Using big data for predicting freshmen retention

Sudha Ram, Yun Wang, Faiz Currim, Sabah Currim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

Traditional research in student retention is survey-based, relying on data collected from questionnaires, which is not optimal for proactive prediction and real-Time decision (student intervention) support. Machine learning approaches have their own limitations. Therefore, in this research, we propose a big data approach to formulating a predictive model. We used commonly available (student demographic and academic) data in academic institutions augmented by derived implicit social networks from students' university smart card transactions. Furthermore, we applied a sequence learning method to infer students' campus integration from their purchasing behaviors. Since student retention data is highly imbalanced, we built a new ensemble classifier to predict students at-risk of dropping out. For model evaluation, we use a real-world dataset of smart card transactions from a large educational institution. The experimental results show that the addition of campus integration and social behavior features refined using the ensemble method significantly improve prediction accuracy and recall.

Original languageEnglish (US)
Title of host publication2015 International Conference on Information Systems: Exploring the Information Frontier, ICIS 2015
PublisherAssociation for Information Systems
StatePublished - 2015
Event2015 International Conference on Information Systems: Exploring the Information Frontier, ICIS 2015 - Fort Worth, United States
Duration: Dec 13 2015Dec 16 2015

Other

Other2015 International Conference on Information Systems: Exploring the Information Frontier, ICIS 2015
CountryUnited States
CityFort Worth
Period12/13/1512/16/15

Keywords

  • Data mining
  • Machine learning
  • Predictive modeling
  • Social Network Analysis

ASJC Scopus subject areas

  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Using big data for predicting freshmen retention'. Together they form a unique fingerprint.

Cite this