Knowledge Types

12 min read

Knowledge Types

The four categories of knowledge — factual, conceptual, procedural, and metacognitive — each of which requires fundamentally different strategies to learn effectively.

What is it?

Not all knowledge is the same. When you learn the date of the French Revolution, you are doing something fundamentally different from learning how to ride a bicycle, which is different again from understanding why revolutions happen, which is different from knowing how you personally learn best. Yet most people study all four the same way — re-reading notes — and wonder why it does not stick.1

In 2001, Lorin Anderson and David Krathwohl revised Benjamin Bloom’s original 1956 taxonomy of educational objectives. One of the most important changes they made was adding a second dimension: the knowledge dimension.2 Where Bloom’s original taxonomy focused on cognitive processes (what you do with knowledge — remember, understand, apply, analyse, evaluate, create), the revised version also asks: what type of knowledge are you working with?3

The result is four knowledge types arranged from concrete to abstract: factual (the raw elements), conceptual (how elements relate), procedural (how to do things), and metacognitive (awareness of your own thinking).2 The key insight is that each type maps to different learning strategies and different learning-paradigms. Treating all knowledge the same way is one of the most common and costly learning mistakes.1

This framework does not just classify what you know — it helps you choose how to learn. A student who recognises that they are dealing with procedural knowledge will practise rather than re-read. A student who recognises conceptual knowledge will build diagrams and connections rather than memorise isolated facts.4

In plain terms

Knowledge types are like different ingredients in cooking. Sugar, flour, butter, and technique are all “things you need to bake a cake,” but you store them differently, use them differently, and learn to work with them differently. Treating flour like butter would ruin your recipe — and treating procedural knowledge like factual knowledge will ruin your learning.

At a glance

How does it work?

Factual knowledge — the building blocks

Factual knowledge is the discrete, concrete information you need to be familiar with a subject: terminology, specific details, dates, names, formulas, and basic elements.2 This is the knowledge you can look up, memorise, and verify as correct or incorrect.

Sub-typeExamples
Terminology”HTTP,” “variable,” “mitosis”
Specific details and elementsThe boiling point of water is 100 C; Python was created in 1991

Think of it like...

The vocabulary cards you make when learning a new language. You need these words before you can form sentences, but knowing individual words does not mean you can hold a conversation.

Conceptual knowledge — the relationships

Conceptual knowledge is understanding how facts connect to form larger structures: classifications, categories, principles, generalisations, theories, and models.2 This is where you move from knowing that to knowing why — seeing the patterns and relationships between individual elements.4

Sub-typeExamples
Classifications and categoriesVertebrates vs. invertebrates; data types in programming
Principles and generalisationsSupply and demand; the DRY principle in software
Theories, models, and structuresEvolution by natural selection; the client-server-model

Think of it like...

Understanding the grammar of a language rather than just the words. Grammar tells you how words relate to each other and why certain arrangements convey meaning while others do not.

Procedural knowledge — the how-to

Procedural knowledge is knowing how to do something: subject-specific skills, algorithms, techniques, methods, and — critically — the criteria for deciding when to use which procedure.2 This is the knowledge type most directly tied to performance and competence.5

Anderson and Krathwohl identified three sub-types:2

Sub-typeExamples
Subject-specific skills and algorithmsLong division; sorting algorithms; surgical suture techniques
Subject-specific techniques and methodsQualitative interview methods; debugging strategies; watercolour wet-on-wet
Criteria for when to use proceduresKnowing when to use a hash map vs. an array; when to apply direct pressure vs. a tourniquet

The third sub-type — knowing when — is often the hardest to acquire and the most valuable in practice. A novice knows the steps. An expert knows which steps to apply in which situation.5

Think of it like...

The difference between reading a recipe and being able to cook. A recipe is text on a page (factual/conceptual knowledge). Cooking is procedural knowledge — it requires practice, muscle memory, timing, and the judgment to adjust when something goes wrong.

Metacognitive knowledge — knowing your own mind

Metacognitive knowledge is awareness of your own cognition: how you think, how you learn, what strategies work for you, and what conditions affect your performance.2 Anderson and Krathwohl considered this the most abstract and the least commonly taught knowledge type.3

Sub-typeExamples
Strategic knowledgeKnowing that you learn better from diagrams than from text; knowing when to skim vs. read deeply
Knowledge about cognitive tasksUnderstanding that essay exams require different preparation than multiple-choice; that creative tasks need different conditions than analytical ones
Self-knowledgeRecognising that you procrastinate on ambiguous tasks; that you perform poorly when anxious; that you need silence to concentrate

Think of it like...

The dashboard of a car. The engine (your cognition) does the work, but the dashboard tells you how fast you are going, how much fuel you have, and whether something is wrong. Without it, you are driving blind.

The matching principle

The most practical takeaway from this framework is the matching principle: each knowledge type maps to different learning strategies and different cognitive processes.3 Using the wrong strategy for the knowledge type is like using a hammer to turn a screw — you might make progress, but it will be slow and the result will be poor.

Knowledge typeCommon mistakeBetter approach
FactualTrying to “understand” random factsMemorise with spaced repetition, then build connections
ConceptualMemorising definitions without understanding relationshipsBuild concept maps, explain to others, compare and contrast
ProceduralReading about how to do something without practisingPractise with feedback, use worked examples, then do it yourself
MetacognitiveIgnoring it entirelyReflect on your learning process, keep a learning journal

The matching principle

1. Identify the type first. Before studying anything, ask: is this factual, conceptual, procedural, or metacognitive knowledge? 2. Choose strategy accordingly. Each type has strategies that work and strategies that waste time. 3. Most knowledge involves multiple types. Learning to code requires factual (syntax), conceptual (design patterns), procedural (writing code), and metacognitive (debugging your own thinking) knowledge — each needs its own approach.3

Why do we use it?

Key reasons

1. It prevents wasted effort. Recognising that you are dealing with procedural knowledge saves you from endlessly re-reading when you should be practising.1 2. It diagnoses learning failures. When learning stalls, the knowledge type framework helps identify whether you are missing facts, misunderstanding relationships, lacking practice, or failing to self-regulate.5 3. It structures curriculum design. Teachers and course designers use the framework to ensure they are not accidentally teaching only factual knowledge when the goal requires procedural competence.3 4. It connects to assessment. Different knowledge types need different forms of evaluation — you cannot test procedural knowledge with a multiple-choice quiz.2

When do we use it?

  • When planning a study session and choosing which strategy to use
  • When designing a course, training programme, or learning path
  • When diagnosing why a learner (or you yourself) is struggling with a topic
  • When creating assessments that actually test what matters
  • When breaking a complex skill into its component knowledge types for systematic learning
  • When building a knowledge-granularity map of a new domain

Rule of thumb

If you have been studying something for a while and feel stuck, ask yourself: “Am I using the right strategy for this type of knowledge?” Chances are you are applying a factual-knowledge strategy to something that requires procedural practice or conceptual understanding.

How can I think about it?

The workshop analogy

Imagine you are learning woodworking.

  • Factual knowledge = knowing that oak is harder than pine, that a dovetail joint is stronger than a butt joint, that a chisel blade angle of 25 degrees is standard. You can look these up.
  • Conceptual knowledge = understanding why different joints work for different purposes, how wood grain affects strength, how moisture content relates to warping. You see the relationships.
  • Procedural knowledge = being able to actually cut a dovetail joint. No amount of reading about angles and wood types will teach your hands to do this. You need practice.
  • Metacognitive knowledge = noticing that you rush the marking-out step and it causes errors downstream, or that you learn cutting techniques better from video than from diagrams.

A master woodworker has all four. A beginner who reads ten books about woodworking but never picks up a chisel has strong factual and conceptual knowledge but zero procedural knowledge — and will not be able to build a shelf.

The language learning analogy

Learning a foreign language makes all four types visible.

  • Factual = vocabulary words, verb conjugation tables, grammar rules as stated in a textbook
  • Conceptual = understanding how tenses relate to each other, why word order matters, how formality levels work as a system
  • Procedural = actually speaking, writing, and listening in real time — the skill of producing and comprehending language under normal conditions
  • Metacognitive = knowing that you learn vocabulary best through conversation, that you need to hear a word seven times before it sticks, that you avoid speaking practice because of anxiety

This is why language apps that focus only on vocabulary (factual) produce learners who “know 2,000 words” but cannot hold a two-minute conversation. They have neglected procedural and metacognitive knowledge.

Concepts to explore next

ConceptWhat it coversStatus
experiential-learning-cycleKolb’s four-phase cycle for acquiring procedural knowledge through experiencecomplete
taxonomiesClassification systems and how they structure knowledgecomplete
dikw-hierarchyData, information, knowledge, wisdom — a complementary frameworkcomplete
knowledge-granularityHow to break knowledge into appropriately sized piecescomplete
claims-and-propositionsThe atomic units of knowledge and how to evaluate themcomplete

Some cards may not exist yet

A broken link is a placeholder for future learning, not an error.

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Where this concept fits

Position in the knowledge graph 

graph TD
    LP[Learning Paradigms] --> KT[Knowledge Types]
    KT --> ELC[Experiential Learning Cycle]
    LP --> CON[Constructivism]
    KT -.-> TAX[Taxonomies]
    KT -.-> DIKW[DIKW Hierarchy]
    style KT fill:#4a9ede,color:#fff

Related concepts:

  • claims-and-propositions — the atomic units within factual and conceptual knowledge
  • knowledge-granularity — how to size knowledge appropriately for learning and retrieval
  • dikw-hierarchy — a complementary framework that distinguishes data, information, knowledge, and wisdom
  • taxonomies — the broader practice of classifying knowledge that this framework exemplifies

Sources

Further reading

Resources

Footnotes

  1. Tapia, J. (2018). 4 Types of Knowledge. LearningStrategist. ↩ ↩2 ↩3 ↩4 ↩5 ↩6

  2. Anderson, L. W. & Krathwohl, D. R., et al. (2001). A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives. Allyn & Bacon. ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8

  3. Wilson, L. O. (2016). Anderson and Krathwohl — Bloom’s Taxonomy Revised. Quincy College. ↩ ↩2 ↩3 ↩4 ↩5

  4. DynDevice (n.d.). Four Types of Knowledge: Definition and Application to Online Courses. DynDevice TMS. ↩ ↩2

  5. Krathwohl, D. R. (2002). A Revision of Bloom’s Taxonomy: An Overview. Theory into Practice, 41(4), 212-218. ↩ ↩2 ↩3

  6. Efklides, A. (2020). Applying Metacognition and Self-Regulated Learning in the Classroom. Oxford Research Encyclopedia of Education. ↩

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