Map as a basis for decision-making in the automated learning process | Научно-инновационный портал СФУ

Map as a basis for decision-making in the automated learning process

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: International Workshop Applied Methods of Statistical Analysis Nonparametric Methods in Cybernetics and System Analysis, AMSA 2017; Красноярск, Россия; Красноярск, Россия

Год издания: 2017

Ключевые слова: Automation Educational System, Cognitive Map of Knowledge Diagnosis, Decision- making, Individualization, Shortliffe-Buchanan method

Аннотация: Decision making in intelligent learning systems is based on the processing of a multitude of factors about the training course, models of the learner, learning situation and specific methodological knowledge (model teacher, methodologist and subject tutor). Their ordering and a compact representation in a single space will simplify the process of finding compromise solutions that take into account the subjective goals of each model. This paper proposes to use the method of mapping, on the basis of which to build a multidimensional Cognitive Maps of Knowledge Diagnosis, represents Atlas individual learning activities. To build and analyze maps in each case tested a number of hypotheses based on small statistical series of data and based on the use of nonparametric methods. One of the key methods of testing hypotheses in this approach is the method Shortliffe-Buchanan. To illustrate its application in testing hypotheses exam- ples related to the level of formation of cognitive maps of diagnostic knowledge and its processing (analysis) in making decisions the solver learning system. © Novosibirsk State Technical University, 2017.

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Издание

Журнал: Applied Methods of Statistical Analysis

Номера страниц: 325-334

ISSN журнала: 2313870X

Издатель: Novosibirsk State Technical University

Авторы

  • Uglev V. (Siberian Federal University, Zheleznogorsk, Russian Federation)
  • Cholodilov S. (Siberian Federal University, Zheleznogorsk, Russian Federation)
  • Cholodilova V. (Siberian Federal University, Zheleznogorsk, Russian Federation)

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