Welcome to the InfoSci Platform
Predicting Student Retention by Linear Programming Discriminant Analysis

Predicting Student Retention by Linear Programming Discriminant Analysis

Jaan Ubi, Evald Ubi, Innar Liiv, Kristina Murtazin
Copyright: © 2014 |Volume: 4 |Issue: 2 |Pages: 11
ISSN: 2155-5605|EISSN: 2155-5613|EISBN13: 9781466657113|DOI: 10.4018/ijtem.2014070104
Cite Article Cite Article

MLA

Ubi, Jaan, et al. "Predicting Student Retention by Linear Programming Discriminant Analysis." IJTEM vol.4, no.2 2014: pp.43-53. http://doi.org/10.4018/ijtem.2014070104

APA

Ubi, J., Ubi, E., Liiv, I., & Murtazin, K. (2014). Predicting Student Retention by Linear Programming Discriminant Analysis. International Journal of Technology and Educational Marketing (IJTEM), 4(2), 43-53. http://doi.org/10.4018/ijtem.2014070104

Chicago

Ubi, Jaan, et al. "Predicting Student Retention by Linear Programming Discriminant Analysis," International Journal of Technology and Educational Marketing (IJTEM) 4, no.2: 43-53. http://doi.org/10.4018/ijtem.2014070104

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

The goal of the paper is to predict student retention with an ensemble method by combining linear programming (LP) discriminant analysis approaches together with bootstrapping and feature salience detection. In order to perform discriminant analysis, we linearize a fractional programming method by using Charnes-Cooper transformation (CCT) and apply linear programming, while comparing with an approach that uses deviation variables (DV) to tackle a similar multiple criteria optimization problem. We train a discriminatory hyperplane family and make the decision based on the average of the histograms created, thereby reducing variability of predictions. Feature salience detection is performed by using the peeling method, which makes the selection based on the proportion of variance explained in the correlation matrix. While the CCT method is superior in detecting true-positives, DV method excels in finding true-negatives. The authors obtain optimal results by selecting either all 14 (CCT) or the 8 (DV) most important student study related and demographic dimensions. They also create an ensemble. A quantitative course along with the age at accession are deemed to be the most important, whereas the two courses resulting in less than 2% of failures are amongst the least important, according to peeling. A five-fold Kolmogorov-Smirnov test is undertaken, in order to help university staff in devising intervention measures.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.