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Who is Hiring Whom: A New Method in Measuring Graduate Programs

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Conference

2015 ASEE Annual Conference & Exposition

Location

Seattle, Washington

Publication Date

June 14, 2015

Start Date

June 14, 2015

End Date

June 17, 2015

ISBN

978-0-692-50180-1

ISSN

2153-5965

Conference Session

Graduate Recruitment & Professional Development

Tagged Division

Graduate Studies

Page Count

26

Page Numbers

26.1736.1 - 26.1736.26

DOI

10.18260/p.25072

Permanent URL

https://peer.asee.org/25072

Download Count

357

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Paper Authors

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Bolun Huang Microsoft Corp.

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Bolun Huang is currently a software engineer in Microsoft Corp., Redmond. Before that, he was a master of science student in the Department of Electrical and Computer Engineering at Texas A&M University. He completed dual bachelors from a joint program between Queen Mary University of London and Beijing University of Posts and Telecommunications. His research interests include: Data Mining, Social Network Analysis, Machine Learning and Computer&Network Security.

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Samantha Wang

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Samantha Wang is an undergraduate student studying Electrical and Computer Engineering at Carnegie Mellon University.

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Narasimha Reddy

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Abstract

Who is Hiring Whom: A New Method in Measuring Graduate ProgramsAs U.S. News claims, they rank the graduate programs based on both statistical dataand expert assessment data. The statistical data includes both input and outputmeasures, reflecting the quality of resources into the programs and educationaloutcomes from the programs. The expert assessment data is collected from the inputof program deans. Each dean is asked to rank a program from 1 to 5 and the averagerating is used as the ranking score. Finally these two types of measurements arenormalized, weighted and totaled into a ranking score for each program.U.S. News ranks graduate programs in a traditional method. However, it is not onlycumbersome but also costly. In addition, it is not necessarily an objective evaluationof the quality of programs because it is a survey-based approach. Differently, wepropose an innovative and effective model on graduate program ranking based onwhat we call the “hiring graph”. The hiring graph is basically a directed social graphrevealing the employment relationships of Ph.D.s among universities. In our hiringgraph, for example, a directed edge from program A to program B indicates that Ahires at least one Ph.D. from B as its faculty member. We note that a lot of resourcesare placed in the hiring activity, including assessment from domain experts,academic review, salaries and so on, and therefore the hiring decision reflects theacademic quality of the faculty member, and therefore the program, in acomprehensive and self-consistent way.In this paper, based on the assumption that “schools tend to hire Ph.D.s from peer orbetter schools”, we propose an analytical and mathematical approach to rankgraduate programs using algorithms deployed on the hiring graph among universities.In order to validate our approach, we collect faculty data from the top 50 ComputerScience (CS) departments, top 50 Mechanical Engineering (ME) departments andtop 50 Electrical Engineering (EE) departments across the United States accordingto U.S. News. We refine the PageRank (PR) algorithm and the Hyperlink-InducedTopic Search (HITS) algorithm in order to rank the graduate programs from thehiring graph. Our new rankings are generally consistent with U.S. News rankings,while having some inconsistencies for some particular programs. By conductingextensive data analysis, we not only discover what is behind the “hiring graph” butalso reveal valuable knowledge beyond the scope of U.S. News ranking. Finally, wepropose a cross-domain graduate program ranking model and our own rankings forthe three collected programs. We believe that our model can be used for rankingother engineering programs and even technology companies. We plan to generalizeour ranking model with more data collection in the future.

Huang, B., & Wang, S., & Reddy, N. (2015, June), Who is Hiring Whom: A New Method in Measuring Graduate Programs Paper presented at 2015 ASEE Annual Conference & Exposition, Seattle, Washington. 10.18260/p.25072

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