DECISION SUPPORT SYSTEM STATISTICAL SYNTHESIS, BASED ON AN ASSESSMENT CARRIED OUT WITH THE INTENSITY OF INTERACTION TEACH AND THE LEARNING ENVIRONMENT

Decision Support System – a powerful tool that is present now in the most complex systems. Decision Support System should take into account the strength of the interaction with the student learning environment. This ensures a high level of knowledge and ability of their application by alumni.The method of learning process modeling of this system in terms of statistics is examined. This paper proposes a statistical synthesis of a decision support system for the case by use of a positive feedback loop takes into account the intensity of the interaction between the students and the learning environment. Synthesis carried out by the criterion of minimum posterior probability of receiving an unsatisfactory assessment on the exam (test, offset). Synthesis carried out under the following assumptions: the intensity of the interaction of the learner with the learning environment is a discrete value, the space state student assessment is a finite and discrete, the process of passing the adaptive educational resources (consistent implementation of the study and the sections of the technical discipline) is probabilistic in nature and is modeled by a Markov chain. It was assumed that the transition probabilities of the interaction intensity of the same, the transition probability density estimates is allocated in accordance with the truncated left and right of a Gaussian distribution (the procedure for determining the expectation of a Gaussian density estimation and standard deviation in the article described), and the a priori probability density for the first step can be approximated Gaussian probability density based on a priori information on the results of the student to him for study providing disciplines. The illustrations amply illustrate the main results of the study. Evaluation of the intensity of the interaction with the student learning environment in the article is defined as a discrete estimate of the maximum a posteriori probability density.The results allow to realize the synthesized system in practice. To assess the adequacy of the model developed by the authors pedagogical experiment was carried out, the nature and the results of which will be given in subsequent publications.