that includes initial data, system parameters, control parameters, disturbing parameters, and output parameters. Utilizing analogies to describe such a dynamic system enables a comprehensive understanding of the internal and external factors that influence the system and allows for the prediction of its response to changes in power dynamics. To accurately assess the quality of the education system, it is crucial to consider the relationship between its socio-economic impact and several key criteria: the system s purposefulness, hierarchical structure, interdependence with the external environment, level of autonomy and openness, reliability, and dimensional characteristics. An algorithm was developed, informed by data from the black, gray, and white box methods, to facilitate the transition between models aimed at enhancing the quality of education. The description of the system advances sequentially from the black box model to the gray box model, and ultimately to the white box model. Each model is capable of independently representing a specific set of input and output parameters, but the level of determinism in the descriptive process increases as one progresses from one model to the next. However, the degree of determinism in the descriptive process increases with the transition from the previous model to the next one. box model, quality of education, black box method, gray box method, white box method Information Technology and Implementation (IT&I-2024), November 20-21, 2024, Kyiv, Ukraine Corresponding author. These authors contributed equally.
In the field of engineering, physics, and other scientific has been widely employed to characterize material systems. A technical system is defined as a system that exhibits the ability to dynamically change over time in response to external & control parameters, and other influencing factors. Such systems are purposefully designed tasked with executing a predetermined set of functions. The functioning of such systems is described using a set of mathematical formulas, known as a mathematical model that can be supplemented by experimental findings in some instances.
A system can be deterministic when external influences, especially those that are random and lack a discernible pattern, are absent. However, applying the concept of a technical system to intangible entities presents certain challenges: the principles governing such entities can be subjective and stochastic, and they are often subject to numerous external disturbances. Despite these challenges, drawing parallels between dynamic systems and intangible entities enables a comprehensive description of both internal and external influences on a dynamic system, facilitating the prediction explaining the operation of dynamic education quality systems. Additionally, various methodologies can be employed to explore causal relationships within the dynamic education system, each differing in the openness of initial data, system parameters, control parameters, disturbances, and output parameters. These methodologies are consistent with the theory of testing technical systems and software products, commonly referred to as black, gray, and white box methods. When assessing the quality of an education system, it is crucial to examine the interplay between its socio-economic impact and several key criteria: the system s purposefulness, its hierarchical structure, the interdependence between the system and its external environment, the degree of autonomy and openness, the system s reliability, and its dimensional characteristics.
The application of black, gray, and white box models for describing dynamic systems extends beyond
technical engineering or computer systems and has been effectively utilized in various fields,
including cybernetics, economics, and other areas of knowledge. Andersson & Johansson [
Although the box method has been employed in the education sector, its use has primarily focused
on specific applications within the educational process, such as training, rather than on analyzing
management (control) processes. Shkarlet et al. [12] describe the development of the Quadruple Helix
model for solving the problems of the information economy. The studies [13-15] focus attention on
SDG (Sustainable Development Goals) in the context of environmental education. Papers [16-20] are
devoted to the analysis of the impact of modern technologies on the quality of higher education in
Ukraine.
within the educational sphere [21-30] and the increasing emphasis on achieving the SDG in education
[
A bibliometric analysis of literature sources obtained from the scientometric Scopus database (Figures 1-2, using the VOSviewer bibliometric analysis tool) reveals a growing interest in applying approaches that describe intangible systems by drawing analogies with technical systems.
[
Thus, on the basis of bibliometric analysis and literature review, it is possible to formulate the purpose of the article evaluation the quality of the education system by conceptualizing it through the black, gray, and white box methods, drawing analogies between physical (engineering) systems and intangible educational systems. In this context, it is imperative to assess how the design of the education system impacts the socio-economic development of an organization, region, or country.
Scopus scientometric database [
An algorithm for developing the facilitation of the transition between various models is used to assess the quality of the education system was constructed based on data obtained from the analysis of the black, gray, and white box methods. The algorithm is presented in the diagram in Figure 3.
The diagram (Figure 3) is accompanied by the following detailed description of each level within the three-level box model framework, specifically in relation to education system quality: 1) Black box: only inputs ( ) and outputs ( ); 2) Gray box: inputs ( ), control parameters ( ), and outputs ( ); 3) White box: inputs ( ), control parameters ( ), system parameters ( ), disturbing parameters ( ), and outputs ( ).
The system is characterized by a sequential progression from the black box model to the gray box model, and ultimately to the white box model. Each of these models can operate autonomously and effectively representing a specific set of input and output parameters. However, as the transition is made from the simpler to the more complex models, there is a corresponding increase in the determinism of the description process. Such layered approach enables progressively deeper insights into the internal structure and influencing factors of the system.
growth and the surrounding region.
Output parameters (Y) include: the ranking of the university s educational programs among employers; the average salary of graduates from the educational programs; the career advancement of graduates of the educational programs.
Input parameters (X) consist of: the range of educational programs offered; the availability of statefunded training opportunities for students; the cost of tuition; the presence of competing educational institutions within the region; the availability of practical training and internships opportunities; the list of potential employers. s economic
Disturbing parameters (Z) refer to external influences, including: changes in the policies of the Ministry of Education and Science of Ukraine, particularly those related to the financing of the activities; the impact of military activities on the national economy, particularly in war-affected regions.
The parameters of the K system can be represented as characteristics of the educational program
and its surrounding environment, especially when evaluated against similar educational programs
through benchmarking (Figure 4) or by using comparative data from rating agencies (Figure 5) such
as QS World University Rankings [
100 100 100 100 100 100 100 100 100 100 90 57 71 35 44 50 60 90 80
70 Fact
Target
Key observation from Figure 4: 1. Learning through research: the target reached 90% (good result). 2. Transparency and publicity: the target reached the lower level of 57%.
3. Internal quality assurance of the educational program: the target reached 71%, indicating strong internal quality assurance overall, though one perspective sees room for improvement.
4. Educational environment and material resources: the target reached a lower level of 35%, suggesting significant concerns about the educational environment and resources.
5. Human resources: the target reached 44%, indicating a perceived difference in the adequacy of human resources.
6. Control measures, evaluation of students, and academic integrity: the target reached 50%, showing a moderate gap. 7. Teaching and learning in the educational program: the target reached 60%, pointing to a discrepancy in perceived effectiveness in teaching and learning practices.
8. Access to the educational program and recognition of learning outcomes: the target reached 90% (good result).
9. The structure and content of the educational program: the target reached 80%, showing that the structure and content are viewed positively overall, though one perspective notes some areas for enhancement.
10. Design and objectives of the educational program: the target reached 70%.
Considering the above, some aspects such as transparency, human resources, and the educational environment being perceived as needing improvement.
3
4 2
2 4 4 4 4 4 4 4 4 4 4 4 4
4 3 3
3 Fact
Target Key observations from the Figure 5: 1. Educational environment and material resources: high or maximum rating (4 from 4). 2. Design and objectives of the educational program: high or maximum rating (4 from 4). 3. Transparency and publicity: high or maximum rating (4 from 4).
4. Control measures, evaluation of students and academic integrity: high or maximum rating (4 from 4).
5. Internal quality assurance of the educational program: 3 of 4 points (good level). 6. Teaching and learning in the educational program: 3 of 4 points (good level). 7. Access to the educational program and recognition of learning outcomes: 3 of 4 points (good level).
8. The structure and content of the educational program: 3 of 4 points (good level). 9. Human resources: 2 of 4 points (average level). 10. Learning through research: 2 of 4 points (average level).
The evaluation of various aspects of the educational program reveals that several areas, including the educational environment and material resources, design and objectives of the educational program, transparency and publicity, and control measures and academic integrity, received the highest possible rating. Other areas, such as internal quality assurance, teaching and learning, access to the program and recognition of learning outcomes, and the structure and content of the program, were rated at a good level. However, human resources and learning through research were rated at an average level.
Parameters of control U can be displayed on a radial diagram with an assessment of the degree of the impact on the output parameter on a scale from 1 to 10 points (Figure 6).
quality of infrastructure
grant funding government funding 10 8 6 4 2 0 popularity of the educational program funding from the creation of developments and the provision of services ranking position international partnership
Key observation form Figure 6: 1. Government funding: the score for government funding is at 5, indicating a moderate level of government support.
2. Grant funding: this parameter is also rated at 5, suggesting a similar level of support from grants as from government funding.
3. Funding from the creation of developments and provision of services: this parameter has a 7 score.
4. International partnership: this parameter is rated very low 4, suggesting that international partnerships are limited.
6. Ranking position: the ranking position is rated around 6. 7. Popularity of the educational program: this parameter has an 8 score (the higher result). 8. Quality of infrastructure: this parameter has a higher score of 5.
Overall, while the program shows strengths in areas such as service-based funding and popularity, it has moderate government and grant support, and there is a clear need for improvement in international partnerships.
The proposed algorithm for the sequential description of the quality of education system and its socio-economic impact allows for several key outcomes: 1) establishing a clear set of indicators that define the influence of input, control, and system parameters on output parameters, thereby enabling the prediction of their changes; 2) developing mechanisms to enhance the system by adjusting control parameters to increase the value of output parameters; 3) evaluating the current state of system parameters and identifying potential areas for improvement; 4) predicting the system's behavior under the influence of external disturbances; 5) creating a roadmap to achieve desired output parameters at operational (situational), tactical, and strategic levels.
The novelty of this study lies in the application of the technical system approach typically used in engineering and physical sciences to the assessment of intangible systems, specifically the quality of education. Through the deployment of the black, gray, and white box models, the study innovatively conceptualizes the education system as a dynamic entity with inputs, control parameters, and external disturbances, allowing for a systematic evaluation of its impact on socioeconomic development.
-03-V01-03-V04-00522/2024/VA) and a grant from the Ministry of Education and Science of Ukraine under project Modeling and forecasting of socioeconomic consequences of higher education and science reforms in wartime (No. 0124U000545).
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