Using Data Mining Classifiers to Predict Academic Performance of High School Students

Authors: Yecheng Yao, Zebang Chen, Sumin Byun, Yizhu Liu
Affiliation: The University of Chicago, The University of California, Hankuk Academy of Foreign Studies, Pius XI Catholic High School


Keywords: Data Mining, Educational Data Mining, Classifier, High School Data Mining
ABSTRACT. The use of data mining techniques for educational datasets is being referred to as educational data mining. This study uses popular classifier algorithms in data mining with secondary school student data to estimate their success rate. Student success depends on various factors related to the student’s personal, family and surrounding environment, among others. This study’s dataset has attributes related to parental education, job information, student travel time, study time, financial status, extracurricular activities, access to the Internet, family relationship, alcoholic consumption, student health condition, regular school attendance. This study analyzes the correlations between these attributes and identifies the attributes that contribute to students’ test achievement for better prediction and management of student performance. The study also compares the performance of top classification algorithms in data mining and concludes J48 classifier and oneR to outperform the other classifiers


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