6533b854fe1ef96bd12aeb74
RESEARCH PRODUCT
Network-based indices of individual and collective advising impacts in mathematics
Alexander SemenovEduardo L. PasiliaoAlexander VeremyevAlexander G. NikolaevVladimir Boginskisubject
social networksKnowledge managementAcademic advisingTrack (rail transport)01 natural sciencesSocial networkslcsh:QA75.5-76.95010305 fluids & plasmas03 medical and health sciencesBig datasosiaaliset verkostotbig data0103 physical sciencesComputingMilieux_COMPUTERSANDEDUCATIONAcademic professionMathematics Genealogy Project030304 developmental biology0303 health sciencesindeksitSocial networklcsh:T58.5-58.64business.industrylcsh:Information technologyScientific advising impactscientific advising impactComputer Science ApplicationsHuman-Computer InteractionRankingModeling and Simulationa-Indicesmathematics genealogy projectlcsh:Electronic computers. Computer sciencebusinessInformation Systemsdescription
AbstractAdvising 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 potentially 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”). In addition, we extend some of the proposed metrics to ranking universities and countries with respect to their “collective” advising impacts, as well as track the evolution of these metrics over the past several decades. 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.
year | journal | country | edition | language |
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2020-01-06 | Computational Social Networks |