Will i Get in? Modeling the Graduate Admission Process for American Universities

Narender Gupta, Aman Sawhney, Dan Roth

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

Abstract

We study the graduate admission process in American universities from students' perspective. Our goal is to build a decision support model that provides candidates with pertinent information as well as the ability to assess their choices during the application process. This model is driven by extensive machine learning based analysis of large amounts of historic data available on the web. Our analysis considers factors such as standardized test scores and GPA as well as world knowledge such as university reputation. The learning problem is modeled as a binary classification problem with latent variables that account for hidden information, such as multiple graduate programs within the same institution. An additional contribution of this paper is the collection of a new dataset of more than 25,000 students, with 6 applications per student on average and, hence, amounting to more than 150,000 applications spanning across more than 3000 source institutions. The dataset covers hundreds of target universities over several years, and allows us to develop models that provide insight into student application behavior and university decision patterns. Our experimental study reveals some key factors in the decision process of programs that provide applicants the ability to make an informed decision during application, with high confidence of being accepted.

Original languageEnglish (US)
Title of host publicationProceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
EditorsCarlotta Domeniconi, Francesco Gullo, Francesco Bonchi, Francesco Bonchi, Josep Domingo-Ferrer, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Zhi-Hua Zhou, Xindong Wu
PublisherIEEE Computer Society
Pages631-638
Number of pages8
ISBN (Electronic)9781509054725
DOIs
StatePublished - Jul 2 2016
Event16th IEEE International Conference on Data Mining Workshops, ICDMW 2016 - Barcelona, Spain
Duration: Dec 12 2016Dec 15 2016

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume0
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Other

Other16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
CountrySpain
CityBarcelona
Period12/12/1612/15/16

Keywords

  • Decision Support
  • Graduate Admissions
  • Latent Variable
  • Machine Learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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