AGE: Age-Gender Effect on Faculty Career Progression in American Universities

被引:0
|
作者
Rahmani, H. [1 ]
Olejniczak, Anthony J. [2 ]
Weckman, Gary R. [1 ]
机构
[1] Ohio Univ, Dept Ind & Syst Engn, Athens, OH 45701 USA
[2] Acad Analyt Res Ctr AARC, Columbus, OH 43220 USA
关键词
Academic career; age bias; age-gender bias; association rule mining; classification; clustering; ensemble learning; extreme gradient boosting; gender bias; machine learning; random forest; WOMEN; IMPACT;
D O I
10.1109/TBDATA.2024.3423726
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This study was undertaken to examine the impact of age and gender on faculty career progression in academia and to identify key performance indicators leading to attaining promotion. To explore any evidence of age-gender effect on faculty career progression, gender compositions, promotion rates, and appointment lengths at the assistant and associate professor levels are investigated. Furthermore, the underlying factors influencing faculty performance evaluation decisions are analyzed using the commercial data provided by Academic Analytics, LLC, which comprises the scholarly records of 336 793 faculty members from 472 Ph.D.-granting universities in the United States during 2011-2020. Various machine learning techniques, including ensemble learning and association rule mining, are performed to determine the important features that provide the most significant insights into academic career growth. Our results indicate strong evidence of age-gender effect on faculty career advancement and underscore the significance of journal article and citation counts for career progression in higher education.
引用
收藏
页码:606 / 619
页数:14
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