Prediction of Student Dropout Using Personal Profile and Data Mining Approach

Phanupong Meedech; Natthakan Iam-On; Tossapon Boongoen

doi:10.1007/978-3-319-27000-5_12

Prediction of Student Dropout Using Personal Profile and Data Mining Approach

Phanupong Meedech^*, Natthakan Iam-On, Tossapon Boongoen

^*Corresponding author for this work

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

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.

Original language	English
Title of host publication	Intelligent and Evolutionary Systems
Subtitle of host publication	The 19th Asia Paciﬁc Symposium, IES 2015, Bangkok, Thailand, November 2015, Proceedings
Editors	Kittichai Lavangnananda, Somnuk Phon-Amnuaisuk, Worrawat Engchuan, Jonathan H. Chan
Publisher	Springer Nature
Pages	143-155
Number of pages	13
ISBN (Electronic)	978-3-319-27000-5
ISBN (Print)	978-3-319-26999-3
DOIs	https://doi.org/10.1007/978-3-319-27000-5_12
Publication status	Published - 12 Nov 2015
Externally published	Yes

Publication series

Name	Proceedings in Adaptation, Learning and Optimization (PALO)
Publisher	Springer
Number	1
Volume	5
ISSN (Print)	2363-6084
ISSN (Electronic)	2363-6092

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10.1007/978-3-319-27000-5_12

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Meedech, P., Iam-On, N., & Boongoen, T. (2015). Prediction of Student Dropout Using Personal Profile and Data Mining Approach. In K. Lavangnananda, S. Phon-Amnuaisuk, W. Engchuan, & J. H. Chan (Eds.), Intelligent and Evolutionary Systems: The 19th Asia Paciﬁc Symposium, IES 2015, Bangkok, Thailand, November 2015, Proceedings (pp. 143-155). ( Proceedings in Adaptation, Learning and Optimization (PALO); Vol. 5, No. 1). Springer Nature. https://doi.org/10.1007/978-3-319-27000-5_12

Meedech, Phanupong ; Iam-On, Natthakan ; Boongoen, Tossapon. / Prediction of Student Dropout Using Personal Profile and Data Mining Approach. Intelligent and Evolutionary Systems: The 19th Asia Paciﬁc Symposium, IES 2015, Bangkok, Thailand, November 2015, Proceedings. editor / Kittichai Lavangnananda ; Somnuk Phon-Amnuaisuk ; Worrawat Engchuan ; Jonathan H. Chan. Springer Nature, 2015. pp. 143-155 ( Proceedings in Adaptation, Learning and Optimization (PALO); 1).

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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{\textquoteright}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.",

author = "Phanupong Meedech and Natthakan Iam-On and Tossapon Boongoen",

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Meedech, P, Iam-On, N & Boongoen, T 2015, Prediction of Student Dropout Using Personal Profile and Data Mining Approach. in K Lavangnananda, S Phon-Amnuaisuk, W Engchuan & JH Chan (eds), Intelligent and Evolutionary Systems: The 19th Asia Paciﬁc Symposium, IES 2015, Bangkok, Thailand, November 2015, Proceedings. Proceedings in Adaptation, Learning and Optimization (PALO), no. 1, vol. 5, Springer Nature, pp. 143-155. https://doi.org/10.1007/978-3-319-27000-5_12

Prediction of Student Dropout Using Personal Profile and Data Mining Approach. / Meedech, Phanupong; Iam-On, Natthakan ; Boongoen, Tossapon.
Intelligent and Evolutionary Systems: The 19th Asia Paciﬁc Symposium, IES 2015, Bangkok, Thailand, November 2015, Proceedings. ed. / Kittichai Lavangnananda; Somnuk Phon-Amnuaisuk; Worrawat Engchuan; Jonathan H. Chan. Springer Nature, 2015. p. 143-155 ( Proceedings in Adaptation, Learning and Optimization (PALO); Vol. 5, No. 1).

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

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T1 - Prediction of Student Dropout Using Personal Profile and Data Mining Approach

AU - Meedech, Phanupong

AU - Iam-On, Natthakan

AU - Boongoen, Tossapon

PY - 2015/11/12

Y1 - 2015/11/12

N2 - 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.

AB - 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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T3 - Proceedings in Adaptation, Learning and Optimization (PALO)

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Meedech P, Iam-On N , Boongoen T. Prediction of Student Dropout Using Personal Profile and Data Mining Approach. In Lavangnananda K, Phon-Amnuaisuk S, Engchuan W, Chan JH, editors, Intelligent and Evolutionary Systems: The 19th Asia Paciﬁc Symposium, IES 2015, Bangkok, Thailand, November 2015, Proceedings. Springer Nature. 2015. p. 143-155. ( Proceedings in Adaptation, Learning and Optimization (PALO); 1). doi: 10.1007/978-3-319-27000-5_12

Prediction of Student Dropout Using Personal Profile and Data Mining Approach

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