Exploring Machine Learning’s Role in Education: A Comprehensive Review and Academic Implications

Behind the academic institution, it is a competitive environment in which there is a question of how to best represent the quality of education produced, what system of evaluation of students’ productivity best suited, and how to best predict future education needs. With the recent developments in the computerization of alongwith other methods data management, the rapidly expanding educational field is becoming more and more interested in finding the novel machine learning (ML) applications into educational Data Analysis. The purpose of this review is to in-depth critically assess the existing literature on the ML application to the educational field. The target is to search for the trend in this field and to define which way tends to give better results. The review focuses on the ML algorithms, examining their effectiveness in predicting academic performance, creating space for adaptive learning formats, and impacting other educational variables. The major objectives include better understanding of the existing gaps in research and opportunities to suggest relevant questions on the subject in the directions of future ML implementation development in education. Four main research questions include different ML approaches and algorithms used in educational data analyses, the role of features in ML modeling, types of educational data often considered in the study, the educational outcomes and changes associated with ML integration. By providing the thorough examination of those areas, the review is designed to present valuable implications for academic, student, and administrative practice, offering guidance for possible further studies in the field.