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The ensemble approach such as boosting is based on heuristic system to develop prediction paradigms. The ensemble learning techniques are typically more accurate than individual classifiers to generate predictions. For these grounds, primarily in this study several ensemble techniques have been discussed to get a comprehensive knowledge of key methods. Among various ensemble approaches, researchers have practiced boosting mechanism to predict the performance of students. As application of ensemble methods is contemplated to be significant phenomenon in classification and prediction procedures, therefore the researchers exploited boosting technique to develop an accurate prediction pedagogical model, in view of the pronounced nature and novelty of the proposed method in educational data mining. The base classifiers including random tree, j48, knn and naïve bayes have been evaluated on 10- fold cross validation system. Moreover, filtering procedures such as oversampling (SMOTE) and under-sampling (Spread subsampling), have been exploited to further inspect any significant change in results among meta and base classifiers. Both ensemble and filtering approaches have demonstrated substantial improvement in predicting the performance of students than the application of conventional classifiers. Furthermore, based on the improvement in results two novel prediction models have been propounded after conducting performance analysis on each approach. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]