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Solutions to Chance-Constrained Programming Problems with Exponential Random Variables by Edgeworth Approximation Read chapter Read first chapter

2018 | OriginalPaper | Chapter

36. Predicting Chronic Absenteeism Using Educational Data Mining Methods

Authors : Şebnem Özdemir, Fatma Çınar, C. Coşkun Küçüközmen, Kutlu Merih

Published in: Chaos, Complexity and Leadership 2016

Publisher: Springer International Publishing

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Abstract

The rate of chronic absenteeism is important in assessing the validity of current educational practices conditions. Every student who exhibits this behavior faces the risk of failing to progress to higher level of education and/or dropping out/leaving the school. Students in this risk group represent not only a problem from an educational standpoint but also a potential and multifaceted problem with respect to participation in the economy, the development of a skilled labor force, and the ability to become well integrated into society. In the literature for Turkey, the framework of this problem was constructed using statistical methods, and it is important to analyze this problem in greater depth. The main objective of this study is therefore to employ educational data mining methods to predict cases of chronic absenteeism at high school level. The data, compiled from 2,495 students from different districts of Istanbul, was prepared for data mining operations based on the CRISP-EDM steps. The analysis process was conducted using R language and R language packages due to their flexibility and strength. The study results revealed that the random forest algorithm is able to establish a more successful model, while the C4.5 algorithm more accurately describes the problem in terms of decision rules.

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Metadata
Title
Predicting Chronic Absenteeism Using Educational Data Mining Methods
Authors
Şebnem Özdemir
Fatma Çınar
C. Coşkun Küçüközmen
Kutlu Merih
Copyright Year
2018
Book
Chaos, Complexity and Leadership 2016

Print ISBN: 978-3-319-64552-0

Electronic ISBN: 978-3-319-64554-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-64554-4_36