Course Description Quality Assessment
Skill: course-description-analyzer v0.03
File assessed: docs/course-description.md (version prior to rewrite)
1. Overall Score
17 / 100
2. Quality Rating
0–39: Poor — Major revision required
The original description read well as marketing copy for the course, but it was missing every structural element a learning-graph generator needs to reliably produce 200+ well-scoped concepts: no itemized topic list, no excluded-topics boundary, and no Bloom's Taxonomy outcomes at all.
3. Detailed Scoring Breakdown
| Element | Points Possible | Points Earned | Notes |
|---|---|---|---|
| Title | 5 | 5 | Clear and descriptive: "Learning Graphs: The Key to Intelligent Textbooks" |
| Target Audience | 5 | 0 | No audience stated anywhere in the document |
| Prerequisites | 5 | 3 | Technical prerequisites given (internet, browser, GenAI tool access) but no explicit statement of required background knowledge, and no "None" fallback |
| Main Topics Covered | 10 | 4 | Topics are mentioned in narrative prose ("Why Learning Graphs" section) but never itemized as a scannable list |
| Topics Excluded | 5 | 0 | No boundaries set — scope is undefined |
| Learning Outcomes Header | 5 | 0 | No "After this course, students will be able to..." statement |
| Remember | 10 | 0 | Absent |
| Understand | 10 | 0 | Absent |
| Apply | 10 | 0 | Absent |
| Analyze | 10 | 0 | Absent |
| Evaluate | 10 | 0 | Absent |
| Create | 10 | 0 | Absent |
| Descriptive Context | 5 | 5 | "Why Learning Graphs" section gives strong motivating context (AI slop problem, coherence, roadmap framing) |
| Total | 100 | 17 |
4. Gap Analysis
- No target audience — without this, learning-graph generation cannot calibrate concept depth/vocabulary (e.g., "professional" vs. "high school").
- No itemized topic list — the generator needs a scannable list of 5–10 topics to seed concept-count estimation; prose paragraphs undercount.
- No excluded topics — without a stated boundary, concept enumeration tends to sprawl into adjacent domains (e.g., general graph theory, general LMS administration).
- All six Bloom's Taxonomy levels missing — this is the single biggest gap. Bloom's outcomes are what let the generator produce concepts spanning recall-level vocabulary through capstone-level synthesis. Without them, a generator tends to only produce "Remember"- and "Understand"-level concepts, undercutting the 200-concept target and the diversity of concept types.
5. Improvement Suggestions (Priority Order)
- Add an explicit Target Audience line.
- Add six Learning Outcomes sections (Remember, Understand, Apply, Analyze, Evaluate, Create), each with 4–5 specific, verb-led outcomes.
- Convert the narrative topic description into an itemized Main Topics Covered list.
- Add a Topics Not Covered section to bound scope.
- Tighten Prerequisites into a clear required-vs-helpful split.
6. Concept Generation Readiness (Original)
At 17/100, the original description could realistically seed only 40–60 concepts before a generator would need to start improvising un-scoped material. The topic breadth (CDGs, taxonomies, graph algorithms, vis.js, agents) is actually reasonable — the missing piece was outcome granularity, which is what Bloom's-level outcomes provide.
7. Next Steps
This assessment was used to drive an immediate rewrite of docs/course-description.md (same session).
See the updated file for the revised score. Recommended follow-on: re-run this analyzer against the
rewritten file, then proceed to the learning-graph-generator skill once the score is ≥ 85.