A Competency Learning and Student Centric Predictive Model for Evaluating Student Performance using Ensemble Learning

Ensemble learning is used to create a competency learning and predictive model which is based on the student-centric and it offers a unique way of evaluating student performance. The model seeks to improve the accuracy prediction and resilience of student outcomes with the assistance of ensemble learning. Algorithms like Random Forest, AdaBoost, Gradient Boosting, and Extra Trees provides an allencompassing structure for evaluating student performance according to diverse academic and geographic aspects. In order to improve the results of the student, the study focus on handling the difficulties associated with predicting student requirements and customizing their education based on their response. The model offers vision to make a student succeed by an intense examination of student data, assisting educational institutions in making precise decisions on student admissions and future plans. This study demonstrate how effective ensemble learning is at improving the accuracy of predicting the performance of the student and improving the results of the student.