Scientific Journal of Astana IT University

10

Authors: Gulnaz Zunimova, Gulzhan Soltan, Dmitry Likhacheuski, Aisulu Ismailova, Nazym Issayeva

The importance of this research topic lies in the need to gather, store, analyze and disseminate accurate information on the management status of educational institutions to guarantee the provision of quality educational services. This is particularly crucial in the current trend towards digitalizing educational work and utilizing the internet to facilitate practical management tasks. The study aims to construct a case method model to supervise educational process management outcomes. For that purpose, the study scrutinises the implementation methodologies of existing case models to monitor and analyse the situation without human intervention. Also, the investigation entails creating an implementation algorithm and a simulation model of an interactive case method to monitor educational activities. The formalized logical-semantic apparatus is used to address the research problems. The algorithm development and simulation modeling enabled correlation of the obtained answer with the assigned task, automating the results output. Generating answers in the case method involves considering rules based on the base word, answer keywords, and constraints (true/false). These elements are part of the thesaurus specific to the survey domain and are included in the test's base vocabulary. To test an answer against a question, each inductive step is viewed through a logical formula. Logical statements such as conjunction, disjunction, logical negation, implication, and equivalence are represented by formulas. This method enables the evaluation of the respondent's answer based on the nodes within the statement tree integrated into the test. The progression from the initial word to the node-connection generates an automated assessment of individual educational process management standards. This study enables the enhancement of automated monitoring capabilities for the University's educational process results. The research suggests the potential for developing models and algorithms to form question sets and enable individualized assessments based on the respondent's performance. As a consequence, the devised algorithm and simulation model for the interactive case approach are showcased. During the testing, the key words and example words were compared with the responses of the participants and specific results were obtained. Additional case vocabulary terms need to be added to address the limitations encountered during the model testing and the testing period.

11

Authors: Batyrbek Bakytkereiuly, Andrii Biloshchytskyi, Dilara Abzhanova

The article assesses the outcomes of digitization on higher education in Kazakhstan. Given current trends, there is a need to establish a dedicated focus on the ongoing professional growth of educators. A crucial measure in this regard involves shifting from disjointed learning approaches to implementing a cohesive system for the continuous professional development of teaching staff. It is crucial to highlight that in accordance with the Development Concept of Higher Education and Science in the Republic of Kazakhstan the duration of an individual's active economic engagement has extended from 35-40 years to 50-60 years. This shift underscores the growing necessity for continuous learning throughout one's life and underscores the significance of non-formal education. The educator plays a pivotal role in influencing the educational landscape and significantly contributes to the outcomes of socio-economic changes in Kazakhstan. In the current era of socio-economic and digital transformations the teacher's proficiency has emerged as a crucial element that directly influences the quality of students' education. The Ministry of Digital Development, Innovation, and Aerospace Industry of Kazakhstan has reported a yearly requirement for approximately 30,000 additional IT professionals. Consequently, the focus is on enhancing the methodological expertise of IT discipline educators, recognizing their pivotal role in influencing the quality of education and student achievements. Information systems aimed at enhancing this competence are emerging as a crucial element in elevating the overall effectiveness of education. The article presents the rationale for the need to create a single space for additional professional education of teachers of IT disciplines. It is considered as an effective mechanism for the implementation of the state educational policy and innovation strategy in the field of education. The aim of this initiative is to ensure that the human resource is ready to successfully achieve the country's strategic goals in the context of digitalization processes. The article focuses on the development of a structural model of an information system aimed at improving the methodological competence of teachers of IT disciplines using the principles of continuing education. Based on a conceptual model with microservices subsystems, this methodology represents a comprehensive approach to the development of the competence of teachers of IT disciplines, integrating technological and methodological aspects. The development of a method for combining data in order to prevent duplication of records in the database of the information system is also considered. Methods of estimating the degree of similarity based on the types of attributes are proposed. The article extensively delineates the models, architecture, and components constituting the software implementation of the envisioned information system. It scrutinizes the accessibility of the employed data sources and formulates conclusions regarding the future potential for advancing the subject matter.

13

Authors: Samat Kabdygali, Ruslan Omirgaliyev, Timur Tursynbayev, Korhan Kayisli, Nurkhat Zhakiyev

The critical transformation of the energy sector demands innovative approaches to ensure the reliability and efficiency of energy systems. In this pursuit, this study delved into the potential of Deep Recurrent Neural Networks (DRNNs) for forecasting energy demand, using a comprehensive dataset detailing Kazakhstan's electrical consumption over a span of two years. Traditional statistical models have historically played a role in energy demand prediction, but the growing intricacy of the energy landscape calls for more advanced solutions. The paper presented a comparison of the DRNN with other traditional and machine learning models and highlighted the superior performance of DRNNs, especially in capturing complex temporal relationships. The energy sector is confronting unprecedented challenges due to population growth and the integration of diverse energy sources, leading to increased demand and system strains. Accurate energy demand prediction is essential for system reliability. Traditional models, though widely used, often overlook intricate variables like weather patterns and temporal factors. Through rigorous methodology, encompassing exploratory data analysis, feature engineering, and hyperparameter optimization, an optimized DRNN model was developed. The results demonstrated the DRNN's exceptional capability in processing complex time-series data, as evidenced by its attainment of an R-squared value of 83.6%. Additionally, it achieved Mean Absolute Errors and Root Mean Squared Errors of less than 2%. However, there were noticeable deviations in some predictions, suggesting areas for refinement. This research underscores the significance of DRNNs in energy demand prediction, highlighting their advantages over traditional models while also noting the need for ongoing optimization. The findings underscore DRNN's promise as a robust forecasting tool, pivotal for the energy sector's future resilience and efficiency.

15

Authors: Kenzhegali Nurgaliyev, Akylbek Tokhmetov, Liliya Tanchenko

The article is devoted to the study of existing technologies regarding Internet of Things (IoT) device interaction in a heterogeneous network. Since each smart home appliance can be controlled by a customer who aims to find a cost-effective and easy-to-connect product for their own connected home, there are certain functional limitations for devices from distinct manufacturers that may decrease the intention to merge them all into a single network. A variety of proprietary protocols and communication standards embedded by vendors make their products unable to interact with other vendor devices if the connection standard used is not identical. Also, an IoT product design refers to its own functionality, mainly excluding the possibility of integration into other existing infrastructure. As IoT equipment emerges on the market, the complexity of its connection to a heterogeneous network corresponds to the firmware and the standard unification according to modern demands. It means that potential users might face the necessity of overcoming these issues to achieve high performance in terms of network interoperability. In general, an IoT gateway operating as a middleware might have the potential to enable a network with distinct communication models to operate without failure or data loss. This task requires the received data to be converted into the format in which the data is intended. This paper includes a comparative analysis of existing IoT device interaction standards, connection protocols, and data transfer technologies, evaluating their features for an effective adoption of the proposed network architecture, which can be used to improve the interoperability of heterogeneous IoT devices.