Mining student at risk in higher education using predictive models

January D. Febro and Jocelyn Barbosa
Additional contact information
January D. Febro: Mindanao State University, Marawi, Philippines
Jocelyn Barbosa: University of Science and Technology of Southern, Cagayan de Oro, Philippines

Journal of Advances in Technology and Engineering Research, 2017, vol. 3, issue 4, 117-132

Abstract: This research uses an approach in Data Mining techniques to analyze historical data of students. The goal is to predict and investigate factors affecting student leavers using university admission (pre-admission variables) and educational achievement indicators (post-admission variables) on at-risk freshmen students in higher institution. Feature selection as preprocessing methods are utilized for 30 potential predictors to identify the most relevant factors. With the aim to evaluate the significant predictors contributing to at-risk students, this research compares predictive models. The models were implemented using various data mining algorithms and it is found that kNN gives 80.50% accuracy, CART with 89.70%, Adaptive Boosting using Decision Tree with 92.20% and Logistic Regression with 92.09%. The models were trained using records from student dataset collected from the Mindanao State University – Marawi, academic year 2010-2015. In addition, this research also verified the precision of the models through 10-fold cross validation, which can give veracity about what kind of data mining models works best in HEI data mining analysis. It would predict the class label ‘Result’ as categorical value, AtRisk or NotAtRisk. Benefits of the prediction model includes improving student retention and graduation rates.

Keywords: Data Mining; Educational Data Mining; Knowledge Discovery in Databases; Student Prediction (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://tafpublications.com/platform/Articles/full-jater3.4.2.php (application/pdf)
https://tafpublications.com/gip_content/paper/jater-3.4.2.pdf (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:apb:jaterr:2017:p:117-132

DOI: 10.20474/jater-3.4.2

Access Statistics for this article

Journal of Advances in Technology and Engineering Research is currently edited by A/Professor Akbar A. Khatibi

More articles in Journal of Advances in Technology and Engineering Research from A/Professor Akbar A. Khatibi Calle Alarcon 66, Sant Adrian De Besos 08930, Barcelona Spain.
Bibliographic data for series maintained by A/Professor Akbar A. Khatibi ().