Data mining approach to predict academic performanceof students

Abstract

Powerful data mining techniques are available in a variety of educational fields. Educational research is advancingrapidly due to the vast amount of student data that can be used to create insightful patterns related to studentlearning. Educational data mining is a tool that helps universities assess and identify student performance. Well-known classification techniques have been widely used to determine student success in data mining. A decisiveand growing exploration area in educational data mining (EDM) is predicting student academic performance.This area uses data mining and automaton learning approaches to extract data from education repositories.According to relevant research, there are several academic performance prediction methods aimed at improvingadministrative and teaching staff in academic institutions.In the put-forwarded approach, the collected data set is preprocessed to ensure data quality and labeled studenteducation data is used to apply ANN classifiers, support vector classifiers, random forests, and DT Compute andtrain a classifier. The achievement of the four classifications is measured by accuracy value, receiver operatingcurve (ROC), F1 score, and confusion matrix scored by each model. Finally, we found that the top three algorithmicmodels had an accuracy of 86–95%, an F1 score of 85–95%, and an average area under ROC curve ofOVA of 98–99.6%.