LearningNAV Contributor Guide

1. Become a Contributor

Anyone can become a LearningNAV contributor. To get started, register for an account on the platform. Once registered, you can immediately begin exploring the knowledge graph, creating learning paths, and participating in the community.

There are no prerequisites or formal qualifications required — we welcome contributors of all backgrounds and experience levels. What matters most is your willingness to contribute accurate, well-sourced knowledge and engage constructively with the community.

2. Create Learning Paths

Learning paths are structured sequences of knowledge nodes that guide learners from foundational concepts to advanced topics. Creating a high-quality learning path involves:

1. Choose a Topic — Select a domain or subject area you are knowledgeable about.
2. Define Prerequisites — Identify the prerequisite knowledge learners need before starting your path.
3. Structure the Sequence — Arrange knowledge nodes in a logical progression, with each step building on the previous one.
4. Add Descriptions — Write clear, concise descriptions for each node in the path, explaining what learners will gain.
5. Submit for Review — Once your path is complete, submit it for community review. Reviewers will provide feedback and help ensure quality standards are met.

Use the Path Builder tool at /community/paths/create to create and manage your learning paths.

3. Submit Knowledge Nodes

Knowledge nodes are the fundamental building blocks of the LearningNAV knowledge graph. Each node represents a distinct concept, topic, or entity with a unique identifier, clear definition, and verifiable sources.

When submitting a new knowledge node, ensure it meets these criteria:

• Unique and Non-Duplicate — Check that the concept does not already exist in the knowledge graph.
• Clear Definition — Provide a concise, accurate description of the concept.
• Proper Relationships — Define how the node relates to existing nodes (e.g., parent concepts, related topics, prerequisites).
• Verifiable Sources — Include citations and references that support the information in the node.
• Stable Identifier — Use the established naming convention for node IDs.

4. Review Community Contributions

Community review is essential to maintaining the quality and integrity of the LearningNAV knowledge graph. Reviewers examine submitted learning paths and knowledge nodes, provide constructive feedback, and decide whether contributions meet the quality standards for approval.

The review process involves:

1. Check Accuracy — Verify that the content is factually correct and well-sourced.
2. Check Completeness — Ensure the contribution includes all required elements (descriptions, relationships, citations).
3. Check Consistency — Confirm the contribution aligns with existing knowledge graph structure and naming conventions.
4. Provide Feedback — If issues are found, provide clear, actionable feedback to help the contributor improve their submission.
5. Approve or Reject — Based on the review, approve the contribution or reject it with an explanation.

Access the review dashboard at /admin/community-paths/review.

5. Contribution Principles

All contributions to LearningNAV should follow these core principles:

• Accuracy — Contributions must be factually correct and based on verifiable sources. Speculation and personal opinion should be clearly distinguished from established knowledge.
• Neutrality — Present information in a balanced, neutral manner. Avoid promotional content, advocacy, or biased framing.
• Openness — All contributions are made available under open licenses (CC BY-SA 4.0), ensuring they remain freely accessible to everyone.
• Respect — Engage with fellow contributors respectfully and constructively. Disagreements should be resolved through discussion and community consensus.
• Transparency — All contributions, reviews, and decisions are publicly recorded and attributable, ensuring accountability.

6. Quality Standards

LearningNAV maintains high quality standards to ensure the knowledge graph is reliable and trustworthy. All contributions should meet the following standards:

• Well-Defined Nodes — Every knowledge node must have a clear, unambiguous definition and stable identifier.
• Complete Metadata — Nodes must include proper categorization, relationships, and source citations.
• Logical Path Structure — Learning paths must present topics in a coherent, pedagogically sound sequence.
• Up-to-Date Information — Contributions should reflect current knowledge. Outdated information should be flagged for revision.
• Accessible Language — Content should be written in clear, accessible language appropriate for the target audience.
• Proper Attribution — All sources must be properly cited, and collaborative contributions should acknowledge all contributors.

Contributions that do not meet these standards may be returned to the contributor for revision or, in cases of persistent quality issues, rejected. The goal of our quality standards is not to create barriers but to ensure that LearningNAV remains a trustworthy and valuable resource for learners worldwide.