Using Data Mining Classifiers to Predict Academic Performance of High School Students
ავტორი: Yecheng Yao, Zebang Chen, Sumin Byun, Yizhu Liu
ორგანიზაცია: The University of Chicago, The University of California, Hankuk Academy of Foreign Studies, Pius XI Catholic High School
საკვანძო სიტყვები: Data Mining, Educational Data Mining, Classifier, High School Data Mining
აბსტრაქტი. 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
Sai Baba et al, "Student Performance Analysis Using Classification Techniques", International Journal of Pure and Applied Mathematics, Vol.115, No.5 2017, pp.1-7
Mohamed Ahmed et al., "Using Data Mining to Predict Instructor Performance", 12th International Conference on Application of Fuzzy Systems and Soft Computing, ICAFS 2016, pp. 137-142
Kalpana and Venkatalakshmi, "Intellectual Performance Analysis of Students by Using Data Mining Techniques", International Journal of Innovative Research in Science, Engineering and Technology, Vol.3, 2014.
Brijesh Kumar Baradwaj and Saurabh Pal, “Mining Educational Data to Analyze Students Performance”, International Journal of Advanced Computer Science and Applications”, Vol. 2, No. 6, 2011.
Kalpesh et al., "Student Performance Prediction System using Data Mining Approach", International Journal of Advanced Research in Computer and Communication Engineering, Vol.6, Issue 3, 2017.
Tair and El-Halees 2012. Mining Educational Data to Improve Student’s performance: A Case Study. International Journal of Information and Communication Technology Research.
P. Cortex and A. Silva. Using data mining to predict secondary school student performance
M. Jovanovic, M. Vukicevic, M. Milovanovic and M. Minovic (2012). Using data mining on student behaviour and cognitive style data for improving e-learning systems: a case study. International Journal of Computational Intelligence Systems
U. K. Pandey and S. Pal, Data Mining: A Prediction of performer or under performer using classification , (IJCSIT), International Journal of Computer Science and Information Technology, Vol 2(2), pp. 686-690.
S. T. Hijazi and R.S.M.M. Naqvi, “Factors affecting students performance: A case of Private Colleges”. Bangladesh e-Journal of Sociology, Vol. 3 No.1, 2006
Z. N. Khan , “Scholastic achievement of higher secondary students in science stream”, Journal of Social Sciences, Vol. 1. No. 2 , pp. 84-87, 2005.
U. K. Pandey and S. Pal , “ A Data mining view on class room teaching language”, (IJCSI), International Journal of Computer Science Issue, Vol. 8, Issue 2, pp. 277-282, 2011
M. Bray, The Shadow education system : Private tutoring and its implications for planners, (2nd edition), UNESCO, PARIS, France, 2007
Sheela Ayesha et. al , “Data mining model for higher education system”, European Journal of Scientific Research”, Vol. 43, No.1. pp. 24-29, 2010.
Q. A. AI-Radaideh, et. al., “ Mining Student data using decision trees”, International Arab Conference on Information Technology (ACIT2006), Yarmouk University, Jordan, 2006