Data mining approach to predict academic performanceof students

Abstract

Powerful data mining techniques are available in a variety of educational fields. Educational research isadvancing rapidly due to the vast amount of student data that can be used to create insightful patternsrelated to student learning. Educational data mining is a tool that helps universities assess and identify studentperformance. Well-known classification techniques have been widely used to determine student success indata mining. A decisive and growing exploration area in educational data mining (EDM) is predicting studentacademic performance. This area uses data mining and automaton learning approaches to extract data fromeducation repositories. According to relevant research, there are several academic performance predictionmethods aimed at improving administrative and teaching staff in academic institutions. In the put-forwardedapproach, the collected data set is preprocessed to ensure data quality and labeled student education datais used to apply ANN classifiers, support vector classifiers, random forests, and DT Compute and train aclassifier. 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 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%.