Economics of learning

Piotr Wozniak, December 1994

Economics of Learning, the doctoral dissertation by Dr Piotr Wozniak, has laid a theoretical foundation for developing SuperMemo 8 for Windows (and its successors). The full text of the dissertation can be found on SuperMemo CD-ROM titles (e.g. Advanced English 97, Cross Country, Video English or MegaMix 99). Below only the most important or influential texts are presented (often hyperlinked to newer or more detailed texts). Small italicized hyperlinks in parentheses suggest and alternative more up-to-date reading
Economics of learning

University of Economics in Wroclaw
Department of Management and Computer Science

Piotr A. Wozniak

Economics of learning

New aspects in designing modern computer aided self-instruction systems

Ph.D. Dissertation

Supervisor:
Prof. Witold Abramowicz

Wroclaw 1995


Contents

  1. Introduction
    • 1.1.1. Education and technology
    • 1.1.2. Novel solutions in demand on social, institutional, regional and national platforms
    • 1.1.3. The thesis
    • 1.1.4. The goals in the perspective of time
    • 1.1.5. The genesis of the dissertation
    • 1.1.6. The structure of the dissertation
  2. Spacing of repetitions in the practice of learning
    • 2.1. Research background
      • 2.1.1. Optimum spacing of repetitions
      • 2.1.2. Spacing effect
      • 2.1.3. Involuntary habituation
    • 2.2. Development of the algorithm for optimally spacing repetitions (see: Optimization of learning)
      • 2.2.1. First computer applications of repetition spacing algorithms
      • 2.2.2. Modifying the function of optimal intervals on the basis of the student’s characteristics
    • 2.3. New algorithmic elements (algorithm used in SuperMemo 6)
      • 2.3.1. Introducing the concept of the forgetting index
      • 2.3.2. Algorithm SM-6 (see: Algorithm SM-11)
    • 2.4. Approximation of the forgetting curve
    • 2.5. Simulation of the long-term learning process
    • 2.6. Long-term savings resulting from the application of repetition spacing
  3. Exemplary implementation of algorithms for spacing repetitions in a self-instruction system (see: SuperMemo 99)
  4. Analysis of data collected from a group of students using a repetition spacing algorithm
    • 4.1. Method
    • 4.2. Results
    • 4.2.1. General learning parameters
    • 4.2.2. Forgetting curves
    • 4.2.3. Matrices of retention factors
    • 4.2.4. Matrices of optimal factors
    • 4.2.5. Distribution of intervals
    • 4.2.6. Distribution of E-factors
    • 4.2.7. Results of the analysis in the light of the economics of learning
  5. Molecular interpretation of mechanisms of memory underlying the optimum spacing of repetitions
    • 5.1. Interpretation of differences in item difficulty
    • 5.2. Two components of long-term memory
    • 5.3. Molecular memory
      • 5.3.1. Advances in molecular research of memory
      • 5.3.2. General observation in reference to memory and learning
      • 5.3.3. Hippocampus as the focus of research on long-term potentiation
      • 5.3.4. The role of acetylcholine in establishing memories
      • 5.3.5. Short-term potentiation
      • 5.3.6. Long-term potentiation of synaptic transmission in the hippocampus
      • 5.3.7. NMDA receptor as the central factor in establishing LTP
      • 5.3.8. Non-NMDA glutamate receptor
      • 5.3.9. Retrograde messengers in synaptic transmission
      • 5.3.10. Role of calcium
      • 5.3.11. Protein kinase C
      • 5.3.12. Other kinases involved in establishing LTP
      • 5.3.13. New evidence on the role of cAMP in memory and learning
      • 5.3.14. Calpain
      • 5.3.15. Metabotropic glutamate receptor
      • 5.3.16. Gene expression and memory
      • 5.3.17. Protein synthesis and memory
      • 5.3.18. Protein G
      • 5.3.19. Potassium channels
    • 5.4. Molecular correlates of the two components of memory
  6. Knowledge structuring and representation in learning economics using self-instruction systems based on the active recall principle (for a popular scientific version of this text see: 20 rules of formulating knowledge in learning)
    • 6.1. Knowledge independent elements of the optimization of self- instruction
    • 6.2. Knowledge representation issue in learning
    • 6.3. Components of effective knowledge representation in active recall systems
    • 6.4. Sequencing items in the stepwise process of acquiring associative knowledge
    • 6.5. Techniques for minimizing the complexity of synaptic patterns as a key to keeping E-factors high
    • 6.5.1. Comprehension
    • 6.5.2. Minimum information principle
    • 6.5.3. Narrowing by example
    • 6.5.4. Metaphoric approach
    • 6.5.5. Vivid approach
    • 6.5.6. Graphic approach
    • 6.5.7. Enumeration techniques
    • 6.5.8. Deletion and graphic deletion
    • 6.5.9. Dismembering complex concepts
    • 6.5.10. Mnemonic techniques
    • 6.5.11. Item univocality and inter-item interference
    • 6.6. Planned redundancy as a way to cross-strengthening synaptic patterns
      • 6.6.1. Passive and active approach
      • 6.6.2. Support for derivation, reasoning and intelligence
      • 6.6.3. Optional reasoning clues, mnemonic clues, context and examples
    • 6.7. Complexity of wording vs. comprehension
    • 6.8. Additional functionality encapsulated in items
    • 6.9. Summary of knowledge representation issues in learning
  7. Modern hypermedia systems encompassing the ability to adapt to the properties of human memory and cognition
    • 7.1. Fusion of the hypertext paradigm with techniques targeted against human forgetfulness
      • 7.1.1. Implementation shortcomings evident hypertext interfaces
      • 7.1.2. New solutions proposed for hypertext systems
      • 7.1.3. Integration of repetition spacing technology hypertext interface
    • 7.2. Hypermedia systems that account for human perception and memory
      • 7.2.1. Knowledge Machine
      • 7.2.2. Technological and economic feasibility of global hyperspace
      • 7.2.3. Infosociety or global infobabble
      • 7.2.4. Processing attributes and repetition spacing tools incorporated in the hyperspace
      • 7.2.5. Global impact of the Knowledge Machine
  8. Conclusions
  9. Acknowledgments
  10. Glossary (see Glossary 1999)
  11. References
  12. Further reading
  13. Index

1.1.14