年龄:年龄性别对美国大学教师职业发展的影响

IF 7.5 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
H. Rahmani;Anthony J. Olejniczak;Gary R. Weckman
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引用次数: 0

摘要

本研究旨在研究年龄和性别对学术界教师职业发展的影响,并确定导致获得晋升的关键绩效指标。为了探究年龄性别对教师职业发展的影响,我们调查了助教和副教授级别的性别构成、晋升率和聘期。此外,利用学术分析有限责任公司提供的商业数据,分析了影响教师绩效评估决策的潜在因素,这些数据包括2011-2020年美国472所博士学位授予大学的336793名教师的学术记录。执行各种机器学习技术,包括集成学习和关联规则挖掘,以确定为学术生涯发展提供最重要见解的重要特征。我们的研究结果有力地证明了年龄性别对教师职业发展的影响,并强调了期刊文章和引文数量对高等教育职业发展的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AGE: Age-Gender Effect on Faculty Career Progression in American Universities
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.
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来源期刊
CiteScore
11.80
自引率
2.80%
发文量
114
期刊介绍: The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.
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