Production-grade, FERPA-aligned engineering guides for building reliable data pipelines across Canvas, Moodle, and Blackboard.
Gradebook · Attendance · Engagement
LMS & EdTech Data Pipelines is a deep, practitioner-focused reference for the engineering and data teams that keep institutional learning platforms in sync. It covers the unglamorous, load-bearing work of turning fragmented learning-management-system exhaust — gradebooks, attendance logs, and engagement telemetry — into reliable, queryable, compliant data assets.
Every page is written for people who ship: Python automation builders, EdTech engineers, institutional data analysts, and academic IT teams. The material is grounded in real production concerns — schema drift, API rate limits, idempotent syncs, identity resolution across systems, and the FERPA boundaries that separate a "working script" from a defensible pipeline. Code examples are runnable Python 3.10+, diagrams are hand-authored, and every example tokenizes student identifiers so the patterns are FERPA-safe by construction.
The site is organized into four in-depth sections, each starting with an architectural overview and drilling down into focused, code-level guides:
- 🗺️ LMS Data Architecture & Schema Mapping — the reference topology from ingestion to serving, a canonical star schema, Canvas / Moodle / Blackboard schema models, cross-LMS identity resolution, CSV export standards, and a dedicated FERPA-compliant PII handling area (salted tokenization, audit-log schema, role-based access).
- 🔄 API Ingestion & Sync Workflows — resilient extraction: Python
requestspatterns, surviving Canvas rate limits, pagination at scale, async grade polling, retry logic with jitter, incremental sync-state management, and webhook-driven event ingestion. - 📊 Gradebook & Attendance Normalization — deterministic engines for weighted grade reconstruction, canonical attendance-state models, and statistical attendance-anomaly detection.
- 📈 Engagement Analytics & Reporting Automation — comparable engagement metrics across platforms, early-warning at-risk scoring, and dashboards and compliance reports that refresh on a schedule.
- 🔐 FERPA-first. Data minimization, a salted-hash tokenization boundary, audit logging, and role-based access are treated as design constraints, not afterthoughts — and every code sample models them.
- 🐍 Runnable, real code. Each guide ships a self-contained, indentation-checked Python script you can adapt, not pseudo-code.
- 🧭 Vendor-specific, not hand-wavy. Exact endpoints, headers, pagination mechanics, and gotchas for Canvas, Moodle, and Blackboard — including the ones that only bite at fleet scale.
- 🖼️ Original diagrams. Every architecture, data-flow, and comparison figure is a hand-authored, accessible, theme-aware inline SVG.
- ➗ Math where it belongs. Grade-weighting and anomaly-detection formulas are rendered with KaTeX, so an analyst and an engineer can agree the pipeline reproduces the platform's own numbers.
- Eleventy (11ty) static-site generator
- Hand-authored inline SVG, KaTeX math, and Prism syntax highlighting
- Installable PWA with an offline-first service worker
- Deployed on Cloudflare
→ Start at www.lms-edtech.org and follow the breadcrumb trails from any topic into its deeper guides.
Engineering reliable, FERPA-aligned LMS data pipelines across Canvas, Moodle, and Blackboard.