Using Ensemble StackingC Method and Base Classifiers to Ameliorate Prediction Accuracy of Pedagogical Data

摘要

Ensemble methods and conventional base class learners have effectively been applied in the realm of educational data mining to ameliorate the accuracy and consistency in prediction. Primarily in the contemporary study, researchers conducted empirical results on pedagogical real dataset acquired from University of Kashmir, using miscellaneous base classifiers viz. j48, random forest and random tree, to predict the performance of students. However, in the later phase, the pedagogical dataset was subjected to more proficient version of stacking viz. stackingC, with the principle objective to ameliorate the performance of students. Furthermore, the dataset was deployed with filtering procedures to corroborate any improvement in results, after the application of techniques such as synthetic minority oversampling technique (SMOTE) and spread sub-sampling method. Moreover, in case of ensemble stackingC, hybridization of predicted output was carried out with three base classifier vis-a- vis j48, random forest and random tree, and the classifier achieved paramount accuracy of 95.65% in predicting the actual class of students. The findings have by and large noticeably corroborated that the stackingC classifier, attained significant prediction accuracy of 95.96% when undergone through undersampling (spread sub-sampling) and 96.11% using oversampling (SMOTE). As a subject of corollary, it calls upon the researchers to broaden the canvas of literature by employing the analogous methods to uncover the diverse patterns hidden in academic datasets.