교육·학술

A research-grade instrument for verifying that experimental participants correctly perceived, understood, and interpreted a presented stimulus. Designed for researchers conducting experiments who need to validate manipulation fidelity and comprehension before analyzing dependent variables.

A structured intake instrument for statisticians and researchers to systematically document effect size, variance, design, and attrition assumptions needed to conduct power and sample size calculations.

Captures how school students are experiencing remote or online classes — focus, workload, technology access, and connection with teachers and classmates — with an AI follow-up that digs into the single biggest obstacle they name. Built for school administrators, ed-tech teams, and researchers evaluating distance learning programs.

A bi-weekly pulse survey measuring student engagement behaviors, support resource usage, and perceived learning progress. Designed for course instructors and instructional designers seeking actionable insights to improve eLearning and hybrid course experiences.

A parent/guardian survey measuring the clarity, timeliness, and overall effectiveness of K-12 school communications across channels (email, app, text, print). Provides actionable data to improve family engagement and information delivery.

Captures how well remote or hybrid learning is working for your students and families — engagement, workload, tech reliability, and communication — with an AI follow-up that digs into the single biggest obstacle parents are facing at home.

Measures how confident and supported parents feel helping their child with schoolwork, which school resources they actually use, and where the biggest gaps are. Includes an AI follow-up interview that reconstructs a real recent moment of struggle to surface what support would have actually helped. Built for schools and PTAs planning parent-engagement programs.

A structured pilot review instrument for expert panelists or cognitive interview participants to evaluate draft survey items on clarity, redundancy, response-option quality, and face validity before fielding.

Measures students' intent to re-enroll, sense of belonging, and satisfaction with academic, financial, and mental-health support, then uses an AI follow-up interview to surface the real story behind low re-enrollment intent or a student weighing whether to leave. Built for student affairs, advising, and retention teams who need to spot risk before it becomes attrition.

Measures how satisfied Pre-K through 12th grade parents are with their child's school across communication, safety, academic rigor, and teacher responsiveness, and shows what matters most to them when priorities compete. An AI follow-up interview digs into the specific experience behind their overall rating instead of just the number, for school leaders and PTA/district research teams.

Measures how satisfied parents are with their child's school across communication, safety, academics, and responsiveness — with an AI follow-up that digs into the specific moment or interaction driving each parent's overall recommendation score. Built for school administrators and PTA leaders who want more than a satisfaction number.

Measures how students experienced a social studies course — material relevance, teaching clarity, discussion quality, and which units actually landed — for teachers, department heads, and curriculum teams reviewing a course after a term. An AI follow-up interview digs into the reasoning behind students' recommend score and topic preferences instead of stopping at the number.

Measures current students' and recent graduates' connection to their institution, giving likelihood and motivations, barriers to donation, and communication preferences to inform alumni relations and advancement strategy.

Collects updated contact and career details from graduates re-registering with your alumni network, along with their interest in mentoring, events, and giving. An AI follow-up interview digs into what would genuinely motivate them to reconnect, rather than just which boxes they check.

Measures how well a school's contact with parents actually works — which channels get used, how quickly urgent matters get followed up, and whether messages get read at all. The AI follow-up reconstructs one specific recent contact experience in detail, surfacing breakdowns that satisfaction scores alone would miss.

Gathers feedback from students who acted, sang, danced, ran crew, or played in the pit for their school's musical — covering rehearsal workload, mentorship, teamwork, and personal growth. An AI follow-up interview digs into one specific challenging moment from the production, giving directors and drama teachers concrete detail behind the ratings.

수료증 및 마이크로자격증과 관련한 학생들의 인지도, 인식 가치, 등록 동기, 성과, 장애 요인을 측정합니다. 실행 가능한 세분화 및 의향 데이터를 찾는 고등교육 및 온라인 학습 연구자를 위해 설계되었습니다.

Measures how clearly parents understand what they're agreeing to when they sign school consent forms — for field trips, media releases, research studies, or medical care — and how much they trust the process. An AI follow-up interview digs into the specific confusion or hesitation behind any low-confidence answer instead of leaving it as a number.

Captures why families register for your Vacation Bible School, their scheduling and logistics preferences, and any health or accessibility needs — with an AI follow-up that surfaces the real motivations and hesitations behind a family's choice instead of just checkbox answers.

Evaluates students' capstone project experiences—including mentorship quality, resource availability, skill development, and key challenges—to inform data-driven program improvement decisions.