The educational optimization process is widely researched in the theoretical aspect. Analyzing existing sources made it possible to highlight the research issues: the need to create an optimizing distance studying model, in which work with weaker students becomes possible both within the educational process and in individual or group independent work. The study aims to develop an information-logical model for optimizing distance studying. This model should provide for the learning process organization in such a way as to strengthen the weaknesses of students, reveal their potential and focus on the comprehensive development of knowledge and skills. The task formalization is carried out using the Hungarian algorithm and Boolean variables. The main work limitation is the operation with integers. Discreteness manifests itself already at the modelling stages in many problems, for example, when working with Boolean variables. An example with the most straightforward information model construction using the logical functions "true/false" with the transition to a chain of matrices is given. It demonstrates the studying optimization algorithm and presents an expanded information-logical model. The presented model was preliminary tested in one of the academic groups of the S. Seifullin Kazakh Agro Technical Research University. Students' knowledge inspections were carried out at the approbation beginning. Then a student group working on a student project was divided into subgroups according to the algorithm. The knowledge inspection showed an 11.3% improvement in results at the work's end. Further research on this topic may consist of expanding the presented model's capabilities and developing appropriate modules for knowledge control and algorithmization of related tasks.

With the advent of new time requirements for the quality of educational services, which is influenced by management in functioning business processes, existing research in the field of resource allocation in the management of complex processes, namely the calculation of the teaching load of university teaching staff, was studied. The purpose of the research in this article is to develop functional and mathematical distribution models of the university educational work volume, as well as an algorithm for optimizing the generation of educational flows and initialization of academic groups, taking into account the specifics of disciplines and classroom fund. The algorithm is based on the construction of all business processes implemented during the formation of educational streams and groups. The functional model described for the process of distributing the volume of educational work includes the definition of the main functions, their relationships, input and output data, as well as the criteria and restrictions that govern this process. The mathematical model is based on the representation of all types of educational work of departments of educational programs as a discrete set of resources that must be distributed between educational departments in accordance with the assumptions and restrictions accepted at the university. Data mining and operations research techniques were used to write the functional model. Empirical and quantitative methods were used to write a mathematical model. Thus, a new methodology has been developed for solving complex optimization problems that arise when modeling and optimizing the distribution of the volume of educational work of a university. It should be noted that comparative experiments under labor-intensive and time-limited conditions confirm the effectiveness of this technique in solving problems of distributing the amount of educational work among departments of educational programs, which in turn contributes to the implementation of high-quality software.

Modern challenges of the digital transformation of education require the development of new approaches to assessing academic success and monitoring students' educational trajectories. This study presents a functional model of the data analytics system and visualization of the digital profile of a graduate of Astana IT University (AITU), based on the Integrated IGPA (Integrated Grade Point Average) indicator, which combines the academic, research, and social achievements of students. The aim of the work is to create a system of analytics, visualization, and interpretation of data reflecting the comprehensive development of students and their readiness for professional activity. The theoretical part examines modern approaches to educational analytics in higher education. A critical analysis of scientific sources, including research on learning analytics, educational data mining, and the formation of digital profiles of students, was carried out. The emphasis on technical aspects and insufficient connection with educational practice reveals the main limitations of the existing models. The empirical part uses anonymized data from AITU students for 2022–2024, covering the indicators of Grade Point Average (GPA), Indicators of Research-Oriented Study (iROS), and Social Competition Indicators (SSCI). Dashboards built with the help of Power BI made it possible to visualize and interpret educational trajectories. The use of machine learning algorithms (K-means clustering, PCA analysis) ensured the typologization of student profiles. Using Python and the scikit-learn, seaborn, and pandas’ libraries allowed us to deeply explore the relationships between IGPA components. The results of the study demonstrate the possibilities of personalized academic support, strategic management of educational processes, and increased transparency of student achievement. The developed model can serve as a basis for making managerial decisions and improving the quality of educational programs in the context of digital transformation. The proposed approach can be scaled and adapted to other educational institutions, regardless of their size and specialization. Flexibility in integrating additional indicators reflecting the unique goals and values of a particular educational environment facilitates the model's versatility.

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.

Evaluating Key Performance Indicators (KPIs) of faculty and staff is critical to ensuring accountability and promoting institutional effectiveness in higher education. However, the management of these processes often relies on manual, error-prone systems, creating significant administrative burdens. This study addresses these challenges by presenting a novel, replicable framework for translating complex institutional regulations into an automated, multi-stakeholder KPI management system. We detail the design and implementation of a web-based platform at Astana IT University, which was developed by programmatically encoding the institution's official KPI calculation and validation rules. The system features a multi-perspective analytical ecosystem, providing role-specific dashboards for faculty, review committees, department heads, and central administration to support synchronized decision-making. The core scientific contribution is a holistic methodology that combines stakeholder-driven requirements analysis with a "Policy-as-Code" approach to create a transparent, auditable, and scalable solution. Preliminary results indicate significant improvements in efficiency and data accuracy, demonstrating the framework's effectiveness. This study contributes not only a practical solution for KPI management but also a validated methodological blueprint for digital transformation applicable to other higher education institutions facing similar regulatory and administrative complexities. Future work will explore the integration of predictive analytics to enable early intervention in cases of underperformance. Additional modules such as goal-setting tools, peer comparison features, and customizable reporting templates are also planned to enhance usability and strategic planning capabilities. By fostering a data-driven culture and ensuring alignment with institutional goals, such systems can play a key role in long-term academic quality assurance and workforce development.