Analyze Faculty Performance System using Data Mining Techniques

Abstract

The development of an improved and intelligent model for the evaluation of instructors‟ performance in higher institutions uses the efficient data mining techniques considering the drawbacks of the prior traditional techniques. This proposed system analyses the factors related with the evaluation of instructors teaching performance using predictive data mining techniques known as regression statistical model. Regression is a data mining predictive technique that is used to make statistical prediction of the variables, given a set of data. Consequently, the evaluation of instructors‟ performance is useful for the academic institutions as it helps to make effective managerial decisions, improve the quality, reliability and efficiency of the instructors, provides a basis for the performance improvement that will optimize students‟ academic outcomes and improve standard of education and contribute to successful accomplishment of the organizational  goals. The knowledge is hidden among the educational data set and it is extractable through data mining techniques. Present paper is designed to justify the capabilities of data mining techniques in context of higher education by offering a data mining model for higher education system in the university. In this research, the classification task is used to evaluate student’s performance and as there are many approaches that are used for data classification, the decision tree method is used here. By this task we extract knowledge that describes students’ performance in end semester examination. It helps earlier in identifying the dropouts and students who need special attention and allow the teacher to provide appropriate advising/counseling.