Application of enhanced analysis model for data mining processes in higher educational system

One of the most important facts in higher education system is quality. It concerns with all the circumstances that allow decision makers to better evaluate and enhance the higher educational organizations. One way to reach the highest level of quality in higher education systems is by improving the decision making procedures on the various processes such as planning, counseling, evaluation and so on. This can be achieved by utilizing the managerial decision makers with valuable implicit knowledge, which is currently unknown to them. This knowledge is hidden among the educational data set and it is extractable through data mining technology. The meaningful knowledge, previously unknown and potentially useful information discovered from raw educational data through data mining techniques are used to assist decision makers to improve the decision-making procedure and to set more enhanced policies for the educational processes. This paper is designed to first present and justify the capabilities of data mining in the context of higher education system by offering an enhanced version of a recently proposed analysis model (DM_EDU) by the author, used for the application of data mining in higher educational system. Then one of the most important sections of the model, "student assessment" sub-process under "evaluation" is implemented in a real world higher education, MMU in Malaysia, to present the ability of data mining in discovering useful patterns. The final result of this application aids managerial MMU decision makers to improve decision-making processes.