Machine Learning Methods and Qualimetric Approach to Determine the Conditions for Train Students in the Field of Environmental and Economic Activities
Artem Salamatov, Chelyabinsk State University ; Elena Gafarova, Vladimir Belevitin, South Ural State Humanitarian-Pedagogical University ; Maxim Gafarov, South Ural State University ; Darya Gordeeva, South Ural State Humanitarian-Pedagogical University
iJET Volume 16, Number 3, ISSN 1863-0383 Publisher: International Journal of Emerging Technology in Learning, Kassel, Germany
The relevance of environmental and economic activity requires professional training of specialists and, accordingly, new organizational and pedagogical conditions for effective education. It is also necessary to develop control and measuring materials that would have all the qualities (validity, reliability, consistency, significance and objectivity) to obtain the most reliable results in justifying the need and sufficiency of the identified conditions. The intensification of information processes in vocational education leads researchers to the need to find optimal conditions and tools to achieve pedagogical goals. Among these tools are machine learning methods and mathematical models built on their basis for quantitative assessment of the quality of vocational training in the field of environmental and economic activities. The use of the qualimetric approach in pedagogy is possible in the presence of a certain array of observational data for one or another criterion related to learning conditions, personal qualities of students, etc. The construction of an algorithmic model allows one to operate with conditions in mental experiments, test hypotheses, and since pedagogical research is quite long in time, the choice of conditions based on the most favorable forecast built using the model allows one to optimize pedagogical resources to achieve the planned results. Rational selection of effective control and measuring materials (CMMs) allows one to determine the need and sufficiency of organizational and pedagogical conditions. While mathematical modeling allows one to quickly adjust the organizational and pedagogical conditions as a set of opportunities for content, forms, teaching methods, information and communication technologies (ICTs) and CMMs used to achieve the planned educational results in the sphere of environmental and economic activity. Interpretation of the derived features in the context of the pedagogical research performed with a cross-validation accuracy of 72% made it possible to reveal the dominant significance of intersubjective connections between the disciplines studied by the sample of students in the bachelor's and master's programs. Namely, programs 44.03.04 and 44.04.04 "Professional training (by industry)", which are the most significant in terms of the formation of competence in the field of environmental and economic activities. The designed mathematical model of the Gradient Boosting Classifier allows making predictive expectations of the studied competency types and testing hypotheses for the inclusion or exclusion of certain significant organizational and pedagogical conditions for the effective implementation of the educational process. A necessary and sufficient organizational and pedagogical condition for the effective formation of competence in the field of environmental and economic activity is to ensure continuity between significant disciplines and the actualization of interdisciplinary relationships based on the development of interdisciplinary courses.
Salamatov, A., Gafarova, E., Belevitin, V., Gafarov, M. & Gordeeva, D. (2021). Machine Learning Methods and Qualimetric Approach to Determine the Conditions for Train Students in the Field of Environmental and Economic Activities. International Journal of Emerging Technologies in Learning (iJET), 16(3), 72-85. Kassel, Germany: International Journal of Emerging Technology in Learning.