We extract university document metadata, course syllabi, textbook solutions, and tutor Q&A from Course Hero. 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 Document Metadata objects from coursehero.com. All fields typed and schema-versioned.
"doc_id": "84729103", "title": "CS101_Midterm_Review_Notes.pdf", "institution": "Stanford University", "course_code": "CS 101", "department": "Computer Science", "page_count": 14, "doc_type": "Study Guide", "rating": 4.8
| # | doc_id | title | institution | course_code | department | page_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Tutor Q&A objects from coursehero.com. All fields typed and schema-versioned.
"question_id": "q9384712", "subject": "Mathematics", "sub_topic": "Calculus", "question_text": "Find the derivative of f(x) = x^3 * ln(x)", "has_answer": true, "tutor_rating": 5.0, "asked_date": "2023-11-14T08:22:00Z", "tags": "['derivatives', 'product rule']"
| # | question_id | subject | sub_topic | question_text | has_answer | tutor_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Textbook Solutions objects from coursehero.com. All fields typed and schema-versioned.
"isbn_13": "9781305071759", "book_title": "Calculus: Early Transcendentals", "author": "James Stewart", "edition": "8th Edition", "publisher": "Cengage Learning", "chapter_count": 17, "problem_count": 8432, "cover_image_url": "https://example.com/cover.jpg"
| # | isbn_13 | book_title | author | edition | publisher | chapter_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for University Directory objects from coursehero.com. All fields typed and schema-versioned.
"institution_id": "u4821", "name": "University of Michigan", "location": "Ann Arbor, MI", "country": "USA", "department_count": 142, "course_count": 4190, "document_count": 892104, "popular_courses": "['EECS 280', 'STATS 250']"
| # | institution_id | name | location | country | department_count | course_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Course Syllabi objects from coursehero.com. All fields typed and schema-versioned.
"course_id": "c92831", "institution": "New York University", "course_code": "ECON-UA 2", "course_name": "Introduction to Microeconomics", "term": "Fall 2023", "credit_hours": 4, "syllabus_text_preview": "This course introduces the principles of microeconomics...", "related_docs": 142
| # | course_id | institution | course_code | course_name | term | instructor |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Course Hero scraper navigates complex university taxonomies, document viewers, and Q&A pagination. We handle the anti-bot routing and DOM parsing, delivering structured educational data directly to your warehouse.
Extract title, institution, course code, page count, upload date, and document type from millions of user-uploaded study materials.
Map hierarchical relationships across institutions, departments, and specific course codes to build comprehensive academic graphs.
Scrape ISBNs, authors, editions, chapter structures, and problem counts across the entire textbook library.
Extract question text, subject categorisation, tutor ratings, and answer previews from the continuous Q&A feed.
Extract OCR text and HTML text snippets available in the unauthenticated document preview windows.
Standardise subject tags across literature, mathematics, science, and business categories for clean downstream analysis.
Navigate deep pagination and infinite-scroll implementations on university course pages and Q&A archives.
Bypass Cloudflare and strict rate limits using residential proxy rotation and automated CAPTCHA solving.
Track newly uploaded documents and freshly answered questions with hash-based diffing to reduce redundant data.
Brief in. Clean data out.
Provide target institutions, course codes, or subject categories. We map the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for coursehero.com.
Schema validation, null-rate checks, and data normalisation rules are applied before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Course Hero relies heavily on dynamic rendering and strict rate limiting. Here is how we maintain stable extraction at scale.
Course Hero uses strict WAF rules to block datacenter IPs. Our pipeline routes requests through US-based residential ISP proxies with spoofed TLS fingerprints and valid browser headers to maintain access.
Document previews and Q&A answers are often rendered via client-side JavaScript. We deploy Playwright to execute the JS, wait for the DOM to hydrate, and extract the text layers before the blur overlays are fully applied.
Academic data is deeply nested: Country > University > Department > Course > Document. Our Scrapy spiders maintain stateful queues to traverse this hierarchy without missing intermediate nodes.
User-generated content results in inconsistent DOM structures. We use fallback chains (XPath, CSS, Regex) to reliably extract course codes and document types even when users format titles unpredictably.
To track the millions of documents uploaded monthly, we monitor category feeds and only emit new document IDs, reducing compute cost and storage overhead.
AI teams extract Q&A pairs, textbook solutions, and academic reasoning text to train domain-specific educational models.
Competitors analyse document upload velocity across institutions and subjects to identify gaps in their own content libraries.
Researchers aggregate syllabi and course codes to track how specific subjects (e.g., machine learning) are taught across different universities.
Publishers monitor textbook solution databases to track usage, map curriculum adoption, and identify potential copyright infringement.
Content teams analyse high-volume Q&A topics and popular document searches to guide their own educational content creation.
Marketplaces track which subjects have the highest volume of unanswered questions to optimise tutor recruitment.
"Course Hero holds the largest structured repository of university curricula and academic reasoning — highly valuable for LLM training, if you can extract it cleanly."
Extracting academic data requires navigating strict anti-bot measures and highly dynamic document viewers. DataFlirt manages the residential proxies, JavaScript rendering, and schema normalisation so your engineers receive clean text and metadata, not raw HTML.
Everything supported by our coursehero.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 taxonomy traversal and queue management. Playwright executes JavaScript to render document previews and infinite-scroll Q&A feeds.
We route requests through US-based residential IPs with precise rate limiting to avoid triggering Course Hero's aggressive WAF rules.
Pipelines run on Kubernetes for sustained extraction. Airflow manages the complex dependency chains of university-to-course-to-document scraping.
Data delivered to where your team already works — no new tooling required.
About coursehero.com scraping, legality, and pipeline operations.
Ask us directly →No. Full document viewing requires a paid subscription and account unlocks. We only extract publicly available metadata, OCR preview text, and syllabus structures visible without authentication.
We use Playwright to execute the client-side JavaScript, wait for the document container to hydrate, and extract the text nodes before the application applies its blur CSS classes.
Yes. Course Hero enforces a strict taxonomy. Every document and Q&A thread we extract includes the associated institution name, department, and specific course code.
Extracting public, non-authenticated factual data (like university names, course codes, and question text) is generally permissible. We do not bypass authentication walls, use stolen accounts, or extract PII. Clients should consult legal counsel regarding copyright implications if training LLMs on preview text.
For targeted subjects or specific universities, we can run daily or hourly pipelines to capture new tutor questions and answers as they are indexed.
We extract the metadata for textbook solutions, including ISBN, author, chapter structure, problem counts, and available solution previews.
Engagements typically start at a specific data slice — for example, all documents for the top 500 US universities, or all Q&A threads in the mathematics category. Contact us to scope your specific volume requirements.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a complete university taxonomy dump or continuous monitoring of tutor Q&A threads — we scope, build, and operate the pipeline. Tell us what you need.
