Ranking Academic Advisors: Analyzing Scientific Advising Impact Using MathGenealogy Social Network
Advising and mentoring Ph.D. students is an increasingly important aspect of the academic profession. We define and interpret a family of metrics (collectively referred to as “a-indices”) that can be applied to “ranking academic advisors” using the academic genealogical records of scientists, with the emphasis on taking into account not only the number of students advised by an individual, but also subsequent academic advising records of those students. We also define and calculate the extensions of the proposed indices that account for student co-advising (referred to as “adjusted a-indices”). Finally, we extend the proposed metrics to ranking universities and countries with respect to their “collective” advising impacts. To illustrate the proposed metrics, we consider the social network of over 200,000 mathematicians (as of July 2018) constructed using the Mathematics Genealogy Project data: the network nodes represent the mathematicians who have completed Ph.D. degrees, and the directed edges connect advisors with their students.
KeywordsSocial networks Big data Scientific advising impact a-indices Mathematics genealogy project
Work of A. Semenov was funded in part by the AFRL European Office of Aerospace Research and Development (grant no. FA9550-17-1-0030). This material is based upon work supported by the AFRL Mathematical Modeling and Optimization Institute.
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