Students performance analysis using machine learning

Abstract

Modern machine learning techniques create fresh footprints in the education landscape by utilizing predictive analytics for the student's performance assessment. Unlike standard assessments of academic performance which are rigid and subject to bias against the individual student, machine learning defines problems in predicting student performance and applies the different scales over these very narrow margins. Unstandardized assessment methods, the difference in the ways of learning by students, and feedback not given in real-time to guide assessments are among the factors that affect student performance. The data availability, feature selection, one of the most important challenges, and interpretation of the model are also identified as some of the most critical challenges. Also, these grading systems have the limitations that urge a shift toward automated, data-driven methods of clear improvements in predictive accuracy. Efficiency of prediction brought about by advanced machine learning techniques provides a basis on which one can forecast the academic performances of students. They use neural networks, decision trees and other ensemble methods through more efficient analysis. It also improves usage with precision by predicting academic results applicable to different student demography, besides being user-friendly. Establishing the prediction model of student performance will need a serious analysis of the prevailing challenges and also practical machine learning solutions. The study results will greatly contribute to improving the educational strategies, enhance the learning experiences of students, and optimize academic decision-making.