FORECASTING THE SUCCESS OF EDUCATION SEEKERS FROM A SEPARATE EDUCATIONAL COMPONENT BASED ON THE RESULTS OF THE PRELIMINARY MASTERY OF SUBJECT COMPETENCIES

Abstract

The paper examines the main concepts related to the quality of education in general and the assimilation of educational material by higher education seekers. The task of predicting a seeker's grade in any discipline is formulated with data on his assimilation of program learning outcomes that also correspond to this discipline. The available specialized information system of own development is described which applies a number of methods (multivariate linear regression, artificial neural networks, k-nearest neighbors) and determines which method will be the most effective for the analysis of specific data. It is noted that during the further improvement of the quality system of knowledge assessment, it is important to determine at what level the student of education possesses the acquired competences, i.e. to calculate the success of seekers in terms of general and professional competences and program learning outcomes, determined by the standards of higher education and educational programs developed on their basis. The developed algorithm for calculating the success rate of higher education applicants in terms of program learning outcomes is presented; according to this algorithm, data were prepared on the acquisition of software creation competencies by 78 seekers of the first level of higher education of the educational and professional program Intelligent Decision Support Systems specialty 124, Systems analysis, of the DSEA.  To solve the problem of forecasting by the method of artificial neural networks, the programming and data analysis language R is proposed. A script for finding the optimal neural network architecture is created. It was found that the best result (correlation is 0.9599, average absolute reduced error is 0.1132, percentage of correctly predicted points on the Ukrainian scale is 79.2) provides a perceptron with two hidden layers and five neurons in each one. This network was applied to predict the success of the new academic group: correlation is 0.923, the average absolute reduced error is 0.0654, the percentage of correctly predicted points on the Ukrainian scale is 82.4. The obtained results can be used to assess the quality of the structural and logical scheme of the EPP and in the work of the department during the analysis of seekers' success, etc.