Differentiating Knowledge Types: Declarative vs. Procedural Understanding

Every time a student recalls that the capital of France is Paris, they are drawing on a different mental resource than when they solve a math equation step by step – or when a trainee teacher explains a concept versus when they actually manage a live classroom. These represent two fundamentally different categories of knowledge that cognitive psychologists call declarative knowledge and procedural knowledge. Understanding the difference between them – and knowing how to develop both – is essential for anyone involved in learning or teaching.

What is declarative knowledge? 🔗

Declarative knowledge is knowledge of facts, concepts, and principles – it is the knowledge of what is true, what the relationships are, and how the world works. It is often described as “knowing that” because it consists of information that can be consciously recalled and verbally expressed. When you can state that photosynthesis converts sunlight into energy, or explain the causes of the First World War, you are demonstrating declarative knowledge.

Declarative knowledge can be further divided into two subtypes. Semantic knowledge refers to general facts and concepts that are not tied to a specific time or place – for example, knowing that water boils at 100°C. Episodic knowledge refers to memory of specific events and experiences tied to a particular time and place, such as recalling the procedure demonstrated in a specific school science lesson. Together, these form the factual and conceptual foundation upon which deeper learning is built.

In the classroom, declarative knowledge is assessed most frequently. Traditional tests, written reports, definitions of vocabulary words, and recitation of formulas are all examples of tasks that draw on this type of knowledge. As Study.com notes, declarative knowledge answers the “who, what, when, and where” of information – it is knowledge demonstrated with nouns and descriptions, not actions.

What is procedural knowledge? 🔗

Procedural knowledge is “knowing how” to do something. It involves skills, strategies, and sequences of action required to complete a task. According to Wikipedia’s overview of procedural knowledge, this type of knowledge involves one’s ability to perform an action – and crucially, a person does not need to be able to verbally articulate their procedural knowledge for it to count as real knowledge. Much of it is tacit or subconscious.

Classic examples include riding a bicycle, performing a surgery, typing on a keyboard, or solving a multi-step algebra problem. You may know perfectly well how to ride a bike yet be completely unable to explain the precise physical adjustments your body makes to stay balanced. That gap between performance and explanation is characteristic of procedural knowledge. As the University of Oregon’s open textbook on language learning describes it, procedural knowledge is demonstrated through action – it may be difficult or even impossible to explain, but it can clearly be observed.

In educational settings, procedural knowledge shows up when students compare and contrast, compose an argument, troubleshoot a problem, or apply a formula to solve an unseen question. It answers the question: What can you do? And it is assessed through tasks involving action words – evaluate, design, solve, create.

Key differences between declarative and procedural knowledge 🔗

The most important distinction lies in consciousness and expressibility. Declarative knowledge is typically conscious and can be verbalized; procedural knowledge is often implicit and may resist explanation. Another key difference is in how each type is acquired and assessed. Declarative knowledge is usually built through reading, instruction, and exposure to information. Procedural knowledge is built through practice, repetition, and hands-on experience.

There is also a difference in transferability. Declarative knowledge tends to transfer more easily across situations because it consists of general facts and principles. Procedural knowledge, on the other hand, is often more context-dependent and harder to transfer unless the learner has sufficiently generalized their skills through varied practice.

A useful way to see the contrast is through a single subject – mathematics. Knowing that the formula for the area of a triangle is ½ × base × height is declarative knowledge. Knowing how to correctly identify the base and height in a non-standard triangle diagram and carry out the calculation accurately is procedural knowledge. Both are necessary for true mathematical competence; neither alone is sufficient.

How the brain handles both types 🔗

Cognitive neuroscience has confirmed that these two knowledge types engage distinct but interacting neural systems. As Shortform’s analysis of learning research explains, when new information is encountered, working memory captures it and sends it to the hippocampus and neocortex, where it is stored as declarative knowledge. With sufficient practice, this knowledge transitions into the procedural system, which operates faster and does not burden working memory during use.

This transition is at the core of John Anderson’s Adaptive Control of Thought (ACT) model – one of the most influential frameworks in cognitive psychology. According to ACT theory, all knowledge begins as declarative information. Through practice and application, it is converted into procedural knowledge through a process called proceduralization. This is why a beginner driver must consciously think through every step – check mirrors, press clutch, shift gear – while an experienced driver performs the same sequence without conscious effort. The knowledge has moved from declarative to procedural, becoming automatic.

Anderson’s model describes three stages: a declarative stage, where facts are stored but not yet efficiently used; an associative stage, where the learner begins to integrate and streamline their knowledge; and an autonomous stage, where performance becomes fast, accurate, and largely effortless. This progression has significant implications for how educators should design instruction and practice.

The role of rote learning and meaningful learning 🔗

A persistent debate in education concerns the value of rote learning – repetition-based memorization – versus meaningful learning, which connects new information to existing knowledge structures. The reality is that both have a legitimate role, depending on what is being learned and why.

Structural Learning’s review of rote learning research notes that rote memorization, when taken to the point of automaticity, actually creates the conditions for higher-order thinking. When foundational knowledge – multiplication tables, chemical symbols, grammatical rules – is encoded automatically, it frees up working memory for complex reasoning. As cognitive psychologist John Sweller’s research on cognitive load demonstrates, working memory can only hold a limited number of elements at once; automating the basics removes cognitive bottlenecks.

However, rote learning on its own is an incomplete strategy. Wikipedia’s entry on rote learning makes this clear: there is greater understanding when students commit a formula to memory through exercises that use the formula, rather than through simple repetition of it alone. The goal is for surface-level memorization to evolve into deep conceptual understanding – what researcher John Biggs, in his study of the “Chinese learner paradox,” found to be common in East Asian educational traditions, where memorization serves as an entry point to mastery rather than a substitute for it.

Meaningful learning, by contrast, actively connects new material to prior knowledge, making it easier to retain and apply. When a student understands why a formula works rather than just what it says, they are far better equipped to adapt their knowledge to novel problems.

Strategies for developing declarative knowledge 🔗

Building declarative knowledge effectively requires techniques that strengthen memory encoding and retrieval. Several well-supported strategies include:

Active recall – rather than passively rereading notes, testing yourself on content forces the brain to retrieve information, which strengthens memory far more effectively than exposure alone.

Spaced repetition – reviewing information at increasing time intervals improves long-term retention. This method works with the brain’s natural forgetting curve rather than against it.

Concept mapping – creating visual diagrams that show how pieces of information connect to one another helps learners see relationships rather than isolated facts, deepening understanding.

Mnemonics – memory aids such as acronyms, rhymes, or visual associations help anchor abstract facts to something memorable, particularly useful for complex terminology or sequences.

Using real-world examples and case studies to illustrate concepts also strengthens declarative knowledge by giving abstract information context and relevance, as highlighted in research on effective higher education pedagogy.

Strategies for developing procedural knowledge 🔗

Procedural knowledge cannot be built through reading or listening alone – it requires doing. The most effective approaches centre on structured, purposeful practice:

Practice and repetition – consistent, deliberate practice is the primary driver of procedural automaticity. As Anderson’s ACT model shows, it is through repeated use that declarative knowledge becomes procedural.

Feedback – regular, specific feedback helps learners identify errors and refine their technique. Without feedback, practice can entrench mistakes rather than correct them.

Modeling – observing an expert perform a skill gives learners a clear mental template of what accurate execution looks like, reducing the trial-and-error phase of skill acquisition.

Real-world application – providing opportunities for learners to apply skills in authentic contexts helps them generalise procedural knowledge beyond a single, narrow task, which is essential for meaningful skill development.

In higher education, this translates into laboratory work, fieldwork, internships, and project-based learning – experiences that cannot be replicated through textbook study alone.

Why both types of knowledge must work together 🔗

The two knowledge types are deeply interdependent. Declarative knowledge provides the conceptual foundation that makes procedural learning meaningful and transferable. Procedural practice, in turn, deepens and reinforces declarative understanding. A student studying chemistry needs to know the basic chemical formulas and concepts before they can carry out experiments – but the more experiments they conduct, the richer their conceptual understanding becomes.

Effective educators recognize this relationship and design instruction accordingly. Typically, this means introducing foundational facts and principles first, then creating structured opportunities for students to apply them in progressively more complex and varied tasks. As a peer-reviewed study in Education Sciences on learning skills development found, procedural knowledge acquired through practical teaching activities can itself strengthen declarative knowledge – the relationship flows in both directions.

The practical implication is straightforward: an instructional approach that focuses exclusively on factual recall risks producing students who know about things but cannot do anything with that knowledge. Equally, skills training without conceptual grounding produces brittle competence that breaks down when circumstances change. A balanced approach – explicit instruction in facts and concepts, followed by rich, feedback-informed practice – is what effective learning design looks like in practice.

What do you think? In your own experience as a learner or educator, which type of knowledge tends to receive more instructional attention – declarative or procedural? And how might a more deliberate balance between the two change the way students prepare for real-world challenges?

References

1. https://www.linkedin.com/pulse/balancing-declarative-procedural-knowledge-higher-education-naseer
2. https://study.com/academy/lesson/teaching-strategies-for-declarative-vs-procedural-knowledge.html
3. https://en.wikipedia.org/wiki/Procedural_knowledge
4. https://opentext.uoregon.edu/languagelearningedition1/chapter/procedural-and-declarative-knowledge/
5. https://www.shortform.com/blog/hub/books-learning/education-books-learning/procedural-declarative-learning/
6. https://www.instructionaldesign.org/theories/act/
7. https://www.structural-learning.com/post/rote-learning
8. https://en.wikipedia.org/wiki/Rote_learning
9. https://files.eric.ed.gov/fulltext/EJ1317912.pdf