Course Description Quality Assessment
Course: Robot Faces: Drawing Expressive Displays for STEM Robots
1. Overall Score: 97/100
2. Quality Rating
Excellent — Ready for learning graph generation (90-100 band)
3. Detailed Scoring Breakdown
| Element | Points Possible | Points Earned | Notes |
|---|---|---|---|
| Title | 5 | 5 | Clear and descriptive |
| Target Audience | 5 | 5 | Specific: high school (9-12), with named secondary audiences |
| Prerequisites | 5 | 5 | Explicitly "None," with a clarifying sentence on what helps but isn't required |
| Main Topics Covered | 10 | 10 | 12 comprehensive topics spanning hardware, drawing, psychology of expression, animation, interaction, and color displays |
| Topics Excluded | 5 | 5 | 7 explicit boundaries (chassis/motors, computer vision, NLP/voice, non-display sensors, PCB/soldering, enclosures, general Python) |
| Learning Outcomes Header | 5 | 5 | Present verbatim |
| Remember | 10 | 10 | 4 specific, recall-level outcomes |
| Understand | 10 | 10 | 4 specific, explanation-level outcomes |
| Apply | 10 | 10 | 4 specific, hands-on outcomes tied to actual FrameBuf methods and hardware |
| Analyze | 10 | 10 | 4 outcomes requiring decomposition/comparison |
| Evaluate | 10 | 10 | 3 outcomes (meets the minimum-of-3 threshold) using critique/judge/assess verbs |
| Create | 10 | 10 | 3 outcomes including a fully specified two-display capstone project |
| Descriptive Context | 5 | 5 | Overview explains relevance (HRI, low-cost classroom access, computational thinking) |
| Total | 100 | 97 | 3 points held back for the minor polish items below |
4. Gap Analysis
Nothing in the rubric is missing or weak enough to lose a full category. Two minor, non-blocking polish opportunities:
- Evaluate outcomes sit at the minimum count (3). A fourth Evaluate-level outcome would add margin and make it easier to derive distinct "evaluate" concepts later (e.g., judging accessibility/readability of a face design at a distance, or evaluating power/CPU trade-offs).
- No explicit target concept-count estimate in the document itself. The course description is rich enough to support 200+ concepts (see Section 6 below), but stating an intended concept count (e.g., "~200-250 concepts") directly in the course description would make the boundary explicit for future edits.
5. Improvement Suggestions
- (Optional) Add one more Evaluate-level outcome, for example: "Evaluate whether a face design remains readable when viewed from typical classroom distance and lighting."
- (Optional) Add a one-line note under Course Overview stating the intended concept-graph size to anchor future scope decisions.
- Neither change is required before proceeding — both are refinements, not gaps.
6. Concept Generation Readiness
- Topic breadth/depth: 12 main topics, each decomposable into many concrete concepts (drawing methods, hardware pins, emotion categories, animation techniques, color-model terms).
- Outcome diversity: All six Bloom's levels have specific, varied outcomes referencing distinct concepts (coordinate systems, FrameBuf methods, SPI wiring, Ekman emotions, minimal- feature research, RGB565, state machines, benchmarking, capstone design) — this variety is a strong signal for generating concepts across the full taxonomy, not just factual recall.
- Existing content cross-check: The prior content scan in starting-concept-list.md already surfaced 97 concepts from the current lessons and code alone, before this course description added new scope (facial- expression research/psychology, animation/interpolation, and color-display parity). Combined, this comfortably supports the standard 200-concept target for a learning graph without needing to go to the 500-concept tier.
- Recommendation: Proceed to the
learning-graph-generatorskill. No additional course description content is required first.
Next Steps
Score is ≥ 85 — ready to proceed directly to learning graph generation.