A NOVICE IS NOT A LITTLE EXPERT — Key Concepts and Teaching Implications

Key idea

  • A novice does not simply have less knowledge than an expert; they think differently and organize knowledge differently.
  • Expertise involves both more knowledge and a qualitatively different way of using it to solve problems.

Core concepts

  • Problem representation:
    • Experts categorize problems using deep physics principles and underlying laws (e.g., conservation laws).
    • Novices categorize problems by surface features and problem descriptors (e.g., keywords like friction, gravity).
  • Schemata:
    • Knowledge stored as rich, interconnected schemata in long-term memory.
    • Experts possess deep, generalized schemata; novices have simpler, often incomplete schemata.
  • Landmarks and categorization:
    • Experts recognize problem types by landmarks and states, not just by descriptive terms.
    • Novices rely on superficial features and try to match surface cues to past problems.
  • Prior knowledge and problem solving:
    • Prior knowledge shapes how problems are perceived and solved; deeper knowledge enables quicker, more accurate solutions.
    • Deep conceptual knowledge guides problem solving, while surface feature knowledge guides initial interpretation for novices.

Key findings from Chi, Feltovich & Glaser (1979)

  • Reading and interpretation differ: experts interpret in terms of underlying principles; novices focus on wording and surface details.
  • Categorization differences:
    • Experts: deeper, principle-based categorization.
    • Novices: description-based categorization.
  • Solution strategies:
    • Experts link problems to correct solution strategies via deep laws.
    • Novices take longer to connect problem to a solution due to lack of deep links.

How experts categorize problems (summary)

  • When confronted with a new problem, experts think about the physical conditions and apply appropriate laws before selecting a strategy.
  • Prior knowledge acts as a scaffold that accelerates correct categorization and solution planning.

Differences between experts and novices (Table 6.1 interpretation)

  • Problem categorisation:
    • NOVICES: problem-specific categorisation based on surface features.
    • EXPERTS: deeper, generalized categorisation based on underlying physical laws.
  • Use of prior knowledge:
    • NOVICES: limited schemata; rely on description cues.
    • EXPERTS: rich schemata; interpret states and apply laws.
  • Approach to solving:
    • NOVICES: connect problems to features and described elements.
    • EXPERTS: link problems to core principles and solution strategies.

Schemas and concept organization

  • SCHEMA: cognitive framework that organizes knowledge for understanding and solving problems.
  • Types of knowledge:
    • CONCEPTUAL KNOWLEDGE: deep understanding of concepts and principles.
    • LANDMARKS: features used to classify and interpret problems (helps with problem matching).
  • Role of schemata in learning:
    • Beginners have rudimentary schemata; their problem-solving is less efficient.
    • Experts have extensive schemata enabling rapid recognition and accurate categorization.

Assimilation and accommodation (Piaget)

  • Assimilation: incorporating new knowledge into existing schemata.
  • Accommodation: altering schemata to fit new knowledge.
  • Misconceptions arise when schemas are incomplete or incorrect; teaching should modify these schemata gradually.

Educational implications

  • Beginners are not mini-experts:
    • They think differently and require different instructional approaches.
  • Epistemology is not pedagogy:
    • Teaching methods effective for experts may not work for novices and can be harmful (expertise reversal effect).
  • Differentiation from early on:
    • Tailor instruction to the learner's prior knowledge and cognitive state.
    • Use think-aloud protocols to reveal students’ thinking and miscomprehensions.
  • Curse of knowledge:
    • Highly knowledgeable instructors may forget the novice perspective and skip essential steps.

Instructional design tips (practical)

  • Worked examples with fading guidance:
    • Gradually remove steps as learners gain competence (guidance fading).
  • Avoid abrupt shifts from worked examples to problems for novices; prefer gradual reduction of support.
  • Differentiation in reading and problem-solving tasks:
    • Provide pathways that align with students’ current schemata.
    • Encourage explicit thinking about problem categorization and solution strategies.
  • Activating prior knowledge:
    • Connect new content to what students already know; assess and correct misconceptions.

How to use this in teaching

  • Do not assume novices are simply smaller versions of experts.
  • Differentiate early in the learning process based on prior knowledge and skill level.
  • Make thinking processes explicit to help students build correct schemata.
  • Create learning activities that help students recognize underlying principles, not just surface features.

Takeaways (from the chapter)

  • Beginners aren’t little experts; they think and know differently.
  • Children see and learn differently from adults; teaching must reflect this.
  • A teaching approach for experts will often fail for beginners; differentiate early.
  • Differentiate at an early stage and tailor to prior knowledge.
  • Epistemology of the expert is not the proper pedagogy for the learner.
  • Beware the curse of knowledge: instructors may forget the novice perspective.

Suggested readings (references)

  • Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1979). Categorization and Representation of Physics Problems by Experts and Novices. Cognitive Science, 5, 121–152.
  • De Groot, A. D. (1946, 1965). Think/Thought processes in chess; Thought and Choice in Chess.
  • Kalyuga, Chandler, & Sweller (1998). Levels of Expertise and Instructional Design.
  • Piaget (1952). The Origins of Intelligence in Children; Assimilation & Accommodation.
  • Sweller, Ayres, Kalyuga, & Chandler (2003). The Expertise Reversal Effect.
  • Schneider & Shiffrin (1977). Controlled and Automatic Human Information Processing.
  • Kennedy (1995). Debiasing the Curse of Knowledge in Audit Judgment.