Understanding the advisor–advisee relationship via scholarly data analysis

Jiaying Liu, Tao Tang, Xiangjie Kong (), Amr Tolba, Zafer AL-Makhadmeh and Feng Xia
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Jiaying Liu: Dalian University of Technology
Tao Tang: University of Electronic Science and Technology of China
Xiangjie Kong: Dalian University of Technology
Amr Tolba: King Saud University
Zafer AL-Makhadmeh: King Saud University
Feng Xia: Dalian University of Technology

Scientometrics, 2018, vol. 116, issue 1, No 7, 180 pages

Abstract: Abstract Advisor–advisee relationship is important in academic networks due to its universality and necessity. Despite the increasing desire to analyze the career of newcomers, however, the outcomes of different collaboration patterns between advisors and advisees remain unknown. The purpose of this paper is to find out the correlation between advisors’ academic characteristics and advisees’ academic performance in Computer Science. Employing both quantitative and qualitative analysis, we find that with the increase of advisors’ academic age, advisees’ performance experiences an initial growth, follows a sustaining stage, and finally ends up with a declining trend. We also discover the phenomenon that accomplished advisors can bring up skilled advisees. We explore the conclusion from two aspects: (1) Advisees mentored by advisors with high academic level have better academic performance than the rest; (2) Advisors with high academic level can raise their advisees’ h-index ranking. This work provides new insights on promoting our understanding of the relationship between advisors’ academic characteristics and advisees’ performance, as well as on advisor choosing.

Keywords: Academic networks; Scholarly data; Social network analysis; Advisor–advisee relationship; Collaboration patterns (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s11192-018-2762-2

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