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Title:ADMIT - a web-based system to facilitate graduate admission process
Author(s):Babichenko, Dmitriy; Druzdzel, Marek
Subject(s):graduate admissions
Bayesian networks
machine learning
Abstract:In this paper we describe ADMIT, a software application developed to assist the graduate admissions process at the University of Pittsburgh School of Information Sciences (SIS). ADMIT uses a Bayesian network model built from historical admissions data and academic performance records to predict how likely each applicant is to succeed. The system rank-orders applicants based on the probability of their success in the Master of Science in Information Science (MSIS) program and presents results as an ordered list and as a histogram to the admission committee members. The system also enables users to see a graphical representation of the model (a causal graph) and observe how each input data point affects the system’s suggestions.
Issue Date:2016-03-15
Citation Info:NA
Series/Report:IConference 2016 Proceedings
Genre:Conference Paper / Presentation
Rights Information:Copyright 2016 is held by the authors. Copyright permissions, when appropriate, must be obtained directly from the authors.
Date Available in IDEALS:2016-03-08

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