decomposing-learning-objectives

You can decompose learning objectives by applying multiple taxonomies and ECD validation to break broad goals into atomic, testable knowledge items. This produces assessable items with source tracing and prerequisite linkage for scalable assessment design.

Learning objective decomposition applies Bloom's Revised, SOLO, Marzano, Webb's DOK, Hess Cognitive Rigor Matrix, and Fink's Significant Learning lenses. These multiple taxonomies validate the cognitive complexity and assessability of each generated knowledge item.

Yes, you can supply an existing knowledge graph to merge newly decomposed knowledge items into it. The output produces a structured item set with source_objectives references, assessment criteria, and competence links ready for integration and analysis.

Generating atomic knowledge items from syllabi and accreditation standards requires providing explicit learning objectives as input. The decomposition process outputs structured items with assessment criteria and prerequisite linkage for adaptive instruction planning.

Learning objective decomposition supports automatic assessment item generation by producing atomic, assessable knowledge items validated through ECD. The structured output includes assessment criteria and competence links needed for generating test questions.