Student performance analysis using elitist teaching learning based optimization etlbo algorithm

Abstract

Educational information mining is rising field that spotlights on breaking down educational information to create models for enhancing learning encounters and enhancing institutional viability. Expanding enthusiasm for information mining and educational frameworks, make educational information mining as another developing exploration group. Educational Data Mining intends to remove the concealed learning from expansive Educational databases with the utilization of procedures and apparatuses. Educational Data Mining grows new techniques to find information from Educational database and it is utilized for basic decision making in Educational framework. The knowledge is hidden among the Educational informational Sets and it is extractable through data mining techniques. It is essential to think about and dissect Educational information particularly understudies execution. Educational Data Mining (EDM) is the field of study relates about mining Educational information to discover intriguing examples and learning in Educational associations. This investigation is similarly worried about this subject, particularly, the understudies execution. This study investigates numerous components theoretically expected to influence student s performance in higher education, and finds a subjective model which best classifies and predicts the student s performance in light of related individual and phenomenal elements. Predicting students grades has emerged as a major area of investigation in education due to the desire to identify the underlying factors that influence academic performance. Because of limited success in predicting the Grade Point Average (GPA), most of the prior research has focused on predicting grades in a specific set of classes based on students prior performances. In recent years, few optimization techniques such as Ant Colony Optimization, Grey-Wolf Optimization are developed for predicting student performance. This paper presents the performance analysis of a newly developed Elitist Teaching-Le