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MACHINE LEARNING FOR PREDICTION OF FACULTY SUCCESS IN WINNING GRANT AWARDS

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Date Issued:
2022
Abstract/Description:
In order for innovation and breakthroughs to occur, principal investigators must constantly apply for grants and other funding sources. Through previous research, it has been shown that peer-review panels responsible for selecting grant award recipients don’t base their decisions on the applicant’s academic or research history and affiliations. Instead, they can identify quality research proposals that achieve high citation counts later on. Therefore, it can be deduced that the recipients are chosen solely due to their research quality and topic with little to no bias involved. This produces two important questions: Can machine learning help predict the success of faculty seeking external awards? What are the important factors related to such predictive models? Using the Academic Analytics Research Center’s rich faculty dataset, I will leverage machine learning models to identify important factors associated with winning grant awards.
Title: MACHINE LEARNING FOR PREDICTION OF FACULTY SUCCESS IN WINNING GRANT AWARDS.
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Name(s): Delgado, Jose , author
Zhu, Xingquan , Thesis advisor
Harriet L. Wilkes Honors College
Florida Atlantic University, Degree grantor
Type of Resource: text
Genre: Thesis
Date Created: 2022
Date Issued: 2022
Publisher: Florida Atlantic University
Place of Publication: Jupiter, Florida
Physical Form: application/pdf
Extent: 42 p.
Language(s): English
Abstract/Description: In order for innovation and breakthroughs to occur, principal investigators must constantly apply for grants and other funding sources. Through previous research, it has been shown that peer-review panels responsible for selecting grant award recipients don’t base their decisions on the applicant’s academic or research history and affiliations. Instead, they can identify quality research proposals that achieve high citation counts later on. Therefore, it can be deduced that the recipients are chosen solely due to their research quality and topic with little to no bias involved. This produces two important questions: Can machine learning help predict the success of faculty seeking external awards? What are the important factors related to such predictive models? Using the Academic Analytics Research Center’s rich faculty dataset, I will leverage machine learning models to identify important factors associated with winning grant awards.
Identifier: FAUHT00192 (IID)
Degree granted: Thesis (B.S.)--Florida Atlantic University, Harriet L. Wilkes Honors College, 2022
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FAUHT00192
Use and Reproduction: Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
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Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.

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