Assessment simulation
The model separates present-student academic scores from attendance and non-participation, so observed zeros are treated as administrative outcomes rather than readiness evidence.
Project Brief
Education Data Simulation Engine
Generates a coherent seven-year synthetic mathematics department, validates cross-table invariants, builds Canvas-style records and a DuckDB star schema, and exports marts consumed by Assessment Intelligence.
Overview
Public-safe simulation foundation
The deterministic synthetic education-data foundation connects a nested school structure, Canvas-style records, longitudinal assessment data, and a local analytics warehouse.
Validation reconciles enrollment, assessment, non-participation, and LMS-style records before downstream reporting.
Current Artifacts
- Canonical synthetic school state JSON
- All-school math assessment gradebook CSV
- Course, section, and enrollment exports
- Canvas-style course profile JSON files
- DuckDB SQL warehouse and star-schema marts
- LMS-to-SQL roster reconciliation outputs
- SHA-256-verified README and data-lineage documentation
- Verified validation summary and Makefile
Statistical Design
What this project proves
Longitudinal foundation
The active seven-year synthetic math-department foundation connects student, course, section, enrollment, assessment, attendance, and LMS-style records.
Validation boundary
The validation layer reconciles enrollment, assessment, non-participation, and LMS-style records before downstream reporting.
SQL analytics layer
The DuckDB warehouse normalizes Canvas-like JSON into raw LMS tables, reconciles rosters against canonical enrollments, and exports star-schema facts and dimensions for downstream reporting.
Relationship
Feeds the assessment portfolio
education-data-simulation-engine is the simulation, validation, and SQL warehouse foundation. assessment-intelligence is the analytics and reporting layer that consumes SQL-backed extracts for dashboards, diagnostics, reports, and decision-support workflows.
Current Scope
- Active seven-year longitudinal synthetic math-department simulation
- Student, course, section, enrollment, assessment, and attendance records
- Synthetic Canvas API-style profiles and SQL extraction
- LMS-to-SQL roster reconciliation
- DuckDB analytics marts and a star-schema reporting model
- Validation checks across the public-safe data workflow
Projects Qualification
Supporting project ยท qualified claims
Strong supporting architecture evidence and the privacy foundation for Assessment Intelligence; the local commit is demo-ready, but public featuring must wait for separately approved remote release synchronization.
Safety Boundary
Synthetic by design
Public artifacts may include fake identifiers, synthetic enrollments, synthetic assignment scores, generalized calibration parameters, and public-safe aggregate diagnostics. They must not include real students, rosters, LMS exports, private assessment artifacts, private teacher names, internal section labels, private paths, or credentials.
Warehouse Outputs
What downstream tools can consume
Analytic marts
Exports include assessment facts, LMS enrollment facts, readiness, growth, missingness, roster reconciliation, teacher-section effects, and validation summaries.
Star schema
The SQL layer provides student, course, section, teacher, assignment, assessment-score, and LMS-enrollment dimensions and facts.