Mining the Student Dropout in Higher Education

Abstract

Abstract Higher technological and vocational education (TVE) has served an important role in the long-term progress and industrial development of Taiwan. However, the high dropout rates in higher TVE are a challenging task for policy makers. This study is a first to propose a hybrid approach that combines both k-means and rough set theory for mining the dropout knowledge among student dropout. An empirical case of student dropout is based on the industrial-academic cooperation (IAC) education of higher TVE in Taiwan. The results of knowledge extraction from the proposed approach are illustrated as knowledge patterns/rules and clusters to provide better understanding of the reasons for or factors influencing student dropout.