Synthetic Data for EdTech
An education dataset is a many-to-many world: students enroll in courses, courses produce assessments, assessments produce scores. Misata wires the junction tables, lets you declare a completion or pass rate that comes out exactly, and keeps score distributions realistic instead of flat.
The tables Misata generates
studentsDemographics, cohorts, enrollment counts that roll upcoursesSubjects, levels, instructorsenrollmentsStudent-course junction, status, completion at a declared rateassessmentsScores with a realistic distribution per courseWhat holds true, every time
- Completion or pass rate hits your declared target
- Every enrollment references a real student and a real course
- Score distributions are realistic, not uniform
- A student's enrollment count reconciles with their rows
Frequently asked
Do I need real EdTech data to generate this?
No. Misata builds the dataset from a specification, not a sample. There is no real EdTech data to source, anonymize, or leak. You describe the tables you need and the engine constructs them with referential integrity and realistic distributions.
Is the generated EdTech data privacy safe?
Yes, by construction. Nothing is learned from real records, so there is no membership to infer and nothing to leak. It runs entirely on your machine with no API key for the core engine.
Can I control the outcomes, like rates and totals?
Yes. Declare a target such as a monthly volume curve or an event rate and Misata produces rows that hit it exactly, while foreign keys stay intact and roll-up columns reconcile after a JOIN.
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