Evaluating Faculty Performance Using Ensemble Techniques

Abstract

Applications of Data Mining techniques are increasing on a rapid pace in the area of Higher Education. Faculty Performance Evaluation is a main concern in today’s scenario as the vast amount of data is generated by educational institutes, which contains valuable hidden information. Though, Data Mining techniques have already applied to evaluate the faculty performance but no technique is found adequate in accuracy. In this research, more than one data mining techniques are ensembled in an order for achieving better accuracy rates. Here, we have applied Boosting (Ensemble) Feature Stacking, Random Forest and Generalized Boosted Model which are based on base learners Decision Tree, SVM and Naive Bayes. These ensemble classifiers are applied on Faculty dataset and student results for evaluation of Faculty Performance. Performances of ensemble classifier are evaluated on the basis of Accuracy, Sensitivity and Specificity. We have conducted two studies for this purpose, one with target variable Pass_Fail and another with Target variable Good_Grades. y are used as a measure for evaluating performance.