extracting-knowledge-items

Curriculum decomposition breaks down syllabi, textbooks, and standards documents into hierarchical atomic knowledge items classified by bloom_level, knowledge_type, and dok_level. This process generates domains, clusters, standards, and leaf items with assessment criteria for Knowledge Space Theory graphs.

Knowledge Space Theory curriculum decomposition is the process of extracting atomic knowledge items and candidate competences from educational artifacts. You need it when building a KST knowledge graph foundation that requires well-classified domains, clusters, and leaf items with assessment criteria.

Yes, knowledge item extraction applies to syllabi, textbooks, standards documents, and curriculum artifacts. It processes these materials to build hierarchical structures from domains down to leaf items, generating CRM-compatible items with taxonomic classifications and ECD-aligned assessment criteria.

Item classification automatically assigns bloom_level, knowledge_type, and dok_level to each extracted atomic knowledge item. This taxonomic classification is applied during curriculum decomposition alongside ECD-aligned assessment criteria generation for every leaf item.

Extracting atomic knowledge items includes mapping candidate latent competences and preparing outputs for concept and competence mapping. This generates graphs in JSON format conforming to the knowledge-graph schema, serving as Phase 1 of the KST pipeline before mapping concepts and competences.

Extracted knowledge items are output as graphs in JSON format conforming to knowledge-graph schema specifications. These files contain domains, clusters, standards, and leaf items with bloom_level, knowledge_type, dok_level classifications, assessment criteria, and candidate competences.