The paper develops a structural model for building a system for developing methodological competence. The structural model is built based on a service-oriented approach to developing large software complexes and includes six subsystems. Each subsystem is implemented as a separate microservice, which allows system scaling. The paper describes a technique that allows you to build a teacher’s competency map and evaluate its eight components: cognitive, didactic, project, informational, communication, reflective, monitoring, and personal-motivational. A four-level scale is proposed for assessing the level of competencies. A methodology for evaluating the effectiveness of the methodical competence development system based on the hierarchical expert method has been developed. The system was verified and implemented. According to the results of the system implementation, the intensification of the educational process and the improvement of the training quality of students were recorded. Students’ success in studying subjects has increased significantly. Namely, the number of bad students has decreased by 15 %, the number of «excellent» students has increased by 10 %, and the number of «good» students has increased by 18 %. The paper touches upon creating information technologies adequate for improving the higher education system. The goal is the targeted development and implementation of information technologies in educational institutions and the creation and operation of integrated, flexible software to support a mixed system for organizing the educational process, as well as increasing the efficiency of implementing a mixed system for organizing the educational process.

The life quality of populations, especially in large agglomerations, is significantly reduced due to air pollution. Major sources of pollution include motor vehicles, industrial facilities and the burning of fossil fuels. A particularly significant source of pollution is thermal power plants and coal-fired power plants, which are widely used in developing countries. The Astana city in the Republic of Kazakhstan is a fast-growing agglomeration where air pollution is compounded by intensive construction and the use of coal for heating. The research is important for the development of urbanism in terms of ensuring the sustainable development of urban agglomerations, which are growing rapidly. Long memory in time series of concentrations of air pollutants (particulate matter PM10, PM2.5) from four stations in Astana using the fractal R/S analysis method was studied. The Hurst exponents for the studied stations are 0.723; 0.548; 0.442 and 0.462. In addition, the behavior of the Hurst exponent in dynamics is studied by the flow window method based on R/S analysis. As a result, it was found that the pollution indicators of one of the stations are characterized by the presence of long-term memory and the time series is persistent. According to the analysis of recordings from the second station, the series is defined as close to random, and for stations 3 and 4, anti-persistence is characteristic. The calculated Hurst exponent values explain the sharp increase in pollution levels in October 2021. The reason for the increase in polluting substances concentration in the air is the close location of thermal power plants to the city. The method of time series fractal analysis can be the ecological state indicator in the corresponding region. Persistent pollution time series can be used to predict the occurrence of a critical pollution level. One of the reasons for anti-persistence or the occurrence of a temporary contamination level may be the close location of the observation station to the source of contamination. The obtained results indicate that the fractal time series analysis method can be an indicator of the ecological state in the relevant region.

This paper describes the basic conceptual apparatus required to form information spaces for scientific activity subjects. Multiple models have been built to identify collective and individual scientific activity subjects, including information on the subjects' publication citations, their abstracts, as well as their indicators in scientometric databases, etc. A conceptual scheme of interaction between collective and individual scientific activity subjects has been described, taking into consideration the dynamics of their productivity. A method has been proposed to form the information spaces for the collective and individual scientific activity subjects such as higher education establishments and scientists. The method involves a series of stages to identify and construct citation and scientific cooperation networks, to form subject scientific spaces, and, based on them, to devise methods in order to quantify productivity. The results of methods application form the components of the relevant information spaces of scientific activity subjects. The spaces to be built could be used to solve the task of selecting subjects for the implementation of joint scientific and educational projects. In addition, these spaces could be applied to form the organizational and functional framework of the collective scientific activity subjects, including their structural units, which would contribute to ensuring their stable development. Creating the information spaces of scientific activity subjects underlies resolving those issues that would stimulate investment in research and innovation, strengthen cooperation between universities, improve the efficiency and productivity of the scientific enterprise. It has been confirmed experimentally that the potential of a collective subject of scientific activity, including individual subjects, the rate of change of identifiers of whom is positive, would have a non-negative potential. A rate of change in the normalized indicators of identifiers of individual and collective scientific activity subjects has been calculated for the period from January 2019 to December 2020 for three higher education establishments

This study presents an advanced hybrid approach for detecting near-duplicate texts in the Kazakh language, addressing the specific challenges posed by its agglutinative morphology. The proposed method combines statistical and semantic techniques, including N-gram analysis, TF-IDF, LSH, LSA, and LDA, and is benchmarked against the bert-base-multilingual-cased model. Experiments were conducted on the purpose-built Arailym-aitu/KazakhTextDuplicates corpus, which contains over 25,000 manually modified text fragments using typical techniques, such as paraphrasing, word order changes, synonym substitution, and morphological transformations. The results show that the hybrid model achieves a precision of 1.00, a recall of 0.73, and an F1-score of 0.84, significantly outperforming traditional N-gram and TF-IDF approaches and demonstrating comparable accuracy to the BERT model while requiring substantially lower computational resources. The hybrid model proved highly effective in detecting various types of near-duplicate texts, including paraphrased and structurally modified content, making it suitable for practical applications in academic integrity verification, plagiarism detection, and intelligent text analysis. Moreover, this study highlights the potential of lightweight hybrid architectures as a practical alternative to large transformer-based models, particularly for languages with limited annotated corpora and linguistic resources. It lays the foundation for future research in cross-lingual duplicate detection and deep model adaptation for the Kazakh language.

The purpose of this study is to identify the problems of the quality assurance system of higher education in the Republic of Kazakhstan. To do this, the opinions of the stakeholders (students, teaching staff, employers, administrative, and managerial staff) in the process were screened. The data obtained were compared with the results of independent international ratings and other parameters. For screening, field studies were conducted using a questionnaire for students, teachers, non-academic employees of universities, and employers covering all regions of Kazakhstan in terms of quality assurance components. Based on the constructivist approach, the answers were interpreted to identify the problems of the quality assurance system in the understanding of the study participants. This study revealed that participants rate the quality of the content at a below-average level, the quality of the contingent at an average level, the quality of staff at a lower-than-average level, and the quality of infrastructure at a low level. Comparing the results of the survey and interviewing with the analysis of the results of entrance exams and ratings made it possible to identify the most pressing problems in ensuring the quality of educational programs, contingent, staff, and infrastructure of universities and draw conclusions about its improvement. The obtained results will help to customize the system of quality assurance of higher and postgraduate education, thus contributing to the training of highly competitive specialists for the country’s economy.

Considering the increased interest in ensuring the well-being of a person, lifelong learning takes a leading place in society. The purpose of this study is to build the concept of the LLL system for the Republic of Kazakhstan based on the methodology of education and international best practices, as well as global trends in the development of education. The key idea of the study is to justify the LLL system, which provides coverage of the country's population with formal, non-formal and informal learning to increase its competitiveness and basic competencies to the level of the OECD countries. To do this, we propose mechanisms that allow us to fully recognize the learning outcomes of formal, non-formal and informal education. These ideas were proposed by the authors to the Kazakhstan’s Government on the development of the Concept of Lifelong Learning.