Using cluster ensemble to improve classification of student dropout in Thai university

Abstract

Dropout or ceasing study prematurely has been widely recognized as a serious issue, especially in the university level. A large number of higher education institutes are facing the common difficulty with low rate of graduations in comparison to the number of enrollment. As compared to western countries, this subject has attracted only a few studies in Thai university, with educational data mining being limited to the use of conventional classification models. This paper presents the most recent investigation of student dropout at Mae Fah Luang University, Thailand, and the novel reuse of link-based cluster ensemble as a data transformation framework for more accurate prediction. The empirical study on students' personal, academic performance and enrollment data, suggests that the proposed approach is usually more effective than several benchmark transformation techniques, across different classifiers.