Comparing Correlation-Based Feature Selection and Symmetrical Uncertainty for Student Dropout Prediction

Comparing Correlation-Based Feature Selection and Symmetrical Uncertainty for Student Dropout Prediction

educational data mining; performance evaluation; correlation-based feature selection; symmetrical uncertainty

Predicting student dropout is essential for universities dealing with high attrition rates.This study compares two feature selection (FS) methods—correlation-based feature selection (CFS) and symmetrical uncertainty (SU)—in educational data mining for dropout prediction. We evaluate these methods using three classification algorithms: decision tree (DT), support vector machine(SVM), and naive Bayes(NB). Results show that SU outperforms CFS overall, with SVM achieving the highest accuracy (98.16%) when combined with SUMoreover, this study identifies total credits in the fourth semester, cumulative GPA, gender, and student domicile as key predictors of student dropout.This study shows how using feature selection methods can improve the accuracy of predicting student dropout, helping educational institutions retain students better

Haryono Setiadi1*, Indah Paksi Larasati2, Esti Suryani3, Dewi Wisnu Wardani4

https://jurnal.iaii.or.id/index.php/RESTI/article/view/5911/959

Research Group Data Information Knowledge and Engineering, Department of Informatics, Universitas Sebelas Maret, Surakarta, Indonesia

26-08-2024

FAJAR BAGUS W

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