Improved student dropout prediction in Thai University using ensemble of mixed-type data clusterings

Natthakan Iam-On*, Tossapon Boongoen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

40 Citations (SciVal)


Increasing student retention has been a common goal of many academic institutions, especially in the university level. The negative effects of student attrition are evident to students, parents, university and the society as a whole. The first-year students are at the greatest risk of dropping out or not completing their degree on time. With this insight, a number of data mining methods have been developed for early detection of students at risk of dropout, hence the immediate application of assistive measure. 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 mixed-type data collection related to students’ demographic detail, academic performance and enrollment record, suggests that the proposed approach is usually more effective than several benchmark transformation techniques, across different classifiers.

Original languageEnglish
Pages (from-to)497-510
Number of pages14
JournalInternational Journal of Machine Learning and Cybernetics
Issue number2
Early online date17 Feb 2015
Publication statusPublished - 01 Apr 2017
Externally publishedYes


  • Classification
  • Educational data mining
  • Ensemble clustering
  • Feature transformation
  • Student dropout


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