FAQ Quality Report¶

Generated: 2026-07-25

Overall Statistics¶

Total Questions: 97
Overall Quality Score: 70/100
Content Completeness Score (pre-generation gate): ~97/100 — course description (25/25), learning graph (25/25, valid DAG, 531 concepts, single connected component), glossary (15/15, 531 terms), chapter word count (20/20, 93,896 words across 20 chapters), estimated concept-to-chapter coverage (~12/15)
FAQ Concept Coverage: 30.7% (163/531 concepts referenced by at least one FAQ answer)

Why concept coverage reads lower than a typical FAQ¶

This course intentionally scaled its learning graph to 531 concepts, roughly 2.5x the ~200-concept default the FAQ generator's question-count guidance (70–110 total questions) is calibrated against. concept-list.md says this directly: "many concepts here are intentionally shallow... the deep treatment lives in a companion book." Reaching 60–80% coverage of 531 concepts would require roughly 250–425 dedicated questions — well outside this skill's per-category count targets (Getting Started 10–15, Core Concepts 20–30, Technical Details 15–25, Common Challenges 10–15, Best Practices 10–15, Advanced Topics 5–10). The 97 questions generated here sit at the high end of that range by design.

The Glossary already provides 100% definitional coverage of all 531 concepts (531/531 terms with ISO-11179-style definitions), so no concept in this book is undocumented — the FAQ's role is to answer the questions students and educators actually ask most often, not to duplicate the glossary one entry at a time.

Measurement note: concept coverage was computed by exact case-insensitive phrase matching between each learning-graph concept label and the full FAQ text. This is a conservative measure — several concepts are discussed at length using a stylistic variant of the label (for example, the FAQ discusses "H-bridge" with a hyphen, but the graph label is "H Bridge"; it discusses "time-of-flight sensor," but the graph label is "Time Of Flight Sensor"). True semantic coverage is very likely a few points higher than the number reported here.

Category Breakdown¶

Category	Questions	Avg Bloom's Level	Avg Word Count
Getting Started	14	Remember/Understand	106
Core Concepts	27	Understand	107
Technical Details	22	Understand	87
Common Challenges	13	Apply/Analyze	89
Best Practices	13	Apply/Evaluate	100
Advanced Topics	8	Evaluate/Create	140

Bloom's Taxonomy Distribution¶

Actual vs. target (target values follow the skill's canonical overall distribution: Remember 20%, Understand 30%, Apply 25%, Analyze 15%, Evaluate 7%, Create 3%):

Level	Count	Actual	Target	Deviation
Remember	19	19.6%	20%	-0.4% ✓
Understand	39	40.2%	30%	+10.2%
Apply	17	17.5%	25%	-7.5%
Analyze	11	11.3%	15%	-3.7%
Evaluate	6	6.2%	7%	-0.8% ✓
Create	5	5.2%	3%	+2.2%

Total absolute deviation: 24.8% → Bloom's Score: 15/25 (21–30% deviation band)

Every level is represented, and the two most cognitively demanding categories (Best Practices and Advanced Topics) are correctly weighted toward Apply/Evaluate/Create. The main imbalance is an oversupply of Understand-level questions in the Core Concepts and Technical Details categories, where many questions were phrased as "What is X, and how/why does it work?" — a natural phrasing for a technical FAQ, but one that reliably classifies as Understand rather than Apply or Analyze under this skill's own Bloom's mapping (references/blooms-taxonomy.md). See Recommendations below for how to shift this in a future revision.

Answer Quality Analysis¶

Examples: 61/97 (62.9%) — Target: 40%+ ✓ (7/7 pts)
Links: 97/97 (100%) — Target: 60%+ ✓ (7/7 pts)
Avg Length: 109.3 words — Target: 100–300 ✓ (6/6 pts)
Complete Answers: 97/97 (100%) — every question has a full, standalone answer ✓ (5/5 pts)

Answer Quality Score: 25/25

Every answer links to at least one chapter, the glossary, or the course description, and grounds its explanation in a specific fact, number, code snippet, or scenario drawn from that chapter (a wiring warning, a worked Ohm's Law calculation, a named MicroSim, a real function name) rather than a generic restatement of the concept.

Concept Coverage¶

Coverage Score: 10/30 (see explanation above — this course's 531-concept graph is roughly 2.5x the scale this skill's question-count guidance targets)

Coverage by taxonomy category:

Category	Covered	Total	%
Foundational Concepts (FOUND)	27	48	56%
Breadboard & Circuits (CIRC)	24	44	55%
AI HAT+ / Vision (VISN)	20	40	50%
NeoPixel/LED & Sensors (LEDSN)	18	60	30%
No-Display & OLED Robot (ROBOT)	19	68	28%
Pi 5 Hardware (PIHW)	11	39	28%
Budgeting & Classroom Logistics (LOGIS)	14	55	25%
Wireless / Pico W (WIFI)	9	36	25%
Audio/Sound AI (AUDIO)	7	34	21%
Clocks & Watches (CLOCK)	6	34	18%
Linux / Pi 500+ (LINUX)	7	45	16%
RGB Keyboard (RGBKB)	1	28	4%

Foundational and breadboard/circuit concepts — the earliest, most-reused material in the book — are covered above 50%, which is the right place to be strongest. The RGB Keyboard and Linux/Pi 500+ categories are the weakest, mainly because each maps to only 1–2 chapters (13, and 11–12 respectively) that were each allotted a proportional handful of FAQ questions rather than an exhaustive one-per-concept treatment. See FAQ Coverage Gaps for the prioritized list.

Organization Quality¶

Logical categorization: ✓ (5/5) — 6 standard categories, no concept question misplaced into the wrong bucket
Progressive difficulty: ✓ (5/5) — Getting Started/Core Concepts skew Remember/Understand, Common Challenges/Best Practices skew Apply/Analyze, Advanced Topics skews Evaluate/Create
No duplicates: ✓ (5/5) — all 97 questions are unique, verified programmatically
Clear questions: ✓ (5/5) — every question is a complete, specific, searchable sentence ending in "?"

Organization Score: 20/20

Overall Quality Score: 70/100¶

Coverage: 10/30
Bloom's Distribution: 15/25
Answer Quality: 25/25
Organization: 20/20

This FAQ clears every structural success criterion the skill defines (97 ≥ 40 minimum questions, all six Bloom's levels represented, zero duplicate questions, zero anchor links, every internal link resolves to a real file) and scores at maximum on Answer Quality and Organization. It falls short of the ">75/100" and "60%+ concept coverage" success criteria specifically because of this course's unusually large 531-concept graph — see Recommendations for the most efficient way to close that gap without a full FAQ rewrite.

Recommendations¶

High Priority¶

Add a targeted batch of Remember/Understand questions for the weakest categories — RGB Keyboard (4% covered) and Linux/Pi 500+ (16% covered) — starting from the critical gaps in FAQ Coverage Gaps.
Cover the 4 remaining high-indegree concepts not yet in the FAQ: Analog Read (ID 130, indegree 9), Bash Shell (ID 302, indegree 9), DS3231 Module (ID 222, indegree 7), and IP Address (ID 258, indegree 7). Each already has substantial supporting discussion elsewhere in the FAQ (ADC/analog pins, the Linux shell, the DS3231 RTC chip, and DHCP) but never uses the exact canonical term — adding one question per term, or lightly revising an existing answer to use the canonical phrase, would fix this cheaply.
Cover the one uncovered foundational concept: Physical Computing (ID 7).

Medium Priority¶

Rebalance Bloom's distribution by rephrasing a portion of the Core Concepts and Technical Details categories' "What is X, and how/why does it work?" (Understand) questions toward Apply framing ("How do I use X to...", "When should I choose X over Y..."), which would simultaneously raise the Apply percentage (currently 17.5% vs. a 25% target) and lower the Understand percentage (currently 40.2% vs. a 30% target).
Add 2–3 more Analyze-level questions to Core Concepts (currently only 2 of 27) — for example, questions connecting two concepts across chapters, like "How does the sense-think-act cycle in Chapter 1 change once a project adds wireless communication in Chapter 10?"

Low Priority¶

Consider a second FAQ pass focused specifically on the Audio/Sound AI (21%) and Clocks & Watches (18%) categories once companion-book content (Clocks and Watches covers the full 20+ display-driver catalog this course intentionally treats shallowly) makes deeper coverage worthwhile.
Re-run concept coverage with a fuzzier matcher (tolerant of hyphenation and word-order differences, e.g. "H-bridge" vs. "H Bridge") to get a more accurate true-coverage baseline before deciding how many more questions are actually needed.