Data mining approach to predict academic performance of students

Abstract

Powerful data mining techniques are available in a variety of educational fields. Educational research is advancing rapidly due to the vast amount of student data that can be used to create insightful patterns related to student learning. 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 decisive and 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 improving administrative and teaching staff in academic institutions. In the put-forwarded approach, the collected data set is preprocessed to ensure data quality and labeled student education data is used to apply ANN classifiers, support vector classifiers, random forests, and DT Compute and train a classifier. The achievement of the four classifications is measured by accuracy value, receiver operating curve (ROC), F1 score, and confusion matrix scored by each model. Finally, we found that the top three algorithmic models had an accuracy of 86–95%, an F1 score of 85–95%, and an average area under ROC curve of OVA of 98–99.6%.