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General Information
    • ISSN: 2010-3700 (Online)
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.

IJMLC 2019 Vol.9(3): 273-278 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.3.798

Analysis of Student-at-Risk of Dropping out (SARDO) Using Decision Tree: An Intelligent Predictive Model for Reduction

Maricel A. Timbal
Abstract—The advancement of the country is exceptionally reliant on the education of its citizens, which is an incontestable truth of the worldwide perspective. Education assumes a focal part of developing an individual to become a productive citizen and is the most important factor contributing to the progress of the nation. However, children in developing countries have denied their right to education by dropping out for different reasons. Student-At-Risk of Dropping Out commonly known as SARDO is a term coined by the Philippines’ Department of Education, defined as, a student who is likely to become a candidate to drop out. Philippine Statistics Authority had conferred that there is a total of 36, 238 people ages from six to twenty-four who are out-of-school. This poses a predicament if this will continue without even attempting to intervene or at least reduce it gradually, to the governing body (DepEd) and ripple its effect to the entire country. In this paper, a classification data mining technique such as decision tree was used to generate a rule-based classifier. Based on results, there were three extracted rules from the decision tree generated using partykit and rpart libraries as a predictive model, a basis for forecasting who among the enrolled students will be a dropout. The first rule is, if a student has an experience being retained from previous grade level/s, he/she is more likely to drop. The second is, if he/she has not experienced retention but his/her number of siblings is greater than six, most probably he/she will drop from school. The last rule is if a student has not experienced retention nor has greater than six siblings, but his/her general average of previous grade level is fairly satisfactory and satisfactory, then he/she will probably stop from schooling. Having this intelligent predictive model, the educational institution with its stakeholders can now have a proactive measure on addressing to reduce the inflation of dropout rate of which is the sole purpose of why this study was conducted.

Index Terms—Dropout, data mining, decision tree, rule-based.

Maricel A. Timbal is with the Department of Education, Division of Davao del Norte, Philippines (e-mail: maricel.timbal@deped.gov.ph).

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Cite: Maricel A. Timbal, "Analysis of Student-at-Risk of Dropping out (SARDO) Using Decision Tree: An Intelligent Predictive Model for Reduction," International Journal of Machine Learning and Computing vol. 9, no. 3, pp. 273-278, 2019.

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