learning-strategy

name

learning-strategy

description

Apply learning-strategy whenever the user needs to close a knowledge gap, ramp up on a new domain, or build a structured plan for acquiring understanding. Triggers on phrases like "how do I learn this?", "I'm new to this domain", "ramp up on", "knowledge gap", "what should I study?", "learning plan", "how do I get up to speed?", "teach me about", "onboarding plan". Use proactively when epistemic-mapping reveals gaps that block decisions — knowing what you don't know is only useful if you then learn it efficiently.

Learning Strategy

Core principle: Learning is not consuming — it is restructuring what you know. Efficient learning targets the specific gap, picks the right mode for the knowledge type, and tests understanding before moving on. A reading list is not a learning plan.

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When to Use This Skill

• epistemic-mapping identified knowledge gaps blocking a decision or analysis
• Someone is entering an unfamiliar domain and needs to ramp up fast
• A team needs shared understanding of a new technology, market, or method
• Prior learning attempts stalled — material consumed without retention or application
• An onboarding plan is needed for a new team member or role transition

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Core Methodology

Step 1: Define the Knowledge Gap Precisely

Vague goals ("learn machine learning") produce vague learning. Specify:

• What you need to know: The specific questions to answer or capabilities to perform. Frame as concrete outcomes: "Evaluate whether a transformer architecture fits our latency requirements" — not "understand transformers."
• Why it matters: What decision, action, or deliverable is blocked. This determines depth — you don't need PhD-level understanding to make a procurement decision.
• Current knowledge level: What you already know in this and adjacent domains. Prior knowledge is the scaffold for new knowledge.
• Target depth: Awareness (know it exists), comprehension (explain it), application (use it), or mastery (teach it and handle edge cases). Most professional learning targets application.

Step 2: Map Prerequisites

Knowledge has dependencies. Build a tree:

1. List the key concepts in the target domain
2. For each, ask: "What must I understand before this makes sense?"
3. Identify which prerequisites you already have (mark satisfied)
4. The unsatisfied prerequisites closest to your current knowledge are where learning starts

Common prerequisite failures:

• Jumping to advanced material without foundations → confusion and surface memorization
• Spending too long on prerequisites that aren't needed for your target depth
• Missing a conceptual prerequisite while having the technical one (or vice versa)

Step 3: Select Learning Modes

Different knowledge types need different approaches:

Declarative (facts, concepts, frameworks) → Reading, lectures, structured summaries. Test with explanation: can you teach it without notes?

Procedural (how to do things) → Hands-on practice, tutorials with exercises, deliberate repetition. Test with performance: can you do it without instructions?

Conditional (when to apply what) → Case studies, worked examples with variation, exposure to edge cases. Test with transfer: can you apply it to a novel situation?

Tacit (judgment, intuition) → Apprenticeship, observation of experts, reflection on your own practice. Test with prediction: can you anticipate what will happen?

For each topic, specify the primary mode. Mismatching mode to knowledge type is the most common source of wasted learning time — reading about negotiation teaches concepts, not skill.

Step 4: Sequence the Learning Plan

Order topics to maximize understanding and motivation:

1. Start with orientation: 30-minute survey of the entire domain — overview reading or intro talk. Build a mental map of the territory before diving into any region.
2. Follow the prerequisite tree: Bottom-up from unsatisfied prerequisites to target concepts.
3. Interleave theory and practice: Alternate conceptual understanding with hands-on application. Pure theory doesn't consolidate; pure practice doesn't transfer.
4. Increase difficulty progressively: Clean prototypical examples first; complexity and edge cases after fundamentals are solid.

For each topic, specify:

• Source(s) — book chapter, doc section, course module, expert
• Mode (read, do, observe, discuss)
• Time estimate
• Output to produce (notes, exercise completion, explanation draft)

Step 5: Build Verification Checkpoints

Learning without testing is just exposure. Build checkpoints that reveal genuine understanding vs. familiarity:

Feynman Test: Explain in plain language as if teaching a non-expert. Where your explanation gets vague, hand-wavy, or jargon-dependent — that's where understanding breaks down.

Application Test: Solve a problem you haven't seen before using the concept. If you can only solve identical problems, you've memorized patterns, not learned principles.

Prediction Test: Before reading the next section or running the next experiment, predict what you'll find. Surprise indicates a gap in your model.

Teaching Test: Explain to a colleague or write it up. If you can handle their questions, you've learned it. If not, you know exactly where to focus next.

Place a checkpoint after each major topic. Define the specific test and the minimum bar for "understood well enough to proceed."

Step 6: Plan for Retention

Understanding today doesn't guarantee recall next month:

• Spaced repetition: Review at increasing intervals — day 1, 3, 7, 14, 30. Focus on material you got wrong at the last checkpoint.
• Active recall: Don't re-read notes. Close them and reconstruct key points from memory. Re-reading feels like learning but produces minimal retention.
• Connection-building: Link new knowledge to what you already know — analogies, comparisons, integration into existing models. Isolated knowledge decays fastest.
• Application schedule: Plan real uses within the first week. Knowledge used in context is retained; idle knowledge fades.

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Output Format

🧠 Knowledge Gap

• What to learn: [Specific knowledge or capability needed]
• Why it matters: [Decision or action blocked by this gap]
• Current level: [What you already know in this and adjacent areas]
• Target depth: [Awareness / Comprehension / Application / Mastery]

🗺️ Prerequisite Map

[Target Concept]
├── [Prerequisite A] ✅ (already known)
├── [Prerequisite B] ❌ (must learn)
│   ├── [Sub-prerequisite B1] ✅
│   └── [Sub-prerequisite B2] ❌ (start here)
└── [Prerequisite C] ❌ (must learn)

📋 Learning Plan

#	Topic	Mode	Source	Time	Output
1	[Topic — start with prerequisites]	[Read/Do/Observe/Discuss]	[Source]	[Estimate]	[What you'll produce]

🎯 Key Concepts to Master

For each core concept, write the Feynman-style plain-language explanation target:

• [Concept 1]: [Plain-language explanation you should be able to give when learned]
• [Concept 2]: [Plain-language explanation target]

✅ Progress Checkpoints

After	Test	Pass criteria
[Topic/module]	[Feynman / Application / Prediction / Teaching]	[Specific bar for "understood"]

⏱️ Time Investment

• Total estimated hours: [Sum of plan]
• Calendar schedule: [Pacing — e.g., 2 hours/day for 5 days]
• First application date: [When to use the knowledge in real work]

🔄 Retention Plan

• Spaced review schedule: [Day 1, 3, 7, 14, 30]
• Active recall method: [Flashcards / self-quizzing / whiteboard reconstruction]
• Application opportunities: [Real tasks where this knowledge will be used]

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Thinking Triggers

• "Am I consuming material or actually understanding it? Can I explain this without looking at my notes?"
• "What's the minimum I need to learn to unblock the decision — and what can wait?"
• "Am I starting at the right level, or missing prerequisites I don't know I'm missing?"
• "Is reading the right mode here, or do I need to learn by doing?"
• "What would surprise me about this topic? Where are my predictions wrong?"
• "A week from now, what from today's learning will I actually remember?"

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Common Traps

• Collector's fallacy: Gathering resources (bookmarking, buying courses, saving papers) and mistaking that for learning. A reading list with nothing read is zero knowledge.
• Tutorial hell: Completing guided exercises but never building anything without instructions. Tutorials teach following; practice teaches doing.
• Depth mismatch: Spending weeks reaching mastery when application was all that was needed. Match investment to requirement.
• Linear completion: Working through a textbook front-to-back when only chapters 3, 7, 12 are relevant. Use the prerequisite map to skip what you don't need.
• Passive review: Re-reading highlighted notes feels productive but produces minimal retention. Active recall is 3-5x more effective per hour invested.

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Example Applications

Trigger	Application
"I need to evaluate whether to adopt Kubernetes"	Target depth: application (make the decision, not operate clusters). Prerequisites: container basics → orchestration → K8s architecture → operational requirements. Mode: read overview + talk to an operator (tacit knowledge about operational burden).
epistemic-mapping flagged "We don't understand our users' workflow"	Target depth: comprehension. Mode: observation (watch users work), then discussion (interview for tacit knowledge). Checkpoint: predict a user's next action correctly 70% of the time.
"New team member needs to onboard to our codebase"	Map prerequisite domains (language, framework, domain concepts). Sequence: architecture overview → guided code walkthrough → small bug fix → feature implementation. Checkpoints at each stage.
"I keep reading about distributed systems but nothing sticks"	Diagnose the mode mismatch: reading alone doesn't build procedural knowledge. Redesign: read one chapter, then implement a simplified version, then predict failure modes before reading about them.