We extract course metadata, syllabus structures, unit learning outcomes, and open educational resource links from Saylor.org. Delivered as clean JSON, CSV, or Parquet to S3 or Postgres on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Course Metadata objects from saylor.org. All fields typed and schema-versioned.
"course_id": "CS101", "title": "Introduction to Computer Science", "subject_area": "Computer Science", "estimated_hours": 45.5, "credit_eligible": true, "certificate_available": true
| # | course_id | title | subject_area | difficulty_level | estimated_hours | credit_eligible |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Syllabus Units objects from saylor.org. All fields typed and schema-versioned.
"course_id": "CS101", "unit_number": 1, "unit_title": "Introduction to Algorithms", "learning_outcomes": "['Define an algorithm', 'Explain basic data structures']", "assessment_type": "Quiz", "estimated_time_hours": 4.0
| # | course_id | unit_number | unit_title | learning_outcomes | reading_links | video_links |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Credit Partnerships objects from saylor.org. All fields typed and schema-versioned.
"partner_name": "Thomas Edison State University", "partner_type": "University", "credit_hours": 3, "ACE_recommendation": true, "NCCRS_recommendation": false, "partner_url": "https://www.tesu.edu/"
| # | partner_name | partner_type | accepted_courses | credit_hours | ACE_recommendation | NCCRS_recommendation |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Learning Resources objects from saylor.org. All fields typed and schema-versioned.
"resource_id": "RES-8492", "course_id": "CS101", "resource_type": "Textbook Chapter", "title": "Think Python: How to Think Like a Computer Scientist", "author": "Allen B. Downey", "license_type": "CC BY-NC 3.0"
| # | resource_id | course_id | unit_id | resource_type | title | author |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Subject Categories objects from saylor.org. All fields typed and schema-versioned.
"category_id": "CAT-CS", "category_name": "Computer Science", "course_count": 14, "total_credits_available": 42, "parent_category": "STEM", "page_url": "https://learn.saylor.org/course/index.php?categoryid=9"
| # | category_id | category_name | course_count | total_credits_available | description | parent_category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Saylor scraper handles the Moodle based architecture: extracting nested syllabi, validating external Open Educational Resource links, and mapping credit transfer recommendations into clean relational tables.
Extract titles, descriptions, difficulty levels, and estimated completion times across all academic subjects.
Parse nested syllabus structures into relational tables mapping units, sub-topics, and required reading materials.
Capture and validate external Open Educational Resource URLs, flagging dead links and redirects.
Map ACE and NCCRS credit recommendations alongside university partner transfer requirements.
Extract specific learning objectives per unit, formatting them as clean arrays for LMS ingestion.
Capture estimated study hours per unit and aggregate them for total course duration metrics.
Identify which courses offer free certificates versus proctored credit exams.
Extract the full category tree, mapping individual courses to their parent disciplines and sub-fields.
Run pipelines weekly or monthly to capture new course additions, syllabus revisions, and updated partner lists.
Brief in. Clean data out.
Provide target subjects, specific course IDs, or request the full catalogue. We design the extraction schema together.
We configure Scrapy crawlers to navigate Saylor's Moodle DOM, parsing nested units and validating resource links.
Schema validation, null-rate checks, and syllabus hierarchy verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket or Postgres database on agreed cadence.
Extracting structured data from educational platforms requires handling inconsistent formatting and nested hierarchies. Here is how we manage it.
Saylor runs on a customised Moodle instance. Our selectors are built to traverse deeply nested HTML lists and tables, accurately mapping parent units to child topics without losing context.
Saylor relies heavily on external Open Educational Resources. We ping external resource URLs during the crawl to capture HTTP status codes, allowing you to filter out 404s before displaying them to your users.
Syllabi are often formatted inconsistently depending on the course author. We use fallback chains and regex patterns to normalise estimated hours, reading assignments, and learning outcomes into a strict schema.
For full catalogue monitoring, we maintain a hash index of last-seen values per course. Subsequent runs only push diffs, reducing downstream processing load. You get a clean changelog of syllabus updates.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, missing syllabus sections, and coverage drops, fixing selector drift before your next scheduled delivery.
Corporate training platforms and universities ingest Saylor syllabi to supplement their own course catalogues with free OER materials.
Course search engines index Saylor metadata alongside Coursera and edX to build comprehensive directories of free online education.
Academic advisors and policy analysts map ACE and NCCRS credit recommendations to track alternative pathways to degree completion.
Machine learning teams use structured syllabi and learning outcomes to train educational LLMs and automated tutoring systems.
Instructional designers analyse Saylor's course structures to benchmark their own curriculum against established open standards.
Libraries and academic institutions extract Saylor's reading lists to build searchable databases of open-source textbooks and articles.
"Saylor Academy provides a wealth of open educational resources, but integrating their unstructured course catalogues into external learning management systems requires dedicated parsing infrastructure."
Most teams underestimate the complexity of extracting educational data. Syllabi are often inconsistently formatted, external reading links rot, and Moodle DOM structures change without warning. DataFlirt manages these extraction layers so your engineering team can focus on building your EdTech product, not maintaining scrapers.
Everything supported by our saylor.org scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and deduplication across the Moodle instance. Playwright handles any dynamic JavaScript rendering required for specific interactive course elements.
We maintain pools of residential and datacenter proxies. Rotation happens per-request to ensure stable extraction without triggering rate limits on educational servers.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About saylor.org scraping, legality, and pipeline operations.
Ask us directly →Saylor Academy operates under an Open Educational Resources (OER) model, and much of its content is licensed under Creative Commons. DataFlirt extracts only public, non-authenticated course metadata, syllabi, and resource links. We do not extract student PII or circumvent authentication walls. Clients should review Saylor's Terms of Use and specific CC licenses for their intended application.
We capture the target URL for all external reading assignments and perform a lightweight HTTP HEAD request during the crawl. The resulting status code (e.g., 200, 404, 301) is included in the payload, allowing you to filter out broken links before ingesting them into your LMS.
Yes. We parse the Moodle DOM to maintain the parent-child relationships between courses, units, sub-topics, and individual learning outcomes. This is delivered as a nested JSON structure or normalised relational tables.
Educational content changes less frequently than eCommerce pricing. Most clients configure weekly or monthly pipeline runs to capture new course additions, syllabus revisions, and updated credit transfer partnerships.
No. We do not extract proctored exam questions, answer keys, or any materials that would violate academic integrity policies or require student authentication.
Yes. We extract the specific American Council on Education (ACE) and National College Credit Recommendation Service (NCCRS) details for eligible courses, including the recommended credit hours and partner university transfer requirements.
Our packages scale based on delivery frequency and schema complexity. For Saylor, a standard engagement includes full catalogue extraction (metadata and syllabi) delivered weekly to your specified sink. Contact us for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off syllabus export or continuous monitoring of OER updates across the catalogue, we scope, build, and operate the pipeline. Tell us what you need.
