Course Description Assessment: Forensic Science¶
Generated by the course-description-analyzer skill v0.03
Overall Score: 94 / 100¶
Quality Rating: Excellent — Ready for learning graph generation
Detailed Scoring Breakdown¶
| Element | Max | Earned | Notes |
|---|---|---|---|
| Title | 5 | 5 | "Forensic Science" — clear, specific |
| Target Audience | 5 | 5 | "High school students (grades 9–12)" with prerequisite context |
| Prerequisites | 5 | 5 | Three explicit prerequisites with content detail |
| Main Topics Covered | 10 | 10 | 15 topics with rich sub-concept detail per topic |
| Topics Excluded | 5 | 5 | Four explicit exclusions with rationale |
| Learning Outcomes Header | 5 | 5 | Present with Bloom's framing |
| Remember Level | 10 | 9 | 8 outcomes; all use correct recall verbs |
| Understand Level | 10 | 9 | 7 outcomes; strong explain/describe/distinguish verbs |
| Apply Level | 10 | 10 | 7 outcomes; all have concrete quantitative or procedural detail |
| Analyze Level | 10 | 10 | 7 outcomes; strong differentiate/categorize/analyze verbs |
| Evaluate Level | 10 | 10 | 7 outcomes; excellent coverage including cross-discipline evaluation |
| Create Level | 10 | 10 | 6 outcomes including a capstone mock trial and protocol design |
| Descriptive Context | 5 | 5 | Module structure overview and course purpose statement present |
| Total | 100 | 94 |
Gap Analysis¶
Minor gaps (−6 points total)¶
Remember (−1): The eight outcomes are all strong, but none address physical/instrumental terminology that students will need to recall during lab work — e.g., the names and functions of write-blocker hardware, the specific presumptive color tests (Kastle-Meyer = pink, Marquis = purple/black, etc.), or the CODIS locus names. Adding 2–3 instrument/reagent recall outcomes would close this gap.
Understand (−1): Missing an explicit outcome for understanding electropherogram interpretation (reading allele peaks on a capillary electrophoresis trace). This is a core DNA profiling competency and currently appears only in the Apply and Analyze tiers indirectly.
Concept Generation Readiness¶
Estimated concept count from current content: 210–240
The 15-chapter structure, each with 5–7 named sub-concepts, yields a conservative floor of 105 named sub-concepts. Bloom's outcomes at the Apply and Create tiers introduce additional procedural and analytical concepts (e.g., "retro-extrapolation", "product rule probability", "stringing technique") that are distinct learnable units. The digital forensics and DNA chapters alone contribute 20+ technical concepts (EXIF, MD5/SHA-256, STR, CODIS, PCR, electropherogram, etc.).
Assessment: Sufficient depth to generate 200+ concepts without augmentation.
Improvement Suggestions (prioritized)¶
-
(High impact — Remember tier) Add 2–3 outcomes naming specific reagents, color reactions, or instrumental techniques: e.g., "Recall the color reaction produced by the Marquis reagent when applied to MDMA." These create discrete concept nodes in the learning graph.
-
(Medium impact — Understand tier) Add an outcome for electropherogram interpretation: "Explain how allele peaks on a capillary electropherogram are read to assign STR genotypes, including the distinction between homozygous and heterozygous loci."
-
(Low impact — optional polish) The Topics Excluded section could note one more boundary: detailed autopsy procedure steps are excluded (post-mortem interval estimation uses entomological/anthropological data, not autopsy narrative). This tightens scope for the learning graph generator.
Next Steps¶
Score ≥ 85 — proceed with learning graph generation.
Run the learning-graph-generator skill next. The course description will
support a full 200-concept DAG with:
- ~15 foundational concepts (legal framework, scientific method, lab safety)
- ~60 domain-specific technical concepts distributed across the 6 modules
- ~50 procedural concepts (lab techniques, calculation methods)
- ~40 interpretive/analytical concepts (pattern recognition, statistical reasoning, cross-discipline inference)
- ~35 integrative/create-tier concepts (case reconstruction, expert testimony, protocol design)
Suggested command: invoke the learning-graph-generator skill from the
project root with this course description as input.