Machine Learning Algorithms in Educational Data Mining: Enhancing Student Outcomes
Year: 2026
Type: conference-paper
Abstract: As the field of machine learning continues to grow, the significance of machine learning in educational data mining which is promising in terms of contemporary pedagogies and the future of recommendations that will be networked to the needs of students grow. The application of our advanced algorithms will help educators and educational institutions to achieve supreme learning experience/outcome from the complex educational data. The effect of machine learning algorithms in education is very wide, and here we introduce a representative of the field of research in the education sector, and that is: A systematic Review on Educational Data Mining. Methods of machine learning include regression, clustering, and classification to analyse huge volumes of educational data. Using these methods of analysis, one can discover trends and patterns that are not revealed using traditional methods of analysis, like dividing students into groups depending on their needs and behavior by using clustering methods, or determining the courses in which the student is likely to succeed by using classification methods. Unlike the simple use of median reverberation properties, regression models allow one to proactively predict the academic success based on past data only. Personalized learning: the role of machine learning methods in this area is very important. By analysing the details about each particular student, these models will be able to improve the process of teaching and learning material in order to accommodate the individual needs of the student. Adaptive learning systems can rapidly change the level of difficulty of any class due to machine learning methods.
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7203707159