TY - GEN
T1 - Will i Get in? Modeling the Graduate Admission Process for American Universities
AU - Gupta, Narender
AU - Sawhney, Aman
AU - Roth, Dan
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/7/2
Y1 - 2016/7/2
N2 - 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.
AB - 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.
KW - Decision Support
KW - Graduate Admissions
KW - Latent Variable
KW - Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85015185581&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015185581&partnerID=8YFLogxK
U2 - 10.1109/ICDMW.2016.0095
DO - 10.1109/ICDMW.2016.0095
M3 - Conference contribution
AN - SCOPUS:85015185581
T3 - IEEE International Conference on Data Mining Workshops, ICDMW
SP - 631
EP - 638
BT - Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
A2 - Domeniconi, Carlotta
A2 - Gullo, Francesco
A2 - Bonchi, Francesco
A2 - Bonchi, Francesco
A2 - Domingo-Ferrer, Josep
A2 - Baeza-Yates, Ricardo
A2 - Baeza-Yates, Ricardo
A2 - Baeza-Yates, Ricardo
A2 - Zhou, Zhi-Hua
A2 - Wu, Xindong
PB - IEEE Computer Society
T2 - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
Y2 - 12 December 2016 through 15 December 2016
ER -