We extract lesson libraries, author profiles, educational standards, and interactive content metadata from Nearpod. 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 Lesson Metadata objects from nearpod.com. All fields typed and schema-versioned.
"lesson_id": "np-847291", "title": "Photosynthesis and Cellular Respiration", "author_name": "Science Made Fun", "grade_levels": "['8th', '9th', '10th']", "subjects": "['Science', 'Biology']", "duration_minutes": 45, "thumbnail_url": "https://nearpod.com/assets/images/np-847291-thumb.jpg", "created_at": "2023-08-14T10:00:00Z"
| # | lesson_id | title | description | author_name | author_id | grade_levels |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Educational Standards objects from nearpod.com. All fields typed and schema-versioned.
"lesson_id": "np-847291", "standard_body": "NGSS", "standard_code": "HS-LS1-5", "standard_description": "Use a model to illustrate how photosynthesis transforms light energy into stored chemical energy.", "grade": "High School", "subject": "Life Sciences"
| # | lesson_id | standard_body | standard_code | standard_description | grade | subject |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Author Profiles objects from nearpod.com. All fields typed and schema-versioned.
"author_id": "pub-39281", "name": "Science Made Fun", "verified_publisher": true, "total_lessons": 142, "total_views": 850400, "subjects_taught": "['Science', 'STEM']", "profile_url": "https://nearpod.com/authors/pub-39281"
| # | author_id | name | bio | verified_publisher | total_lessons | total_views |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Interactive Elements objects from nearpod.com. All fields typed and schema-versioned.
"lesson_id": "np-847291", "element_type": "Time to Climb", "slide_index": 12, "question_text": "What is the primary product of photosynthesis?", "options": "['Glucose', 'Oxygen', 'Water', 'Carbon Dioxide']", "has_media": true, "required": true
| # | lesson_id | element_type | slide_index | question_text | options | has_media |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search & Discovery objects from nearpod.com. All fields typed and schema-versioned.
"keyword": "fractions", "category": "Math", "position": 1, "lesson_id": "np-112093", "title": "Introduction to Equivalent Fractions", "featured_badge": true, "scraped_at": "2023-10-14T08:30:00Z"
| # | keyword | category | position | lesson_id | title | match_score |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Nearpod pipeline handles dynamic single-page applications, standard alignment matrices, and complex interactive element metadata. We deliver clean curriculum datasets ready for analysis.
Capture titles, descriptions, grade levels, subjects, duration estimates, and resource types for every public lesson in the catalogue.
Extract curriculum alignments including Common Core, NGSS, and state-specific standards linked to individual lessons.
Track publisher output, verified status, total lesson counts, and subject specialisations across the platform.
Log the presence of quizzes, polls, VR field trips, Draw It activities, and Time to Climb elements within lesson structures.
Monitor lesson visibility for specific educational keywords, tracking organic position and featured placement.
Map Nearpod's internal categorisation system, capturing subject hierarchies and grade-band distributions.
Identify underserved subjects and grade levels by cross-referencing lesson availability against standard curriculum requirements.
Run daily or weekly pipelines that only extract new or modified lessons, reducing data processing overhead.
Extract region-specific content libraries and localised standard alignments where available.
Brief in. Clean data out.
Provide subject areas, grade levels, publisher IDs, or keyword lists. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management for nearpod.com.
Schema validation, null-rate checks, alignment verification, and sample datasets before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Modern educational platforms rely on heavy JavaScript and dynamic loading. Here is how we extract structured data from Nearpod without missing nested content.
Nearpod is a single-page application heavily reliant on React. We run full Playwright browser sessions to execute JavaScript, trigger lazy loading, and hydrate lesson metadata that headless HTTP clients cannot access.
Educational standards and interactive elements exist in deeply nested JSON structures within the page state. Our parsers extract and normalise this nested data into flat, queryable formats suitable for relational databases.
Publisher libraries and search results often use infinite scroll or complex pagination tokens. We build resilient traversal logic to ensure complete capture of large lesson catalogues without dropping records.
We route requests through ISP-grade residential proxies and spoof TLS fingerprints to maintain high success rates and avoid rate limiting during bulk curriculum extraction.
EdTech platforms frequently update their UI. Our selector strategy uses multiple fallback chains, including internal API interception, to ensure pipeline continuity even when the DOM changes.
Competing platforms monitor Nearpod's lesson catalogue to identify feature trends, popular subjects, and top-performing interactive formats.
Instructional designers analyse standard alignment coverage to identify gaps in the market and develop targeted educational content.
Investors and analysts track publisher growth, lesson volume, and category expansion to evaluate the digital curriculum market.
Machine learning teams use structured lesson metadata and standard alignments to train educational recommendation engines and content classifiers.
Educational publishers track their own content placement, search visibility, and catalogue completeness across the platform.
Researchers analyse the distribution of interactive elements and VR usage across different grade levels to study digital pedagogy trends.
"Nearpod houses one of the largest interactive curriculum libraries online, but standardising that metadata requires a purpose-built extraction pipeline."
Extracting educational content requires navigating heavy JavaScript applications and complex standard alignment schemas. DataFlirt handles the rendering, session management, and normalisation so your data science teams can focus on curriculum analysis.
Everything supported by our nearpod.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 manages crawl logic, deduplication, and scheduling. Playwright executes JavaScript and intercepts internal API calls to capture structured lesson data.
We utilise ISP-grade residential proxies to distribute requests geographically, ensuring reliable access to the Nearpod catalogue without triggering rate limits.
Pipelines run on containerised AWS infrastructure. Airflow handles job dependencies and scheduling, while Prometheus monitors pipeline health metrics.
Data delivered to where your team already works — no new tooling required.
About nearpod.com scraping, legality, and pipeline operations.
Ask us directly →We extract publicly available lesson metadata, author profiles, educational standard alignments, subject categorisations, and interactive element indicators. We do not extract private lessons or student data.
Yes. If Nearpod displays standard alignments for a lesson, our pipeline captures the standard body, code, and description, mapping it directly to the lesson ID.
We extract metadata about the slides, such as the presence of quizzes, VR elements, or Draw It activities. We do not extract the proprietary media files or full text of copyright-protected slides.
Our infrastructure uses Playwright to execute JavaScript, triggering necessary lazy-loading events and intercepting internal API responses to capture complete datasets.
Yes. We can configure targeted pipelines to monitor specific publisher IDs, tracking search visibility, total lesson counts, and categorisation accuracy.
We deliver data in JSON, CSV, or Parquet formats, pushed directly to your S3 bucket, Google Cloud Storage, BigQuery, Snowflake, or via Webhook.
Pipelines can be scheduled daily, weekly, or monthly depending on your requirements. We use change detection to only deliver new or modified lesson records.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a complete catalogue extraction or targeted standard alignment monitoring, we build and operate the infrastructure. Tell us your curriculum data requirements.
