We extract course catalogues, institution profiles, syllabus structures, pricing, and educator metadata from OpenLearning. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake 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 openlearning.com. All fields typed and schema-versioned.
"course_id": "crs_8921", "title": "Introduction to Cyber Security", "institution": "UNSW", "price": 450.0, "currency": "AUD", "level": "Beginner", "rating": 4.7, "enrollment_count": 12450
| # | course_id | url | title | institution | educator | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Syllabus & Modules objects from openlearning.com. All fields typed and schema-versioned.
"course_id": "crs_8921", "module_id": "mod_01", "module_title": "Network Fundamentals", "lesson_count": 5, "assessment_type": "Quiz", "video_hours": 2.5, "prerequisites": "None"
| # | course_id | module_id | module_title | module_duration | lesson_count | topics |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Institution Data objects from openlearning.com. All fields typed and schema-versioned.
"institution_id": "inst_unsw", "name": "University of New South Wales", "location": "Sydney, Australia", "course_count": 142, "student_count": 85000, "website": "unsw.edu.au", "established_year": 1949
| # | institution_id | name | url | location | course_count | student_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from openlearning.com. All fields typed and schema-versioned.
"review_id": "rev_99281", "course_id": "crs_8921", "reviewer_name": "Alex M.", "rating": 5, "review_text": "Excellent primer on network security.", "date_posted": "2023-11-14", "helpful_votes": 24
| # | review_id | course_id | reviewer_name | rating | review_text | date_posted |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Educator Profiles objects from openlearning.com. All fields typed and schema-versioned.
"educator_id": "edu_4412", "name": "Dr. Sarah Jenkins", "institution": "UNSW", "courses_taught": 4, "total_students": 32014, "average_rating": 4.8, "join_date": "2019-03-12"
| # | educator_id | name | institution | bio | courses_taught | total_students |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our OpenLearning scraper handles dynamic content loading, pagination, and complex syllabus hierarchies to deliver structured educational data.
Title, description, category, language, duration, level, and enrollment counts scraped at the individual course level.
Extract module structures, lesson counts, video hour estimates, and learning outcomes mapped directly to the parent course.
Capture university and corporate profiles, total student metrics, active course counts, and verified credentials.
Monitor course fees across currencies, discount events, and historical enrollment velocity over time.
Full review text, star ratings, and helpful vote counts paginated across all course review pages.
Extract instructor biographies, qualification tags, total students taught, and aggregate ratings across their course portfolio.
Map the entire OpenLearning category tree to understand subject density and trending topics.
Identify courses offering formal certifications, university credit, or digital badges upon completion.
Run one-off bulk exports or configure continuous pipelines at weekly or monthly cadences with change-detection diffing.
Brief in. Clean data out.
Provide category URLs, institution IDs, or keyword sets. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for openlearning.com.
Schema validation, null-rate checks, and sample syllabus extraction before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting structured data from modern EdTech platforms requires handling dynamic React components and complex pagination. Here is how we build it.
We route requests through residential proxies to distribute load and avoid IP rate limits when scraping large course catalogues.
OpenLearning relies heavily on client-side rendering. We run full Playwright browser sessions to hydrate course pages and extract dynamic pricing components.
EdTech platforms frequently update their UI. Our selector strategy uses fallback chains - CSS selectors, XPath, and JSON-LD extraction - to maintain data integrity.
For ongoing monitoring, we maintain a hash index of last-seen values per course. Subsequent runs only push diffs, reducing downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes and schema drift, responding before you notice.
Online learning platforms monitor OpenLearning course catalogues, pricing models, and syllabus structures to benchmark their own offerings.
HR and L&D teams aggregate course metadata to build internal training portals mapped to specific employee skill gaps.
Analysts track enrollment velocity across categories to identify trending skills and subject matter demand.
Machine learning teams use structured syllabus data and learning outcomes to train educational recommendation engines.
Universities and competing platforms identify highly-rated instructors based on review sentiment and student volume.
Course creators analyse competitor pricing tiers, discount frequency, and micro-credential fees to optimise their own pricing.
"OpenLearning contains a massive repository of social learning data and micro-credentials, but extracting structured syllabus and enrollment metrics requires a dedicated pipeline."
Most teams underestimate the investment required: reliable OpenLearning scraping requires handling dynamic React components, pagination logic, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our openlearning.com 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, deduplication, and retry logic. Playwright handles JavaScript rendering and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies to distribute request load and prevent rate-limiting during large catalogue extractions.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About openlearning.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from OpenLearning is generally permissible under applicable law. DataFlirt targets only public, non-authenticated course catalogues, syllabus data, and institution profiles. We do not extract personal student data or circumvent authentication walls.
We use full Playwright browser sessions to render client-side JavaScript, ensuring we capture pricing widgets, dynamic syllabus expansions, and paginated review sections accurately.
Yes. We map the parent-child relationship between courses, modules, and individual lessons, outputting a nested JSON structure or relational CSV tables.
Full catalogue refreshes at weekly or monthly cadences complete within a 6-12 hour window. Targeted category pipelines can run daily for pricing and enrollment monitoring.
Our smallest packages start at a defined category list or institution set with weekly delivery. For full-platform extraction, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 courses as part of the pre-engagement scoping process so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous course monitoring - we scope, build, and operate the pipeline. Tell us what you need.
