성과, 지원 요구 사항을 측정하여 근거 기반의 프로그램 개선을 이끌어내기 위한 설문입니다.

Measures what path graduating students are choosing after high school (college, work, military, trade, gap year), how confident they feel, and who influenced the decision — for counselors, schools, and districts. An AI follow-up interview digs into the specific uncertainty or concern behind each student's plan.

Measures student perceptions of financial aid application clarity, fairness, and support quality. Designed for higher-education institutions seeking actionable feedback from applicants and non-applicants within the past 12 months.

A research-grade survey for K–12 parents and guardians to assess perceived school safety, evaluate communication effectiveness, and identify priorities for prevention investment.

A short weekly pulse check for students learning remotely — covering engagement, workload, tech reliability, and connection to instructors. An AI follow-up interview digs into whatever obstacle the student flagged (tech, motivation, unclear instructions) to find out exactly what happened and what would help, instead of stopping at a checkbox.

Measures student perceptions of campus safety, emergency communication effectiveness, and awareness of support resources to inform institutional safety planning and resource allocation.

Measures how likely students are to recommend a course, program, or instructor to a peer, what drives that willingness, and where the experience falls short. An AI follow-up interview digs into the specific reasons behind each student's score, surfacing concrete moments and details that a rating alone can't capture. Built for academic departments, bootcamps, and course teams tracking word-of-mouth and retention risk.

Captures how families and students experienced remote and hybrid learning during the coronavirus pandemic — engagement, workload, tech access, and school communication — for education researchers and administrators. An AI follow-up interview digs into the single biggest obstacle a respondent flags, reconstructing what actually happened on a typical remote-learning day.

학생들이 교육, 학업 상담, 시설, 캠퍼스 생활에 얼마나 만족하는지 추적하고, 최선-최악 선택 방식을 활용하여 어떤 개선 사항이 가장 중요한지 파악합니다. AI 후속 인터뷰는 각 학생의 만족도 점수 이면에 있는 구체적인 순간이나 경험을 파고들어 폐쇄형 질문이 놓치는 세부 사항을 끌어냅니다.

Measure behaviors people won't admit directly: the list experiment (item count technique) asks only HOW MANY statements apply — never which — so individual answers stay genuinely deniable while group comparisons reveal the true rate. The native question type randomizes control and treatment lists for you.