Using Data Mining Classifiers to Predict Academic Performance of High School Students
Автор: Yecheng Yao, Zebang Chen, Sumin Byun, Yizhu Liu
Организация: The University of Chicago, The University of California, Hankuk Academy of Foreign Studies, Pius XI Catholic High School
Ключевые слова: Data Mining, Educational Data Mining, Classifier, High School Data Mining
Аннотация. The use of data mining techniques for educational datasets is being referred to aseducational data mining. This study uses popular classifier algorithms in data mining with secondaryschool student data to estimate their success rate. Student success depends on various factors relatedto the student’s personal, family and surrounding environment, among others. This study’s dataset hasattributes related to parental education, job information, student travel time, study time, financialstatus, extracurricular activities, access to the Internet, family relationship, alcoholic consumption,student health condition, regular school attendance. This study analyzes the correlations betweenthese attributes and identifies the attributes that contribute to students’ test achievement for betterprediction and management of student performance. The study also compares the performance of topclassification algorithms in data mining and concludes J48 classifier and oneR to outperform the otherclassifiers
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