Faculty performance evaluation based on prediction in distributed data mining

Education is a very large area to study. In the real world, predicting the performance of the faculties is a very much challenging task. We can find different parameters used in evaluating faculty performance to be used with different classification algorithms that predicts the faculty performance. After investigation we can predict the performance of the faculty and then it becomes feasible for taking necessary action to improve it. It can be proved helpful for academic institutes. This topic provides a better solution for the problem of predicting and analyzing faculty performance in distributed data mining. With the use of distributed data mining we can fetch data from the different sources then we can apply classification algorithm on it. Distributed data mining provides an efficient path for data storing and thus data can be accessed quickly and easily. By classification we can get better efficiency and accuracy in measuring the performance of faculty. And we can build the performance prediction model based on faculty´s skills, punctuality and performance in various tests. This classification technique is tested in WEKA tool to get accurate results.