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dc.contributor.authorGoncharenko, Svitlana-
dc.date.accessioned2025-11-05T10:39:30Z-
dc.date.available2025-11-05T10:39:30Z-
dc.date.issued2025-10-06-
dc.identifier.citationGoncharenko S. Big data & ML in contemporary education / S. Goncharenko // Science: Development and Factors its Influence : Proceedings of the 6th International Scientific and Practical Conference (October 6-8, 2025; Amsterdam, Netherlands). - Amsterdam: Scientific Collection «InterConf», 2025. - P. 34-37.uk
dc.identifier.urihttps://er.knutd.edu.ua/handle/123456789/31652-
dc.description.abstractIn the context of digitalization and globalization, education is experiencing significant transformations that demand new approaches to teaching, learning, and administration. Conventional methods often cannot adequately address the complexity of educational processes, the variety of learners’ needs, and the rising expectations for quality and adaptability. Big Data technologies provide a powerful framework for tackling these challenges by enabling the collection, integration, and analysis of vast and diverse datasets [1], [2]. Through advanced analytics, Big Data supports the identification of hidden correlations, forecasting performance, and the development of personalized and flexible models [3], [4]. In addition, it drives educational innovation, supports research activities, and strengthens institutional competitiveness in a knowledge-driven economy. Consequently, the application of Big Data in education signifies not only the adoption of a modern technological tool but also a shift in educational philosophy, where data-driven insights become the foundation for effective knowledge management and progressive pedagogical practices. As stated above, the modern education system is in a state of rapid digital transformation, which requires the use of innovative methods of analysis and management of educational processes. The growing volumes of information created by students, teachers and educational platforms create the need to use Big Data for effective analysis and making informed decisions. Machine learning methods, both classical [5], [6] and based on neural networks [7], [8], allow processing this data, predicting results, assessing effectiveness and creating personalized trajectories of nf proposals. The integration of Big Data with various machine learning approaches opens up new prospects for increasing the adaptability and competitiveness of the modern economy and public administration within the framework of the further creation of appropriate knowledge-based & data-driven artificial intelligence systems [9], [10].uk
dc.language.isoenuk
dc.publisherScientific Collection «InterConf»uk
dc.subjectpersonalized and flexible modelsuk
dc.subjectdigitalization and globalizationuk
dc.subjectquality and adaptabilityuk
dc.titleBig data & ML in contemporary educationuk
dc.title.alternativeВеликі об'єми даних та машинне навчання в сучасній освітіuk
dc.typeThesisuk
local.contributor.altauthorГончаренко, Світлана Миколаївна-
local.subject.sectionСоціально-гуманітарні наукиuk
local.subject.facultyНавчально-науковий інститут культури і креативних індустрійuk
local.subject.departmentКафедра філології та перекладу (ФП)uk
local.conference.locationAmsterdam, Netherlandsuk
local.conference.date2025-10-06-
local.conference.nameScience: Development and Factors its Influenceuk
local.subject.method1uk
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