An Intelligent Clustering Technique for Analysing the Performance of Students during Lockdown Period of Covid-19

Abstract

Corona virus or simply Corona is the current leading pandemic of the world. It has affected students and their in education in higher numbers than any other sector putting them into a depression. Hence this research attempts to suggest solutions for reducing depression amongst students amidst the pandemic. This work proposes ESVMs (Enhanced Support Vector Machines) model for its predictions. Identifying student performances is complex issue as the numbers are voluminous and hence the objective of this research is to assess student performance prediction model by using an efficient clustering method. Missing values and irrelevant data are resolved in this work using SCCs (Statistical correlation Coefficients) which work on subject wise manner or student wise data. This work also provides a novel solution for data pre-processing. IFCM (Improved Fuzzy C-means clustering) proposed in this work identifies high quality clusters with robustness. Further, the use of PSO (Particle Swarm Optimization) in feature selections improves its efficiency of the given data. Classifications are executed by the proposed ESVMs which predicts student's grade with accuracy. The evaluation results of this study improve classification accuracy significantly when compared to existing prediction models.