Open AccessHIGHER SCHOOL EDUCATION QUALITY FORECASTING BY REGRESSION ANALYSIS METHODS
Author(s) -
Ivan M. Kharitonov,
Е. Г. Крушель,
Oleg O. Privalov,
И. В. Степанченко,
Olga V. Stepanchenko
Publication year - 2021
Publication title -
izvestiâ sankt-peterburgskogo gosudarstvennogo tehnologičeskogo instituta (tehničeskogo universiteta)
Language(s) - English
Resource type - Journals
ISSN - 1998-9849
DOI - 10.36807/1998-9849-2020-56-82-72-80
Subject(s) - quality (philosophy) , due diligence , diligence , regression analysis , mathematics education , multivariate statistics , bayesian multivariate linear regression , scale (ratio) , index (typography) , linear regression , process (computing) , multivariate analysis , computer science , psychology , machine learning , business , geography , social psychology , philosophy , cartography , epistemology , world wide web , finance , operating system
In addition to the career guidance measures, the computer-aided facilities are proposed to assist secondary school graduates in choosing the appropriate direction of higher school speciality and in the estimation of the perspectives of the future education process achievements. The facilities are based on the multivariate linear regression model used for forecasting the education quality at senior years of a higher school. The model is based on a vector of predictor factors; the components of that vector are the data of secondary school education results in the disciplines, which are essential for successful study at the chosen speciality, and the data of characteristic marks of a candidate’s general abilities level and diligence. The education quality index is formed as the middle value of the expert estimations assigned by the lecturers for each senior university year student in accordance with the accepted mark scale. An approach efficiency example is presented for the forecasting of the successful study of the computer science disciplines.