This text was taken from P.A.Wozniak, Economics of learning, Doctoral Dissertation, University of Economics, Wroclaw, 1995 and adapted for publishing as an independent article on the Web. (P.A.Wozniak, Apr 23, 1997)

Knowledge structuring and representation in learning economics using self-instruction systems based on the active recall principle

In this article I will attempt to show that it is feasible to dismember learned knowledge into small pieces that can be attributed to individual semantic units of topics in a knowledge system subject to a learning procedure. I also predict the inevitability of such an approach in future self-instruction systems based on active recall.

We will discuss elements that, independently of the repetition spacing algorithm (e.g. as used in SuperMemo), influence the effectiveness of learning. In particular we will see, using examples from a simple knowledge system used in learning microeconomics, how knowledge representation affects the easiness with which knowledge can be retained in the student’s memory. The microeconomics knowledge system has almost entirely been based on the material included in Economics of the firm. Theory and practice by Arthur A. Thompson, Jr, 1989. Some general concepts of macroeconomics have been added from Macroeconomics by M. McKenzie, 1986, while the mathematical capsule items have been taken from Marketing Research by David A. Aaker and George S. Day, 1990.

  1. Knowledge independent elements of the optimization of self-instruction

  2. Knowledge representation issue in learning

  3. Components of effective knowledge representation in active recall systems

  4. Sequencing items in the stepwise process of acquiring associative knowledge

  5. Techniques for minimizing the complexity of synaptic patterns as a key to keeping A-factors high

  6. Planned redundancy as a way to cross-strengthening synaptic patterns

  7. Complexity of wording vs. comprehension

  8. Additional functionality encapsulated in items

  9. Summary of knowledge representation issues in learning

  • The main concerns in minimization of the complexity of synaptic patterns in learning are:
    • ensuring full comprehension of the isolated item of knowledge
    • applying minimum information principle
    • reducing the complexity of items by narrowing the information contents by example
    • capitalizing on visual capabilities of the human brain by applying mnemonic, metaphoric, vivid and graphic approaches
    • applying strict enumeration techniques (e.g. deletion, grouping, etc.)
    • complying with univocality principle
  • As far as planned redundancy is concerned, the most important principles to remember are:
    • applying both passive and active approach to recall of information
    • applying the full derivation approach (i.e. learning the derivation steps of an assertion rather than the assertion alone)
    • providing reasoning, mnemonic and context clues

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