A comparative study of classifiers for student module in technical education system (Tes)

In recent years, huge amount of data with regard to higher education system especially with regard to technical education system(TES) is available and queries related to Edu-DATA are of practical interest as SQL approach is insufficient and needs to be focused in a different way. The present study aims at developing a technique called Edu-MINING which converts raw data coming from educational institutions using data mining techniques into useful information. The discovered knowledge will have a great impact on the educational research and practices. Edu-MINING explores Edu-DATA, discovers new knowledge and suggests useful methods to improve the quality of education with regard to teaching and learning process. The study is carried out for the Edu-student-data set comprising of 3500 instances and fourteen attributes. A comprehensive study of the experimental analysis is presented and the results are found to be of immense practical interest. Finally, optimal classifiers are identified and excellent accuracy is achieved.