{"title":"年龄:年龄性别对美国大学教师职业发展的影响","authors":"H. Rahmani;Anthony J. Olejniczak;Gary R. Weckman","doi":"10.1109/TBDATA.2024.3423726","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 2","pages":"606-619"},"PeriodicalIF":7.5000,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10587099","citationCount":"0","resultStr":"{\"title\":\"AGE: Age-Gender Effect on Faculty Career Progression in American Universities\",\"authors\":\"H. Rahmani;Anthony J. Olejniczak;Gary R. Weckman\",\"doi\":\"10.1109/TBDATA.2024.3423726\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":13106,\"journal\":{\"name\":\"IEEE Transactions on Big Data\",\"volume\":\"11 2\",\"pages\":\"606-619\"},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2024-07-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10587099\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Big Data\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10587099/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Big Data","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10587099/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
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.
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.