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Prediction of Student Dropout Using Personal Profile and Data Mining Approach

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Intelligent and Evolutionary Systems

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 5))

Abstract

The problem of student dropout has steadily increased in many universities in Thailand. The main purpose of this research is to develop a model for predicting dropout occurences with the first-year students and determine the factors behind these cases. Despite several classification techniques being made available in the literature, the current study focuses on using decision trees and rule induction models to discover knowledge from data of students at Mae Fah Luang University. The resulting classifiers that is interpretable and analyzed by those involved in the assistant and consultation aid, are built from the collection of different attributes. These include student’s academic performance in the first semester, student social behavior, personal background and education background. With respect to the experiments with various classifiers and the application of data rebalancing algorithm, the results indicate a promising accuracy, hence the reliability of this study as a decision support tool.

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Author information

Authors and Affiliations

  1. School of Information Technology, Mae Fah Luang University, Chiang Rai, 57100, Thailand

    Phanupong Meedech & Natthakan Iam-On

  2. Department of Mathematics and Computer Science, Royal Thai Air Force Academy, Bangkok, 10220, Thailand

    Tossapon Boongoen

Authors
  1. Phanupong Meedech
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  2. Natthakan Iam-On
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  3. Tossapon Boongoen
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Corresponding author

Correspondence to Phanupong Meedech .

Editor information

Editors and Affiliations

  1. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Kittichai Lavangnananda

  2. School of Computing and Informatics, Institut Teknologi Brunei, Gadong, Brunei Darussalam

    Somnuk Phon-Amnuaisuk

  3. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Worrawat Engchuan

  4. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Jonathan H. Chan

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© 2016 Springer International Publishing Switzerland

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Meedech, P., Iam-On, N., Boongoen, T. (2016). Prediction of Student Dropout Using Personal Profile and Data Mining Approach. In: Lavangnananda, K., Phon-Amnuaisuk, S., Engchuan, W., Chan, J. (eds) Intelligent and Evolutionary Systems. Proceedings in Adaptation, Learning and Optimization, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-27000-5_12

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  • DOI: https://doi.org/10.1007/978-3-319-27000-5_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26999-3

  • Online ISBN: 978-3-319-27000-5

  • eBook Packages: EngineeringEngineering (R0)

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