os-taxonomy

Product Introduction

1. Definition: OS-Taxonomy is an open-source, structured database and classification system for educational content. It is a technical graph database of learning micro-topics, their prerequisites, and their alignment to formal curriculum standards.
2. Core Value Proposition: It exists to solve the problem of inconsistent and siloed educational data by providing a standardized, machine-readable taxonomy of learning objectives. This enables systematic discovery, comparison, and analysis of educational content and skills, facilitating better learning pathways and product development.

Main Features

1. Fine-Grained Micro-Topic Graph: The core of OS-Taxonomy is its collection of 1,590 "micro-topics"—single, teachable ideas like "Building sentences" or "Apparent brightness of stars." Each node contains a plain-language description, mastery evidence criteria, subject/domain classification, age range, and a type (conceptual, procedural, etc.). This granularity allows for precise mapping of knowledge.
2. Prerequisite Dependency Graph: The taxonomy is not a flat list but a directed acyclic graph (DAG) with 3,221 prerequisite edges. Each dependency links two micro-topics (e.g., "topic X depends on prerequisite Y") and is tagged with a strength ("hard" or "soft") and a reason. This models the inherent structure of learning, enabling the generation of personalized learning sequences and prerequisite analysis.
3. Multi-Curriculum Standard Alignment: Each micro-topic is explicitly linked to source standards from major curricula like NGSS, Common Core, and the UK National Curriculum via keys in data/curriculum-standards.json. This provides traceability and allows educational tools to map their content to multiple regional frameworks simultaneously from a single data source.
4. Structured Data & Validation Scripts: All data is provided as UTF-8 JSON files (topics.json, dependencies.json, etc.) with accompanying JSON Schemas for validation. The repository includes a Node.js validation script (scripts/validate.mjs) to ensure referential integrity and data structure consistency, making integration into applications reliable.
5. Domain Clusters for Human Readability: Alongside the granular graph, the clusters.json file provides 183 parent-friendly, one-paragraph summaries for each subject, domain, and age band combination. This bridges the gap between the detailed technical data and high-level, understandable learning goals.

Problems Solved

1. Pain Point: Inconsistent and ad-hoc categorization of learning objectives across educational platforms, which hinders content discovery, interoperability, and longitudinal skill analysis.
2. Target Audience: EdTech Developers building adaptive learning platforms, curriculum tools, or content libraries; Educational Researchers studying learning progressions or knowledge gaps; Curriculum Designers and Instructional Coordinators mapping standards to resources; Data Scientists in education needing structured training data for ML models.
3. Use Cases: Powering a "skills graph" for a personalized learning app that dynamically generates learning paths; benchmarking and aligning a repository of educational games or videos to a comprehensive skill framework; conducting research on prerequisite chains in science education; building a competency-based assessment system that references a shared ontology of skills.

Unique Advantages

1. Strengths & Limitations (Pros & Cons):

   • Pros:
     • Open & Standardized: Provides a much-needed common language for educational skills, reducing fragmentation.
     • Technically Robust: Graph-based structure with explicit dependencies is far more powerful than tag-based or flat-list taxonomies.
     • Immediately Usable: Clean JSON data with schemas and validation lowers the integration barrier.
     • Commercial-Friendly Licensing (ODbL/CC BY-SA): Allows use in proprietary products, requiring share-alike only for direct modifications to the taxonomy itself.
   • Cons:
     • Subject Scope: Currently focused on primary/elementary years (K-6/7) and core subjects, lacking coverage for secondary, higher-ed, or vocational skills.
     • Authoritativeness: While aligned to standards, the micro-topic definitions and prerequisite edges represent Marble's expert interpretation, which may differ from other interpretations.
     • Static Data: The graph is a versioned snapshot; it does not include real-time collaborative editing or versioning tools within the repo itself.

2. Key Alternatives & Differentiation:

   • Academic Learning Progressions (e.g., CCSS Progressions Documents): These are often PDFs or loosely structured documents, not machine-readable graphs. OS-Taxonomy differentiates by providing the same conceptual insight in a ready-to-query JSON format with explicit prerequisite links.
   • Proprietary Skill Taxonomies (e.g., inside LMS platforms like Canvas or proprietary content libraries): These are locked inside specific products. OS-Taxonomy is completely open-source and portable, allowing any tool to build upon it without vendor lock-in.
   • Schema.org EducationalOccupationalCredential: A broad schema for marking up credentials on the web. OS-Taxonomy is far more granular, focused on the sub-skill level needed for instruction and assessment, not credential representation.

Frequently Asked Questions (FAQ)

1. Can I use OS-Taxonomy in my commercial EdTech product? Yes. The database structure (ODbL 1.0) and Marble's content (CC BY-SA 4.0) are share-alike but distinguish between a derivative database (modifications must stay open) and a produced work (your product that uses the data). You can build a commercial product without open-sourcing your entire codebase.
2. How does OS-Taxonomy handle different countries' curriculum standards? It aligns each micro-topic to multiple source standards (NGSS, UK National Curriculum, etc.) in the curriculum-standards.json file. This allows a single platform to reference a micro-topic and automatically know its correlation to various regional frameworks.
3. What is the difference between a "hard" and "soft" prerequisite in the dependency graph? A "hard" prerequisite indicates a concept that is fundamentally necessary to understand the dependent topic. A "soft" prerequisite suggests a concept that is helpful or commonly taught first but is not strictly essential. This nuance aids in creating more flexible learning pathways.
4. Does OS-Taxonomy include assessments or learning resources? No. OS-Taxonomy is purely a metadata framework—a map of what should be learned and in what order. It defines skills and provides assessment prompts as guidelines, but it does not include quiz questions, video lessons, or other instructional content.
5. How can I contribute or suggest changes to the taxonomy? As an open-source project hosted on GitHub, contributions can be made via the repository's Issue tracker and Pull Request system. The maintainers review suggestions for new micro-topics, prerequisite adjustments, or corrections.