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

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

8 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014
PublisherIEEE Press
Pages452-457
Number of pages6
ISBN (Electronic)9781479959556
DOIs
Publication statusPublished - 18 Feb 2014
Externally publishedYes
Event2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014 - Kitakyushu, Japan
Duration: 03 Dec 201406 Dec 2014

Publication series

Name2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014

Conference

Conference2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014
Country/TerritoryJapan
CityKitakyushu
Period03 Dec 201406 Dec 2014

Keywords

  • classification
  • cluster ensemble
  • educational data mining
  • student dropout

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