Deep vs. Surface Learning


Conceptual vs. Procedural Knowledge
Relational vs. Instrumental Understanding


Contrasting attitudes and motivations around what it means to “know.”

Principal Metaphors

Deep Learning Surface Learning
  • Knowledge is … scope of current human possibility
  • Knowing is … meaning
  • Learner is … an integrator (active agent)
  • Learning is …  making meaning, integrating
  • Teaching is … engaging
  • Knowledge is … information
  • Knowing is … using/applying information
  • Learner is … a collector (passive recipient)
  • Learning is … taking in information, memorizing
  • Teaching is … transmission




Deep vs. Surface Learning highlights a fundamental divergence of opinion around what it means to know something. Deep Learning is associated with intrinsically motivated forms of engagement, characterized by making meaningful connections between new and previous understandings. With Deep Learning, the learner knows how concepts relate to each other and knows how to apply the concepts to solve problems. Surface Learning tends to be associated with extrinsic motivations and is focused on the memorization and recall of information for formulaic responses. With Surface Learning, the learner is imagined to acquire new information without connection to other ideas and with little personal investment. (See Expert–Novice for associated distinctions.) Associated constructs and discourses include:
  • Inert Knowledge (Alfred North Whitehead, 1920s) – a dismissive reference to decontextualized knowledge learned in formal settings (reminiscent of Surface Learning)
  • Conceptual Knowledge vs. Procedural Knowledge
    • Conceptual Knowledge (Abstract Learning; Conceptual LearningConceptual Understanding) – learning focused on developing appreciations of the structure and logics of abstract ideas, typically emphasizing logical and systematic associations among them
    • Procedural Knowledge (Procedural LearningProcedural UnderstandingSkill Learning) – learning aimed at eventually performing a task automatically, typically emphasizing accuracy and speed
  • Operative Knowledge vs. Figurative Knowledge
    • Operative Knowledge (Jean Piaget, 1950s) – knowledge developed by “performing operations” – that is, engaging/operating meaningfully with/in one’s world
    • Figurative Knowledge (Jean Piaget, 1950s) – memory-based knowledge of information
  • Relational Understanding vs. Instrumental Understanding
    • Relational Understanding (Richard Skemp, 1970s) – knowing when, where, and why to apply a mastered rule or procedure
    • Instrumental Understanding (Richard Skemp, 1970s) – knowing how to apply a mastered rule or procedure
  • Heuristic vs. Algorithm
    • Heuristic (Cognitive Heuristic; Heuristic Technique) (Herbert A. Simon, 1950s) – a practical, experience-based (i.e., neither logical nor generalizable) means to solve a problem that usually, but not always, offers an efficient route to a solution. (Compare: Algorithm.)
    • Algorithm – a well-defined sequence of steps that can be used to generate a solution to a specific type of question. (Compare: Heuristic)
  • Deep Active Learning – a mash-up of Deep vs. Surface Learning and Active Learning, thus emphasizing learner agency, active engagement, conceptual understanding, and intrinsic motivation. (Note: There’s also a Deep Active Learning associated with Machine Learning.)


Deep vs. Surface Learning might be viewed as a scaled-down and simplified version of the contrast between Correspondence Discourses and Coherence Discourses, as it foregrounds incompatible conceptions of learners (e.g., passive recipients vs. active agents), knowledge (e.g., external objects vs. emergent possibilities), and so on. However, the discourse is generally engaged much more superficially. For example, Deep vs. Surface Learning is often interpreted not as a contrast, but as a tension or “war.” Examples include:
  • Math Wars – an ongoing debate over the relative merits of “traditional” (skills-focused, practice-based, teacher-centered) and “reform” (understanding-oriented, inquiry-heavy, and learner-focused) modes of school mathematics
  • Reading Wars – an ongoing debate on the best way to teach reading – either by “decoding” (i.e., applying phonetic rules) or by “Whole Language” (attending to meaning and comprehension).
To make matters worse, rather than grappling with the subtleties and complexities of the underlying issues, many commentators seem convinced that answers rest on the metaphor of “balance.” Examples include:
  • Balanced Math – a combination of direct, practice-based instruction and more explorative, problem-based engagements (a common ratio is 80%–20%)
  • Balanced Literacy – subject to multiple interpretations, but most often a combination teacher-directed, skills-focused activities and student-led, meaning-centered activities. Prominent discourses within the Balanced Literacy movement include:
    • Whole Language (Ken Goodman, 1980s) – a mode of teaching reading that emphasizes meaning, context, expression, integration, and interaction over (but not instead of) phonics
Unfortunately, the this-or-that logic of “wars” and “balances” veils the fact that these popular-but-narrow debates are rooted in just two subsets of discourses (see the map below), thus eclipsing or distorting most of what is known about learning and teaching.

Authors and/or Prominent Influences

Ference Marton; Roger Saljö; Richard Skemp

Status as a Theory of Learning

Deep vs. Surface Learning is not a theory of learning because it does not offer new insights into the complex dynamics of learning.

Status as a Theory of Teaching

Deep vs. Surface Learning is a theory of teaching. That is, it is focused on modes of structuring lessons and manners of student engagement that are associated with different types of learning.

Status as a Scientific Theory

Deep vs. Surface Learning is more a principle to interpret an individual’s learning than a theory that invites a research program. As such, it doesn’t make much sense to consider its scientific status. That said, recent research in Neuroscience has demonstrated that there are observable differences in brain function between the two types of learning.


  • Algorithm
  • Balanced Literacy
  • Balanced Math
  • Conceptual Knowledge (Abstract Learning; Conceptual Learning; Conceptual Understanding)
  • Deep Active Learning
  • Figurative Knowledge
  • Heuristic (Cognitive Heuristic; Heuristic Technique)
  • Inert Knowledge
  • Instrumental Understanding
  • Math Wars
  • Operative Knowledge
  • Procedural Knowledge (Procedural Learning; Procedural Understanding; Skill Learning)
  • Reading Wars
  • Relational Understanding
  • Whole Language

Map Location

Please cite this article as:
Davis, B., & Francis, K. (2022). “Deep vs. Surface Learning” in Discourses on Learning in Education.

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