Using data mining to predict instructor performance

Abstract

During these decades, data mining has become one of the effective tools for data analysis and knowledge management system, so that there are many areas which adapted data mining approach to solve their problems. Using data mining in education to enhance the education system is still relatively new. This paper focuses on predicting the instructor performance and investigates the factors that affect students' achievements to improve the education system quality. Turkey Student Evaluation records dataset is considered and run on different data classifier such as J48 Decision Tree, Multilayer Perception, Naive Bayes, and Sequential Minimal Optimization. Comparison of all the four classifiers is conducted to predict the accuracy and to find the best performing classification algorithm among all. The conclusions of this study are very promising and provide another point of view to evaluate student performance. It also highlights the importance of employing data mining tools in the field of education. The results show that using the attribute evaluation method on the dataset increases the prediction performance accuracy. (C) 2016 The Authors. Published by Elsevier B.V.