DocumentCode :
243521
Title :
Moneyball for Academia: Toward Measuring and Maximizing Faculty Performance and Impact
Author :
Nocka, Andrew ; Danning Zheng ; Tianran Hu ; Jiebo Luo
Author_Institution :
Dept. of Comput. Sci., Univ. of Rochester, Rochester, NY, USA
fYear :
2014
fDate :
14-14 Dec. 2014
Firstpage :
193
Lastpage :
197
Abstract :
As described in Michael Lewis´ Money ball: The Art of Winning an Unfair Game, a baseball player´s On Base Percentage is a good metric for rating players´ usefulness or productivity. What metrics can be used for gauging a professor´s performance? Is there a correlation between salary and these metrics? Are these metrics more accurate for certain fields of study? Is the currently popular practice of predominantly hiring junior faculty justified? This paper explores citations and number of publications as metrics for performance in terms of research impact. It searches for correlation between professor salary and these metrics across several subsets of professors. While there are certainly other, less concrete metrics that can be used to rate a professor such as teaching ability or regard in the field, this paper aims to look for correlation in the more measurable metrics. Finally, we build classifiers to predict IEEE/ACM Fellowships of CS/EE professors using these metrics.
Keywords :
citation analysis; data mining; educational administrative data processing; learning (artificial intelligence); pattern classification; productivity; salaries; ACM fellowship; CS professors; EE professors; IEEE fellowship; Moneyball; baseball player on base percentage; data mining; faculty performance maximization; faculty performance measurement; junior faculty hiring; learning; player usefulness rating; productivity rating; professor performance gauging; professor salary; teaching ability; Bagging; Correlation; Decision making; Educational institutions; Measurement; Psychology; Remuneration; academia; data analytics; faculty; performance metric;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining Workshop (ICDMW), 2014 IEEE International Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-1-4799-4275-6
Type :
conf
DOI :
10.1109/ICDMW.2014.156
Filename :
7022597
Link To Document :
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