Ranking Academic Advisors: Analyzing Scientific Advising Impact Using Mathgenealogy Social Network

Keywords

a-indices; Big data; Mathematics genealogy project; Scientific advising impact; Social networks

Abstract

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.

Publication Date

1-1-2018

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

11280 LNCS

Number of Pages

437-449

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-030-04648-4_37

Socpus ID

85059069570 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85059069570

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