The rapid digitalization of education has increased the demand for data-driven approaches to evaluate and enhance the quality of teaching and learning. This article explores the development of scientific methods for assessing the effectiveness of the educational process through data science technologies, including machine learning, learning analytics, and educational data mining. By collecting and analyzing large-scale educational datasets, it becomes possible to identify hidden learning patterns, predict student performance, and design adaptive instructional strategies. The study highlights the integration of predictive analytics, data-driven decision-making systems, and automated feedback mechanisms for improving academic outcomes and instructional quality. Additionally, ethical issues related to data privacy, transparency, and responsible AI deployment in educational settings are discussed. The proposed methodological framework supports administrators, educators, and policymakers in developing efficient monitoring mechanisms, optimizing teaching practices, and fostering a personalized learning environment. The findings contribute to the ongoing global transition toward evidence-based education and demonstrate that the use of data science tools can significantly increase the effectiveness, fairness, and accessibility of the educational process.

Data science, machine learning, educational data mining

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