Education & Academic

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 student perceptions of rubric clarity, fairness, and usefulness for learning. Designed for course designers and academic researchers seeking to improve grading transparency and rubric design.

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.

Measures student engagement, satisfaction, perceived learning outcomes, and support needs in online and hybrid courses to guide evidence-based program improvements.

A structured instrument for expert reviewers to evaluate the clarity, completeness, and pre-specification quality of a pre-analysis plan or preregistration draft before data collection begins.

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.

Assesses whether research participants understood key elements of informed consent—study purpose, risks, data handling, and participant rights. Designed for post-study administration to support IRB compliance and iterative improvement of consent materials.

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

A team-level diagnostic for mixed-methods research projects, evaluating convergence, divergence, and integration rigor across qualitative and quantitative strands to guide analytic next steps.

Measures student awareness, perceived value, enrollment drivers, outcomes, and barriers related to certificates and microcredentials. Designed for higher-education and online-learning researchers seeking actionable segmentation and intent data.

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.

Diagnoses why participants miss or consider skipping follow-up time points in longitudinal research, and identifies actionable levers—reminders, incentives, burden reduction—to improve retention and compliance.

Measures teacher-reported reliability, error frequency, and satisfaction with LMS integrations and single sign-on (SSO) systems. Use this instrument to identify failure points, prioritize fixes, and benchmark integration performance over 30-day recall windows.

Screens prospective research participants for eligibility and verifies comprehension of informed consent elements before enrollment in an academic study.

Measures the quality of institutional internship support, supervision quality, and skill development outcomes to guide program improvement for higher education administrators and career services teams.

Collects structured employer feedback on intern performance, program structure, supervision investment, and organizational outcomes to guide internship program improvements.

A pre-study instrument that verifies participants' understanding of consent information, risks, and withdrawal rights. Designed for IRB compliance and suitable for any human subjects research protocol.

Measures educators' AI tool usage patterns, institutional policy clarity, observed classroom outcomes, and support needs to inform evidence-based AI integration strategies across K-12 and higher education contexts.

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.

Measures awareness decay of recently launched features across user segments, identifying which communication channels sustain visibility and where messaging drop-offs occur post-launch.