Intelligent approaches to designing cloud-oriented decision-support systems in education

Authors

DOI:

https://doi.org/10.18372/2073-4751.83.20545

Keywords:

decision-support system, microservice architecture, educational data analytics, MLOps lifecycle, adaptive learning systems

Abstract

The article presents a comprehensive approach to designing an intelligent cloud-oriented decision-support system (DSS) for the educational domain, combining the principles of microservice architecture, cognitive modelling, and machine-learning operations. The relevance of the study is justified by the growing volume of educational data, the need for personalized learning trajectories, and the necessity to maintain stable SLO indicators under dynamically changing learning conditions. The analysis of current research highlights the limitations of traditional monolithic LMS platforms, which lack sufficient flexibility, scalability, and capabilities for integrating intelligent models.
The proposed system architecture is based on event-driven microservice interaction via Kafka and incorporates modules for collecting and normalizing learning events, a feature-engineering subsystem, recommendation and risk-prediction services, and a dedicated pipeline for modelling and monitoring ML components. The results of the study demonstrate that combining microservice decomposition with intelligent data-analysis methods improves recommendation accuracy, enhances performance indicators, and ensures the resilience of the educational platform under high load. The presented architecture can serve as a foundation for building scalable and adaptive next-generation educational ecosystems.

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Published

2025-12-19

Issue

Vol. 3 No. 83 (2025)

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