INFORMATIONAL SYNTHESIS OF INFORMATION AND ANALYTICAL SYSTEM OF ASSESSMENT QUALITY OF LEARNING CONTENT

А. В. Васильєв, А. С. Довбиш, В. О. Любчак, А. С. Осадчий

Abstract


In this paper the algorithm for parameters optimization of learning information-analytical system assessing the quality of educational content graduating department of higher education with the aim to adapt it to the needs of the labor market is proposed. Machine learning systems implemented under the informational and extremal intellectual technology of data analysis based on maximizing the capacity of information in the process of learning. The parameters of system settings that are optimized for machine learning stage, examined control tolerances mentioned signs of recognition and the geometrical parameters of the partition feature space into classes of quality of educational content as parameters of decision rules are considered. The algorithm of machine learning implements iterative approximation the global maximum informational criterion to its limit values in admissible domain of its function. The results of the proposed algorithm of machine learning system obtained by the example of assessment content modules disciplines of Bachelor in specialty "Computer science and information technology."

Keywords


machine learning, informational and extremal algorithm, recognition, signs of recognition, nested a container class of recognition, information criteria, learning content

References


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DOI: https://doi.org/10.32620/reks.2016.4.09

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