Course Description Assessment Report
Course: Computational Thinking with STEM Robots
Assessment Tool Version: 0.03
Date: 2026-06-23
Overall Score: 96 / 100
Quality Rating: Excellent — Ready for learning graph generation
Detailed Scoring Breakdown
| Element | Max Points | Points Earned | Notes |
|---|---|---|---|
| Title | 5 | 5 | Clear, specific title with formal **Title:** field |
| Target Audience | 5 | 5 | "High school students, grades 9–12, with little or no prior coding experience" — precise and actionable |
| Prerequisites | 5 | 5 | "None." explicitly stated with helpful clarification |
| Main Topics Covered | 10 | 10 | 12 well-scoped topics in a logical teaching sequence |
| Topics Excluded | 5 | 5 | 14 out-of-scope topics explicitly listed — excellent boundary setting |
| Learning Outcomes Header | 5 | 5 | "After completing this course, students will be able to:" — canonical phrasing |
| Remember | 10 | 10 | 8 outcomes with strong recall verbs (List, Recall, Identify, Name, State, Recognize) |
| Understand | 10 | 10 | 9 outcomes with strong comprehension verbs (Explain, Describe, Interpret, Summarize) |
| Apply | 10 | 10 | 11 outcomes with strong procedural verbs (Write, Connect, Configure, Program, Assemble, Use, Implement, Apply, Build) |
| Analyze | 10 | 10 | 8 outcomes with strong analytic verbs (Compare, Examine, Distinguish, Analyze, Break down, Differentiate) |
| Evaluate | 10 | 10 | 8 outcomes with strong judgment verbs (Assess, Evaluate, Judge, Test, Critique, Select) |
| Create | 10 | 9 | 9 outcomes including a capstone project; docked 1 point as no explicit mention of a formal presentation deliverable format |
| Descriptive Context | 5 | 5 | 3-paragraph overview covering purpose, hardware, pedagogy, and course arc |
| TOTAL | 100 | 96 |
Gap Analysis
Only one minor gap was identified:
Create level (–1 point): The capstone project is well-described, but there is no explicit requirement for a student presentation or demonstration deliverable. For the most rigorous Bloom's "Create" level, students should be required to communicate their design choices — not just build and submit code. This is addressed in the Assessment Methods section but not in the Create learning outcomes themselves.
All other elements scored full marks.
Improvement Suggestions
High Priority (will improve learning graph generation)
-
Add a presentation outcome to Create level:
Consider adding:
"Present their capstone robot project to peers, explaining the problem decomposition, algorithm design, and lessons learned during iteration." -
Consider adding a "Week-by-Week" concept map:
A table mapping each of the 14 weeks to specific concepts from the Topics list would help the learning graph generator assign weights and sequencing. This could live in the course description or in a companion schedule document.
Low Priority (nice-to-have refinements)
-
Concept count estimate: Based on the 12 main topics and 60+ Bloom's outcomes, this course description is expected to yield 180–230 concepts in the learning graph. The hardware platform section alone (RP2040, GPIO, PWM, I2C, SPI, motors, sensors, displays) contributes approximately 60 concepts. The MicroPython programming section adds ~50. The remaining topics fill the rest.
-
Distinguish core vs. extension concepts: A brief note on which topics are required for all students versus challenge/extension topics (e.g., WiFi/IoT, advanced display graphics, PID control) would help instructors using the learning graph to differentiate instruction.
Concept Generation Readiness
| Dimension | Assessment |
|---|---|
| Topic breadth | Excellent — 12 major topic areas |
| Topic depth | Excellent — each topic has 3–10 sub-concepts derivable from the course docs |
| Bloom's level diversity | Excellent — all six levels populated with specific, concrete outcomes |
| Concept count estimate | ~200–230 concepts (above the 200-concept threshold) |
| Boundary clarity | Excellent — 14 out-of-scope topics prevent over-generation |
Verdict: This course description is ready for learning graph generation. The topic list, excluded topics, and Bloom's taxonomy outcomes together provide sufficient signal to generate a well-scoped graph of 200+ concepts with accurate dependency relationships.
Original File Assessment
For the record, the course-description.md file before this revision scored 5/100 (Poor):
| Element | Old Score | New Score |
|---|---|---|
| Title | 4 | 5 |
| Target Audience | 0 | 5 |
| Prerequisites | 0 | 5 |
| Main Topics | 0 | 10 |
| Topics Excluded | 0 | 5 |
| Outcomes Header | 0 | 5 |
| Remember | 0 | 10 |
| Understand | 0 | 10 |
| Apply | 0 | 10 |
| Analyze | 0 | 10 |
| Evaluate | 0 | 10 |
| Create | 0 | 9 |
| Descriptive Context | 1 | 5 |
| Total | 5 | 96 |
Next Steps
Score is 96/100 — proceed directly to learning graph generation.
The recommended next step is to run the learning-graph-generator skill, which will use this course description and the docs/learning-graph/concepts-covered-v1.md file as inputs to build a structured dependency graph of ~200 concepts.
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