We extract skill hierarchies, subject taxonomies, Common Core alignments, and curriculum metadata from IXL. 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 Skill Hierarchies objects from ixl.com. All fields typed and schema-versioned.
"skill_id": "M_8_A_1", "skill_name": "Identify linear and nonlinear functions", "subject": "Math", "grade": "8th grade", "category": "Functions", "difficulty_level": "intermediate", "url": "https://www.ixl.com/math/grade-8/identify-linear-and-nonlinear-functions"
| # | skill_id | skill_name | subject | grade | category | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Standard Alignments objects from ixl.com. All fields typed and schema-versioned.
"alignment_id": "CCSS_M_8_F_A_1", "state": "National", "standard_set": "Common Core State Standards", "grade": "8", "subject": "Math", "standard_code": "8.F.A.1", "standard_description": "Understand that a function is a rule that assigns to each input exactly one output."
| # | alignment_id | state | standard_set | grade | subject | standard_code |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Subject Taxonomies objects from ixl.com. All fields typed and schema-versioned.
"subject_id": "math", "subject_name": "Math", "total_skills": 4582, "grades_covered": "['Pre-K', 'K', '1', '2', '3', '4', '5', '6', '7', '8', 'Algebra 1', 'Geometry', 'Algebra 2', 'Precalculus', 'Calculus']", "curriculum_url": "https://www.ixl.com/math", "update_timestamp": "2023-10-12T08:14:00Z"
| # | subject_id | subject_name | total_skills | grades_covered | categories | subcategories |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Grade Levels objects from ixl.com. All fields typed and schema-versioned.
"grade_id": "grade-8", "grade_name": "8th grade", "subjects_available": "['Math', 'Language arts', 'Science', 'Social studies']", "total_skills": 1245, "typical_age_range": "13-14", "grade_url": "https://www.ixl.com/promo/grade-8"
| # | grade_id | grade_name | subjects_available | total_skills | total_topics | typical_age_range |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Topic Classifications objects from ixl.com. All fields typed and schema-versioned.
"topic_id": "algebraic-expressions", "topic_name": "Algebraic expressions", "subject": "Math", "grade_span": "6-12", "skill_count": 48, "related_topics": "['Equations', 'Variables', 'Functions']", "topic_url": "https://www.ixl.com/math/topics/algebraic-expressions"
| # | topic_id | topic_name | subject | grade_span | related_topics | skill_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our IXL scraper handles the complex taxonomy of educational content: subjects, grades, topics, and skill mappings — with JavaScript rendering for dynamic content and standard alignment tables.
Extract math, language arts, science, social studies, and Spanish hierarchies into a relational database.
Capture skill progression from Pre-K through 12th grade, including advanced high school topics.
Scrape skill IDs, names, descriptions, and category groupings with parent-child relationships intact.
Map IXL skills directly to Common Core standard codes and descriptions across all grades.
Extract alignments for all 50 US states and international curricula, capturing specific regional codes.
Extract publicly visible sample questions and visual assets associated with specific skills.
Scrape skill mappings to popular textbook series and individual chapters.
Execute JavaScript to render complex math formulas and interactive element previews during extraction.
Run continuous pipelines to detect curriculum additions or standard alignment changes.
Brief in. Clean data out.
Provide target subjects, grades, or standard sets. We design the extraction schema together.
We configure Scrapy / Playwright crawlers and session management for ixl.com.
Schema validation, taxonomy structure checks, and standard mapping verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Educational taxonomies are deeply nested and JavaScript-heavy. Here is how we extract clean, relational data.
Educational hierarchies are complex graphs. We traverse subjects, grades, categories, and skills, maintaining parent-child relationships via foreign keys in the output data.
IXL uses complex JS to render math formulas and interactive questions. We run full Playwright sessions to capture the evaluated DOM, ensuring equations extract cleanly.
Standard alignments are presented in dynamic, tabbed interfaces. Our crawlers simulate user interaction to expose and extract mappings for every state and standard set.
Educational platforms update UI structures before new school years. Our selectors use multiple fallback chains — CSS, XPath, and text-pattern matching — to survive layout changes.
We maintain a hash index of last-seen values per skill. Subsequent runs only push diffs, providing a clean changelog of curriculum updates rather than full re-dumps.
EdTech platforms monitor IXL's skill taxonomy and curriculum coverage to benchmark their own content offerings.
School districts and educational publishers map their proprietary content against IXL's comprehensive skill database.
Policy analysts and curriculum developers track how digital skills align with evolving state and Common Core standards.
ML teams use structured skill hierarchies and standard mappings to train educational recommendation engines and LLM tutors.
Publishers analyze IXL's textbook alignment data to understand digital supplement usage alongside physical materials.
Investors and product managers identify underserved grades or subjects within the digital curriculum landscape.
"IXL's taxonomy is a masterclass in curriculum mapping — but turning that web interface into a queryable relational database requires precise, hierarchical extraction."
Most teams fail at scraping educational platforms because they treat them like flat directories. Extracting IXL requires maintaining deep parent-child relationships across subjects, grades, topics, and standards, while rendering complex math formulas via JavaScript. DataFlirt handles the graph traversal and state management so you get clean, relational curriculum data.
Everything supported by our ixl.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 and deduplication. Playwright handles JavaScript rendering for math formulas and dynamic standard tables.
Custom middleware manages deep hierarchical crawling, ensuring referential integrity between grades, subjects, topics, and skills in the final dataset.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. State stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About ixl.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available curriculum taxonomies, skill names, and standard alignments is generally permissible under applicable law. DataFlirt targets only public, non-authenticated data. We do not extract student PII or authenticated dashboard data.
No. Student progress, diagnostic scores, and teacher dashboards are gated behind authentication and contain PII. We strictly extract public curriculum structure and standard alignments.
We use full Playwright browser sessions to execute IXL's rendering JavaScript, capturing the evaluated DOM and extracting formulas as clean text or LaTeX equivalents where possible.
Yes. We extract the exact standard codes, descriptions, and their corresponding IXL skill mappings for all 50 US states and supported international curricula.
Our extraction schema is relational. Every skill record includes foreign keys mapping it back to its parent topic, category, grade, and subject, ensuring the taxonomy graph remains intact.
Educational platforms typically push major curriculum updates over the summer, with minor adjustments throughout the year. We recommend a weekly or monthly change-detection pipeline for continuous monitoring.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off export of state standard alignments or a continuous monitor of IXL's skill taxonomy — we scope, build, and operate the pipeline. Tell us what you need.
