При цитировании ссылка на журнал «Интеграция образования Integration of Education» обязательна. Полное или частичное воспроизведение в СМИ материалов, опубликованных в журнале, допускается только с разрешения редакции ИНТЕГРАЦИЯ ОБРАЗОВАНИЯ INTEGRATION OF EDUCATION

Introduction. The article is concerned with the use of special electronic teaching tools to increase the students’ understanding of the subject and adaptation to the professional language environment of the host country, taking into account the mathematical education. Our purpose is to develop a methodology of multilingual support of mathematical courses in the host country to improve the effectiveness of students’ academic mobility using e-learning tools. Materials and Methods. The basis of the research was methods of system analysis and descriptive and analytical methods, primarily experimental. To identify advantages of the proposed approach the methods of empirical research were used (observation and comparison). To prove the efficiency, classical methods of measurement were used. Results. We analyzed the existing electronic learning environments and defined an e-learning environment Math-Bridge that allows for creating mathematical courses in several languages in parallel. For example, the e-training course “Optimization Methods” was developed in three languages for training Russian-speaking Master programme students. The comparative analysis of the target and control student’s groups showed that 100 % of the students in the target group achieved an excellent level of mastering competencies, while the control group has only 75 %. For the control group, the degree of motivation to mathematical studying has not virtually changed (increase by 0,86 %). In the target group the level of student interest to the mathematics increased from 0,9 % to 8,9 % (mean 2.21 %). Discussion and Conclusion. The results described in the article will be useful for the staff of international departments, administrations and deans, as well as teachers of those universities that participate in the students’ international academic mobility programmes.

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Introduction. At the present time, more and more students are changing either their field of study or the university in the process of studying. This raises the problem of how to determine whether a student’s level of knowledge meets the host institution’s criteria. A simple comparison of competencies is not enough. Therefore, the authors propose a new system of comparing existing and required knowledge (competencies) at the new place of study. The purpose of this article is to present the results of research on the development and practical application of specific “competency trees” that allow for the automatic comparison and re-crediting of disciplines. Materials and Methods. The research is based on the methods of system analysis for weakly formalized problems: the method of expert evaluations and the method of the goal tree. For direct development the method of construction of binary decision trees was used. To evaluate the effectiveness of the developed method, methods of observation and comparison were used. Results. This article describes the specific steps for creating checklists based on multilevel competency indicator trees. The tables describe the four levels of competency acquisition. Based on the experiments carried out on the use of such tables for retake disciplines when transferring a student from one specialty to another, the following recommendations are made: if it is necessary to obtain a mark of the “Test” type in the Host University, the comparison is made according to the second level indicators; if it is necessary to obtain a mark of the type “Graded test/Test with a grade” in the Host University, the comparison is made according to the third level indicators; if it is necessary to obtain a mark of the “Exam” type in the Host University, the comparison is made according to the indicators of the deepest level for this indicator of the first level. The technique has been successfully tested for moving of a student within Kazan National Research Technical University named after A. N. Tupolev-KAI between the academic programs Aircraft Engineering and Applied Mathematics and Informatics. Discussion and Conclusion. The proposed multilevel system of interuniversity indicators will significantly simplify the procedure for transferring subjects for students who are moved from one study program to another at any level – whether within one university, or between different universities of the Russian Federation. The use of an automated system for comparing the level of knowledge of a student when moving from one university to another will not only reduce the time of a student and teachers, but also eliminate the human factor, bias and subjectivity in the process of making decisions about transferring, and increase the transparency of this process. All this together will contribute to the development of academic mobility of students, increasing their competitiveness in the labor market and strengthening academic interuniversity relationships both in Russia and abroad.

В статье описаны методы принятия решений на основе алгоритмов интеллектуального обучения, для построения которых используются вербальные элементы. Такие алгоритмы и методы обычно работают в расчетах со строго количественными данными, однако, принимая во внимание человеческий способ восприятия информации в вербальной форме. Человек не принимает непосредственного участия в процессе построения модели, то есть ее структура не зависит от экспертных или иных человеческих мнений, однако качественная вербальная информация (например, элементы нормативных актов, документов, приказов и т. д.) встраивается в алгоритм в закодированной форме. Представлены вычислительные эксперименты. The article describes decision-making methods based on intelligent learning algorithms; for the construction of which verbal elements are used. Such algorithms and methods usually work in calculations with strictly quantitative data; however; taking into account the human way of perceiving information in verbal form. The person does not directly participate in the process of building the model; that is; its structure does not depend on expert or other human opinions; however; high-quality verbal information (for example; elements of regulations; documents; orders; etc.) is embedded in the algorithm in coded form. Computational experiments are presented.