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Generate EdTech Synthetic Data in Python

EdTech platforms have a distinctive data model: instructors create courses, students enroll and progress at non-uniform rates, quiz scores cluster toward passing (most students who attempt pass), and enrollment spikes around the new year and back-to-school season. Misata generates a five-table EdTech dataset with all of this built in — instructors, courses, students, enrollments, and quiz attempts — with no orphaned foreign keys and realistic completion and certificate issuance rates.

import misata

tables = misata.generate(
    "An edtech platform with 5k students and 200 courses",
    rows=5000,
    seed=42,
)
print(list(tables.keys()))   # ['instructors', 'courses', 'students', 'enrollments', 'quiz_attempts']
print(tables["enrollments"][["completion_pct", "certificate_issued"]].describe())

What Misata generates

Five tables: instructors → courses → enrollments (linking students to courses) → quiz_attempts. Every enrollment references a real student and course; every quiz attempt references a real enrollment.

Tables and columns

Table Key columns
instructors instructor_id, name, email, specialty, rating, courses_taught
courses course_id, instructor_id, title, category, price, difficulty, duration_hours, rating
students student_id, name, email, country, joined_at, total_courses_enrolled
enrollments enrollment_id, student_id, course_id, enrolled_at, completion_pct, completed_at, certificate_issued
quiz_attempts attempt_id, enrollment_id, quiz_name, score, passed, attempted_at

Realistic distributions

  • Enrollment timing peaks in January (New Year learning resolutions) and August-September (back to school)
  • Completion rates follow a realistic dropout curve: ~30% of enrollments reach 100% completion
  • certificate_issued is only true for completed enrollments — conditional logic enforced
  • Quiz scores cluster toward passing (60–85 range) with a realistic fail tail
  • Course prices lognormal — free courses plus premium paid content

Quick start

import misata

tables = misata.generate(
    "An edtech platform with 5k students — back to school surge, New Year spike in programming courses",
    rows=5000,
    seed=42,
)

# Enrollment seasonality
import pandas as pd
enrollments = tables["enrollments"].copy()
enrollments["month"] = pd.to_datetime(enrollments["enrolled_at"]).dt.month
print(enrollments.groupby("month").size())   # spikes in Jan and Sep

# Certificate issuance rate
cert_rate = tables["enrollments"]["certificate_issued"].mean()
print(f"Certificate issuance rate: {cert_rate:.1%}")

# Quiz pass rate by difficulty
merged = tables["quiz_attempts"].merge(
    tables["enrollments"].merge(tables["courses"][["course_id", "difficulty"]], on="course_id"),
    on="enrollment_id"
)
print(merged.groupby("difficulty")["passed"].mean())

Common use cases

  • LMS platform development — seed a test database with instructors, courses, and enrollment histories before your platform has real users
  • Learning analytics dashboards — build completion rate, dropout analysis, and learner progress reports on realistic enrollment data
  • Recommendation engine training — use enrollment and quiz score histories to prototype course recommendation models
  • Revenue and subscription analytics — test cohort revenue, course monetization, and refund rate calculations before real sales data exists
  • Adaptive learning system testing — validate quiz difficulty adaptation logic against thousands of attempts with varied score distributions
  • GDPR-safe student data exports — replace real student records with synthetic equivalents for vendor integrations and compliance audits

Advanced: enrollment narrative curves

tables = misata.generate(
    "Edtech platform with 10k students — January New Year resolution spike, "
    "August back-to-school surge, summer lull in June-July",
    rows=10_000,
    seed=42,
)

Advanced: locale-aware generation

# Indian edtech — Indian student names, INR course pricing, regional subjects
tables = misata.generate("Indian edtech platform with 5k students and coding courses", rows=5000)

# Global MOOC — students from US, UK, India, Nigeria, Brazil
tables = misata.generate("Global online learning platform with 10k students", rows=10_000)

Advanced: quality-guaranteed generation

tables = misata.generate(
    "EdTech platform with 5k students",
    min_quality_score=85,
    smart_correlations=True,  # auto-adds completion_pct↔quiz score correlations
    rows=5000,
    seed=42,
)