Conference paper 2021

Application of data mining methods to development of qualification framework standards

CV
Chernyshenko, Vsevolod S.
Proceedings - 2021 1st International Conference on Technology Enhanced Learning in Higher Education, TELE 2021
Conference · pp. 209-215
Abstract

Specific features of creating a web environment for implementing feedback with stakeholders of the education system being discussed. Specific focus is aimed on the representatives of real economy. Authors propose mechanism of constructing individual environment for each individual user, associating their roles (during the registration process), e.g. with specific economic sectors and professions as the user registers as a professional in the labor market. Depending on the educational/professional interests of the users, individual questionnaires may be designed for an intelligent data mining. The necessity to rank the votes by significance, degree of confidence in the qualifications of respondents is highlighted. Algorithms for selecting the most important and relevant lists of competencies and disciplines in various professional training profiles are considered. Methods for designing networks that can be used to build classification rules, revealing hidden dependencies among professions and labor standards are discussed. © 2021 IEEE.

Keywords

Author Keywords

labor market APRIORI algorithm Professional competence Classification rules Skills and knowledge assessment

Index Keywords

Data mining Education systems Professional training Surveys Classification rules Data mining methods Degree of confidence Economic sectors Intelligent data minings Registration process
Author Affiliations
Faculty of Information Technology and Big Data Analysis, Financial University under the Government of the Russian Federation, Moscow, Russian Federation
Funding & Acknowledgements
No funding information
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