{"id":52334,"date":"1990-09-20T07:00:00","date_gmt":"1990-09-20T07:00:00","guid":{"rendered":"https:\/\/www.supermemo.com?p=52334"},"modified":"2022-11-02T11:19:56","modified_gmt":"2022-11-02T11:19:56","slug":"optimization-of-learning-summary","status":"publish","type":"post","link":"https:\/\/stage.supermemo.com\/en\/blog\/optimization-of-learning-summary","title":{"rendered":"Optimization of Learning: Summary"},"content":{"rendered":"
\n

Piotr Wozniak, 1990<\/p>\n\n

From P.A.Wozniak, Optimization of Learning, updated and corrected May 20, 1997<\/p>\n\n

Here is the list of the most important points of the thesis (terminology given in boldface was included in the Glossary):<\/p>\n\n

    \n
  1. The\u00a0SuperMemo method<\/strong>\u00a0used in\u00a0repetition spacing<\/strong>\u00a0was presented (Chapter 3). The following elements of the method marked the most significant steps in its development:\n
      \n
    • application of the recall principle <\/strong>(Chapter 3.1)<\/li>\n\n\n\n
    • application of the minimum information principle<\/strong> (Chapters 2 and 3.1)<\/li>\n\n\n\n
    • application of the optimum repetition spacing principle <\/strong>(Chapters 2 and 3.1)<\/li>\n\n\n\n
    • introducing E-factors<\/strong> (Chapter 3.2)<\/li>\n\n\n\n
    • introducing the function of optimum intervals <\/strong>(Chapter 3.3 and 3.4)<\/li>\n\n\n\n
    • application of interval dispersing <\/strong>(Chapter 3.5)<\/li>\n\n\n\n
    • application of the propagation of changes in the matrix of optimal intervals <\/strong>(Chapter 3.7)<\/li>\n<\/ul>\n<\/li>\n\n\n\n
    • Software implementation of the SuperMemo method was described (Chapter 4)<\/li>\n\n\n\n
    • SuperMemo on paper was described (Chapter 7)<\/li>\n\n\n\n
    • The\u00a0function of optimum intervals <\/strong>was found by means of three methods:\n
        \n
      • specially designed experiment (p.16)<\/li>\n\n\n\n
      • univalent matrices of optimal factors <\/strong>in the Algorithm SM-5 <\/strong>(Chapter 3.6)<\/li>\n\n\n\n
      • model of intermittent learning <\/strong>(Chapter 11.4)<\/li>\n<\/ul>\n<\/li>\n\n\n\n
      • A comprehensive analysis of the SuperMemo learning process was presented:\n
          \n
        • an accurate simulation model of the SuperMemo process was constructed (Chapter 5)<\/li>\n\n\n\n
        • function of the acquisition rate was found (p.68)<\/li>\n\n\n\n
        • all-life, maximum acquisition rate <\/strong>was predicted to be about 230 item\/year\/min (this value may be substantially lower in case of ill-structured SuperMemo databases<\/strong>, or, possibly, higher in case of further development of the knowledge structuring <\/strong>techniques) (p.68)<\/li>\n\n\n\n
        • the all-life capacity of the human brain was estimated to be about several million SuperMemo items <\/strong>(p. 72)<\/li>\n\n\n\n
        • long-term acquisition of knowledge with the use of the SuperMemo method <\/strong>was shown to be close to linear (p.69)<\/li>\n\n\n\n
        • workload<\/strong> function <\/strong>was found (p. 65)<\/li>\n\n\n\n
        • reducing the forgetting index<\/strong> was found as of little value for the speed and quality of learning<\/li>\n\n\n\n
        • eliminating items <\/strong>characterized by low E-factors <\/strong>was demonstrated to be crucial for the speed of learning (p. 68)<\/li>\n\n\n\n
        • it was found that only 5% of the learning process can be spent on acquisition of knew knowledge, the rest is consumed by repetitions of the old material<\/li>\n\n\n\n
        • forgetting rate <\/strong>in case of the cessation of repetitions was found to be much higher than the acquisition rate <\/strong>(e.g. after 5 years of the process, 60% of knowledge is lost in the first year after the cessation) (p. 70)<\/li>\n\n\n\n
        • burden<\/strong> parameter was proposed as a very accurate measure of the learning progress in SuperMemo (p.53)<\/li>\n\n\n\n
        • model of intermittent learning <\/strong>was constructed (Chapter 11)<\/li>\n\n\n\n
        • relationship between the forgetting index <\/strong>and knowledge retention <\/strong>was found to be close to linear. For the index equal to 10%, as in the Algorithm SM-5<\/strong>, the long-term retention was predicted to be 94% (the presently reported retention reaches 96%) (p. 154)<\/li>\n\n\n\n
        • the increase of the stability of memories <\/strong>was found to be the greatest if the intervals are twice as long as the optimal intervals <\/strong>(it corresponds to the forgetting index equal to 20%) (p. 156)<\/li>\n\n\n\n
        • the function of the workload-retention trade-off<\/strong> was found (p. 155)<\/li>\n\n\n\n
        • the function of the workload-retention<\/strong> trade-off<\/strong> was used to determine that the desirable value of the forgetting index <\/strong>falls in the rage 5% to 10% (p. 155)<\/li>\n<\/ul>\n<\/li>\n\n\n\n
        • Method-independent prerequisites of the successful application of SuperMemo were formulated (Chapter 6)<\/li>\n\n\n\n
        • Results of a questionnaire collecting opinions of SuperMemo students were presented (Chapter 8)<\/li>\n\n\n\n
        • Biological aspects of learning in the light of the SuperMemo method were analyzed:\n
            \n
          • distinction between stochastic<\/strong> and deterministic learning <\/strong>was proposed (Chapter 10.2)<\/li>\n\n\n\n
          • optimum repetition spacing in stochastic learning was found to be possibly less dense than that of deterministic learning (p. 95)<\/li>\n\n\n\n
          • illustrative, hypothetical models of neural circuitry involved in stochastic<\/strong> and deterministic learning <\/strong>were described (p. 106)<\/li>\n\n\n\n
          • arguments for the presynaptic character of the facilitation in case of stochastic learning <\/strong>were listed as well as arguments for heterosynaptic facilitation in deterministic learning <\/strong>(Chapter 10.2)<\/li>\n\n\n\n
          • discussion of the nature of short-term and long-term memory was included (Chapter 10.1 and 10.3)<\/li>\n\n\n\n
          • new arguments for the postsynaptic membrane as the location of long-term memory were put forward (Chapter 10.3)<\/li>\n\n\n\n
          • E-factors<\/strong> were proposed as a reflection of the number of synapses involved in remembering particular items<\/strong> (Chapter 10.4.1)<\/li>\n\n\n\n
          • existence of at least two components of memory was postulated and demonstrated: retrievability<\/strong> and stability<\/strong> (Chapter 10.4.2)<\/li>\n\n\n\n
          • phosphorylation of proteins was considered as possibly responsible for retrievability<\/strong> (Chapter 10.4.3)<\/li>\n\n\n\n
          • the number of postsynaptic receptors was considered as possibly responsible for stability<\/strong> (Chapter 10.4.3)<\/li>\n<\/ul>\n<\/li>\n\n\n\n
          • Possible future applications of SuperMemo were outlined (Chapter 12). As an illustration, software supervising a touch typing training was described (p. 87). A simple method for using SuperMemo in learning to play musical instruments was presented (p. 92). Universal nature of learning based on repetition spacing was suggested (p. 165).<\/li>\n\n\n\n
          • The mere existence of the SuperMemo METHOD refutes or calls in question a pretty large number of common sense conceptions and dogmas of the psychology of learning. The most prominent examples are listed below:\n
              \n
            • opposition of memorization to logical thinking is pointless. Memorization (or according to my terminology deterministic learning<\/strong>) lays ground for the refinement of the circuitry of the brain which is later on used in the process of thinking (p. 167)<\/li>\n\n\n\n
            • ever-lasting memory acquired by a single learning act is unlikely. Cases of supernatural memory refer to either short-term memory, mutant individuals or must be otherwise seriously reconsidered [Luria, 73] (p. 168)<\/li>\n\n\n\n
            • time necessary to learn a given material is proportional to the first, not second power of the size of the material. This refers only to long-term memory and properly spaced repetition process (p. 169)<\/li>\n\n\n\n
            • forgetting has a biochemical nature (trace-decay theory) and is only partially caused by interference (interference theory). Proper application of the minimum information principle<\/strong>, principle of univocality<\/strong>, mnemonic techniques etc. allows to one to reduce interference to a negligible level (Chapter 10.4)<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<\/div><\/div><\/div><\/div>","protected":false},"excerpt":{"rendered":"Optimization of Learning: Summary","protected":false},"author":6,"featured_media":52756,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[65,214],"tags":[1699,1698],"acf":[],"yoast_head":"\nOptimization of Learning: Summary - SuperMemo<\/title>\n<meta name=\"description\" content=\"Here is the list of the most important points of the thesis (terminology given in boldface was included in the Glossary.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/stage.supermemo.com\/en\/blog\/optimization-of-learning-summary\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Optimization of Learning: Summary - 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