Processing and prediction of educational process data based on fuzzy regression analysis

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Abstract

Quality indicators of fuzzy regression models designed to study the dependencies between the qualitative characteristics of the educational process and to predict their values, as well as a model for recognizing fuzzy values of the output characteristics of regressions are presented. An algorithm for selecting a fuzzy regression model from linear and nonlinear models based on their quality indicators is given. An analysis of the degree of influence of input characteristics on the output characteristic is carried out. A fuzzy regression model has been constructed to predict the success of the dissertation defense when the applicant enters the PhD program and to study the dependencies between the applicant’s input characteristics and the output characteristic. An alternative approach to the construction of regression models based on non-numerical data of the educational process allows not to impose incorrect conditions on the initial data, considering them to be the values of random variables, and not to use incorrect arithmetic operations for the elements of ordinal scales.

About the authors

Ol’ga M. Poleshchuk

BMSTU (Mytishchi branch)

Author for correspondence.
Email: poleshchuk@mgul.ac.ru

Dr. Sci. (Tech.), Professor, Head of Higher Mathematics and Physics Department

Russian Federation, 1, 1st Institutskaya st., 141005, Mytishchi, Moscow reg.

Evgeniy G. Komarov

BMSTU (Mytishchi branch)

Email: komarov@mgul.ac.ru

Dr. Sci. (Tech.), Professor, Head of the Department of Information and Measuring Systems and Instrumentation Technologies

Russian Federation, 1, 1st Institutskaya st., 141005, Mytishchi, Moscow reg.

Nikolay G. Poyarkov

BMSTU (Mytishchi branch)

Email: poyarkov@mgul.ac.ru

Cand. Sci. (Tech.), Associate Professor, Dean of the Space Faculty

Russian Federation, 1, 1st Institutskaya st., 141005, Mytishchi, Moscow reg.

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