DIAGNOSTIC-TECHNOLOGICAL APPROACH TO EDUCATIONAL PROCESS MANAGEMENT

Abstract

Introduction. The effectiveness of educational process management largely depends on the quality of data. These data are obtained from electronic gradebooks, learning platforms, internal monitoring systems, and digital services. They often remain fragmented and poorly coordinated. Weak links between statistical indicators and actual managerial actions complicate analysis. Administrators receive large volumes of information. However, a holistic understanding of the state of the educational process is formed only occasionally.

Aim. The aim of the study is to substantiate a diagnostic and technological approach to educational process management.

Materials and Methods. The study is based on scientific publications in the fields of educational management, pedagogical monitoring, digital transformation, learning analytics, and the application of artificial intelligence. The methodological framework includes systemic, process-based, and diagnostic approaches. A structural and analytical method was used to construct the system of indicators. Data normalization and the calculation of the integral index were performed using qualimetric evaluation and weighted aggregation methods. The dynamics of changes were analyzed by comparing results before and after the implementation of the model.

Results. The study developed a matrix of diagnostic criteria. It includes academic performance, attendance, competency development, student satisfaction, teachers’ digital activity, curriculum implementation, and the timeliness of assessment. A structural and functional model of diagnostic and technological management was designed. The model contains modules for data integration, indicator standardization, analytical interpretation, decision support, and repeated diagnostics. Empirical modeling demonstrated an increase in the integral index from 0.742 to 0.881. Teachers’ digital activity increased by 44.3%. The number of critical deviations decreased by 77.8%.

Prospects. Further research should focus on adapting the proposed model to different educational programs. It is also advisable to integrate predictive artificial intelligence algorithms to enhance analytical capabilities.