We extract course listings, module structures, learning outcomes, and educator profiles from The Open University. 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 open.edu. All fields typed and schema-versioned.
"course_id": "DD102", "title": "Introducing the social sciences", "category": "Social Sciences", "study_level": "OU level 1", "credit_value": 60, "duration_hours": 600, "price_gbp": 3462.0
| # | course_id | title | category | sub_category | study_level | credit_value |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for OpenLearn Free Courses objects from open.edu. All fields typed and schema-versioned.
"article_id": "OL-1092", "title": "Introduction to cyber security", "topic": "Digital Computing", "format": "Course", "duration_mins": 1440, "level": "Introductory", "rating": 4.8
| # | article_id | title | topic | format | duration_mins | level |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Module Structures objects from open.edu. All fields typed and schema-versioned.
"module_id": "M1-DD102", "course_id": "DD102", "module_title": "Discovering psychology", "sequence_number": 1, "assessment_type": "TMA", "learning_outcomes": "['Understand basic psychological concepts', 'Evaluate evidence']"
| # | module_id | course_id | module_title | sequence_number | description | learning_outcomes |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Educator Profiles objects from open.edu. All fields typed and schema-versioned.
"educator_id": "ED-582", "name": "Dr. Sarah Jones", "department": "Faculty of Arts and Social Sciences", "role": "Senior Lecturer", "courses_taught": "['DD102', 'DD210']", "research_interests": "['Sociology', 'Digital humanities']"
| # | educator_id | name | department | role | bio | courses_taught |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Subject Categories objects from open.edu. All fields typed and schema-versioned.
"category_id": "CAT-04", "name": "Computing and IT", "parent_category": "STEM", "course_count": 45, "degree_count": 12, "certificate_count": 8
| # | category_id | name | parent_category | description | course_count | degree_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our open.edu scraper handles every layer of the platform: course catalogues, nested module structures, OpenLearn free content, and educator profiles.
Extract titles, study levels, credit values, duration estimates, and pricing data across all undergraduate and postgraduate offerings.
Capture free course materials, article formats, duration estimates, and user ratings from the OpenLearn platform.
Extract nested module structures, sequence numbers, and assessment types linked to parent courses.
Parse unstructured syllabus text into structured arrays of specific learning objectives and skills acquired.
Extract faculty bios, department affiliations, roles, and lists of courses taught by specific educators.
Capture tuition fees, student loan eligibility flags, and regional pricing variations for paid degree paths.
Map the entire subject tree to understand how individual modules roll up into certificates, diplomas, and full degrees.
Identify and log URLs for downloadable PDFs, audio transcripts, and video resources associated with course materials.
Run one-off bulk exports or configure continuous pipelines to track syllabus modifications and price changes over time.
Brief in. Clean data out.
Provide subject URLs, degree paths, or OpenLearn categories. We design the extraction schema together.
We configure Scrapy crawlers, handle pagination, and map nested module structures.
Schema validation, null-rate checks, and syllabus completeness verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting educational data requires traversing complex hierarchies and parsing unstructured text. Here is how we build resilient pipelines.
Open.edu structures courses within multi-level subject categories. Our crawlers recursively traverse these trees to ensure 100% coverage of all modules and degree paths without missing nested items.
A single degree contains multiple modules, which contain multiple learning outcomes. We maintain these relational links during extraction, delivering normalised data that maps parent-child relationships accurately.
Some course materials and interactive previews require JavaScript to render. We use Playwright to execute page scripts and capture data hidden behind accordions or dynamic tabs.
We maintain a hash index of last-seen values for course metadata. Subsequent runs only push diffs, providing a clean changelog of tuition fee adjustments or syllabus modifications.
Educational schemas are prone to missing fields on older courses. Our observability stack monitors null rates for prices, credits, and learning outcomes, alerting us to structural DOM changes instantly.
Online learning platforms benchmark course offerings, module structures, and pricing against The Open University.
Course discovery engines populate their directories with structured OpenLearn free courses and paid degree paths.
Researchers analyse curriculum trends, credit value distributions, and learning outcome terminologies across disciplines.
Learning and development teams map free OpenLearn modules to internal corporate training requirements.
Universities track tuition fee changes and regional pricing variations for online degrees.
Workforce planning platforms correlate extracted learning outcomes with current job market demands.
"The Open University holds one of the most comprehensive digital curricula available, but mapping its nested modules into queryable schemas requires dedicated infrastructure."
Extracting educational data from open.edu involves traversing complex category hierarchies, parsing unstructured syllabi, and handling dynamic course previews. DataFlirt manages the extraction, schema normalisation, and pipeline maintenance so your data engineering teams can focus on integration rather than scraping.
Everything supported by our open.edu 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. Playwright handles JavaScript rendering and interactive syllabus elements.
We maintain pools of residential ISP proxies to bypass basic rate limits and access region-specific pricing data.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management for complex hierarchical crawls.
Data delivered to where your team already works — no new tooling required.
About open.edu scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available educational data from open.edu is generally permissible. DataFlirt targets only public, non-authenticated course catalogues and OpenLearn materials. We do not extract student personal data or circumvent VLE authentication walls.
Yes. We extract metadata, duration, formats, and resource links for all free courses available on the OpenLearn platform.
We extract data hierarchically, maintaining the relational links between a degree, its constituent modules, and the specific learning outcomes for each module. This is delivered as nested JSON or relational Postgres tables.
Yes. We maintain a hash index of previous runs and can emit diffs when tuition fees or regional prices change.
Full catalogue refreshes typically run weekly or monthly depending on your requirements, capturing all new courses and syllabus modifications.
Engagements start at a defined extraction scope, such as all OpenLearn courses or a specific faculty's degree paths. We price based on volume and delivery frequency.
Yes. We provide a sample run of up to 100 courses or modules during the scoping phase to validate schema fit and data completeness.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous feed of syllabus updates, we scope, build, and operate the pipeline. Tell us what you need.
