PYTHON AND BIG DATA TECHNOLOGIES FOR DEVELOPING ADAPTIVE SMART EDUCATIONAL PLATFORMS

Abstract

Digital transformation in education stimulates the development of analytical platforms capable of processing large volumes of data on learning activities. This article explores the application of Python and the Big Data technology stack for building adaptive educational environments. The main focus is on demonstrating machine learning methods that enable personalized learning pathways through the analysis of students' behavioral patterns. The presented algorithms and implementation cases will be useful for developers of educational solutions.

Additionally, the article highlights the role of data-driven approaches in improving the quality of education through real-time analytics, predictive modeling, and automated feedback systems. Special attention is given to the integration of open-source tools, such as Python libraries and distributed data-processing technologies, which make scalable educational analytics accessible even for institutions with limited resources. The research emphasizes the importance of using Big Data not only for monitoring academic performance but also for designing adaptive curricula that respond to learners’ cognitive needs, engagement levels, and preferred learning strategies. The findings contribute to the methodological foundation for creating intelligent educational platforms and underscore the potential of Big Data technologies in shaping the future of personalized learning.