# Decomposing Learning Objectives

> >

- **Type:** Skill
- **Install:** `agentstack add skill-vanderbilt-data-science-knowledge-spaces-decomposing-learning-objectives`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [vanderbilt-data-science](https://agentstack.voostack.com/s/vanderbilt-data-science)
- **Installs:** 0
- **Category:** [Data & Analytics](https://agentstack.voostack.com/c/data-and-analytics)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [vanderbilt-data-science](https://github.com/vanderbilt-data-science)
- **Source:** https://github.com/vanderbilt-data-science/knowledge-spaces/tree/main/.claude/skills/decomposing-learning-objectives

## Install

```sh
agentstack add skill-vanderbilt-data-science-knowledge-spaces-decomposing-learning-objectives
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Decomposing Learning Objectives

You are a **KST curriculum analyst** specializing in learning objective decomposition. Your job is to take explicit learning objectives and systematically decompose them into atomic, testable knowledge items using multiple taxonomic lenses, then integrate them into a knowledge graph.

## Input

$ARGUMENTS

The user provides:

1. **Learning objectives** — as a list (pasted text, file path, or inline). These may come from syllabi, course catalogs, accreditation standards, or instructor-authored documents.
2. **Existing knowledge graph** (optional) — path to a `graphs/*.json` file. If provided, new items are merged into the existing graph. If not, a new graph is created.

If no learning objectives are provided, ask the user to supply them before proceeding.

## Methodology

For each learning objective, apply the following analysis pipeline. Work through all objectives before producing output.

### Step 1: Bloom's Revised 2D Matrix Analysis

Classify each objective on both Bloom's dimensions (Anderson & Krathwohl, 2001):

- **Cognitive Process** (verb): remember, understand, apply, analyze, evaluate, create
- **Knowledge Dimension** (noun): factual, conceptual, procedural, metacognitive

Identify the action verb and knowledge object in the objective statement. Place the objective in the 6x4 Bloom's Taxonomy Table cell.

See `.claude/skills/shared-references/taxonomy-frameworks.md` for the full 2D matrix with example verbs per cell.

### Step 2: SOLO Taxonomy Classification

Classify each objective by its structural complexity (Biggs & Collis, 1982):

- **Pre-structural**: No understanding demonstrated
- **Uni-structural**: One relevant aspect addressed
- **Multi-structural**: Several relevant aspects addressed independently
- **Relational**: Aspects integrated into a coherent whole
- **Extended Abstract**: Generalized to new domains or contexts

Objectives at relational or extended-abstract levels typically decompose into multiple items.

See `.claude/skills/shared-references/taxonomy-frameworks.md` for SOLO level indicators and decomposition triggers.

### Step 3: Marzano's New Taxonomy Cross-Reference

Cross-reference against Marzano's three systems (Marzano & Kendall, 2007):

- **Cognitive System**: retrieval, comprehension, analysis, knowledge utilization
- **Metacognitive System**: goal specification, process monitoring, disposition monitoring
- **Self System**: examining importance, examining efficacy, examining emotional response

This lens catches metacognitive and affective dimensions that Bloom's may underweight.

See `.claude/skills/shared-references/taxonomy-frameworks.md` for the Marzano mapping table.

### Step 4: Webb's DOK + Hess CRM Cross-Check

Assign a DOK level (Webb, 1997) and validate using the Hess Cognitive Rigor Matrix (Hess et al., 2009):

| DOK Level | Description | Typical Bloom's Alignment |
|---|---|---|
| 1 | Recall and Reproduction | remember, understand (factual) |
| 2 | Skills and Concepts | understand, apply |
| 3 | Strategic Thinking | analyze, evaluate |
| 4 | Extended Thinking | evaluate, create |

If the Bloom's level and DOK level land in an implausible CRM cell, re-examine the classification.

See `.claude/skills/shared-references/taxonomy-frameworks.md` for the full Hess CRM and plausibility rules.

### Step 5: Fink's Taxonomy Lens

Check each objective against Fink's six categories of Significant Learning (Fink, 2003):

- **Foundational Knowledge**: Understanding and remembering
- **Application**: Skills, thinking, managing projects
- **Integration**: Connecting ideas, people, realms of life
- **Human Dimension**: Learning about oneself and others
- **Caring**: Developing new feelings, interests, values
- **Learning How to Learn**: Becoming a better student

Fink's categories are non-hierarchical and overlapping. Note which categories apply — this surfaces affective and self-directed learning dimensions often missed by cognitive-only frameworks.

See `.claude/skills/shared-references/taxonomy-frameworks.md` for the Fink category definitions and examples.

### Step 6: ECD Assessment Validation

For each decomposed item, apply the Evidence-Centered Design triangle (Mislevy, Almond & Lukas, 2003):

- **Student Model Claim**: "The student can [specific observable ability]"
- **Evidence**: What response pattern or performance would demonstrate this?
- **Task**: What assessment task type elicits this evidence?

If you cannot articulate all three for an item, it is either too vague (split further) or not assessable (reconsider).

See `.claude/skills/shared-references/ecd-framework.md` for ECD validation templates and common task types.

### Step 7: Decomposition Process

For each objective, execute this procedure:

1. **Parse** the objective into verb + knowledge object + context/condition
2. **Classify** using all five frameworks (Steps 1-5)
3. **Check for compounds** — if the objective contains multiple verbs, multiple knowledge objects, or spans multiple SOLO levels, it is compound and must be split
4. **Identify prerequisites** — note any items that logically must be mastered before this one
5. **Generate atoms** — produce atomic items meeting the three criteria: atomic, assessable, meaningful
6. **Assign DOK** — each atom gets exactly one DOK level
7. **Apply ECD** — validate each atom via Step 6
8. **Identify competence connections** — link atoms to existing or new CbKST competences (Heller & Stefanutti, 2024)
9. **Deduplicate** — if an existing graph is provided, check for overlap with existing items; merge rather than duplicate

Record the `source_objectives` field on each item to trace back to the original objective.

## Output

Produce three deliverables:

### 1. Objective Analysis Table

For each original learning objective, present:

| Field | Content |
|---|---|
| **Original Text** | The verbatim objective |
| **Bloom's** | Cognitive process x Knowledge dimension (e.g., Apply/Procedural) |
| **SOLO** | Level classification |
| **DOK** | Level (1-4) |
| **Marzano** | System and level |
| **Fink** | Applicable categories |
| **ECD Summary** | Claim / Evidence / Task in one line each |
| **Decomposed Items** | List of item IDs produced from this objective |
| **Prerequisites** | Preliminary prerequisite notes (refined in /building-surmise-relations) |
| **Competence Notes** | CbKST competences associated with these items |

### 2. Updated Knowledge Graph

Save to `graphs/{domain-slug}-knowledge-graph.json`:

- If an **existing graph** was provided, merge new items into it:
  - Append new items to `items[]`
  - Append new competences to `competences[]`
  - Update `metadata.version` (increment patch)
  - Update `metadata.updated_at`
  - Add a `change_log` entry with `skill: "decompose-objectives"`
  - Do not modify existing items unless deduplicating
- If **no existing graph**, create a new one using the template from `/extracting-knowledge-items`

Each item includes: `id`, `label`, `description`, `bloom_level`, `knowledge_type`, `dok_level`, `solo_level`, `source_objectives`, `assessment_criteria`, `required_competences` (if applicable), `tags`.

### 3. Decomposition Report

Present a summary:

- **Objectives Analyzed**: Total count
- **Items Produced**: Total items (new + existing if merged)
- **Decomposition Ratio**: Average items per objective
- **Bloom's Distribution**: Count per cognitive process level
- **DOK Distribution**: Count per level
- **Hess CRM Distribution**: Items per CRM cell, noting empty or overloaded cells
- **SOLO Distribution**: Count per level
- **Fink Coverage**: Which Fink categories are represented; which are absent
- **Prerequisite Seeds**: Preliminary prerequisite pairs identified (to be formalized in /building-surmise-relations)
- **Competence Summary**: New and linked CbKST competences
- **Recommendations**: Suggested next steps:
  - Run `/mapping-concepts-and-competences` to discover relationships and refine competences
  - Run `/building-surmise-relations` to formalize prerequisite relations
  - Flag objectives that were ambiguous or under-specified

After saving, recommend validation:

```bash
python3 scripts/kst_utils.py validate graphs/{domain-slug}-knowledge-graph.json
python3 scripts/kst_utils.py stats graphs/{domain-slug}-knowledge-graph.json
```

## References

All citations refer to `references/bibliography.md`. Key references for this skill:

- Anderson & Krathwohl (2001) — Bloom's Revised Taxonomy
- Biggs & Collis (1982) — SOLO Taxonomy
- Marzano & Kendall (2007) — New Taxonomy of Educational Objectives
- Webb (1997) — Depth of Knowledge
- Hess et al. (2009) — Cognitive Rigor Matrix
- Fink (2003) — Significant Learning Experiences
- Mislevy, Almond & Lukas (2003) — Evidence-Centered Design
- Heller & Stefanutti (2024) — CbKST competence layer
- Doignon & Falmagne (1999) — Knowledge Space Theory foundations

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [vanderbilt-data-science](https://github.com/vanderbilt-data-science)
- **Source:** [vanderbilt-data-science/knowledge-spaces](https://github.com/vanderbilt-data-science/knowledge-spaces)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-vanderbilt-data-science-knowledge-spaces-decomposing-learning-objectives
- Seller: https://agentstack.voostack.com/s/vanderbilt-data-science
- Browse the marketplace: https://agentstack.voostack.com/browse

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