Uplift Modeling for preventing student dropout in higher education

Diego Olaya, Jonathan Vasquez, Sebastian Maldonado, Jaime Miranda, Wouter Verbeke

Research output: Contribution to journalArticle

6 Citations (Scopus)
5 Downloads (Pure)

Abstract

Uplift modeling is an approach for estimating the incremental effect of an action or treatment at the individual level. It has gained attention in the marketing and analytics communities due to its ability to adequately model the effect of direct marketing actions via predictive analytics. The main contribution of our study is the implementation of the uplift modeling framework to maximize the effectiveness of retention efforts in higher education institutions i.e., improvement of academic performance by offering tutorials. The objective is to improve the design of retention programs by tailoring them to students who are more likely to be retained if targeted. Data from three different bachelor programs from a Chilean university were collected. Students who participated in the tutorials are considered the treatment group, otherwise, they are assigned to the nontreatment group. Our results demonstrate the virtues of uplift modeling in tailoring retention efforts in higher education over conventional predictive modeling approaches.
Original languageEnglish
Article number113320
Pages (from-to)1-11
Number of pages11
JournalDecision Support Systems
Volume134
DOIs
Publication statusPublished - Jul 2020

Keywords

  • Learning analytics
  • Uplift modeling
  • Student dropout
  • Educational data mining

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