STUDY OF SIGNS OF IMPACT ON THE QUALITY OF EDUCATION BY ML

Abstract

Use of machine learning (ML) algorithms to analyze and identify signs that affect the quality of education opens up new opportunities for individualization of education, optimization of educational processes and improvement of educational results of students.

The personalization of education stands as a paramount trend in contemporary learning. Each student possesses distinct requirements, passions, and talents. By scrutinizing the factors impacting educational excellence, we can pinpoint individual elements that wield substantial influence over each student's success. Consequently, this enables the crafting of customized educational programs and techniques finely tuned to the unique needs and aptitudes of every student.

The objective of this endeavor is to construct a system employing machine learning algorithms that can discern the factors influencing the assessment of students' educational quality. These facets render the research pertinent and noteworthy within the landscape of modern education. It offers a gateway to a deeper comprehension of the learning processes, streamlining educational procedures, and ultimately yielding improved outcomes in student education.