Understanding the Advisor-advisee Relationship via Scholarly Data Analysis
It addresses the problem of understanding advisor-advisee relationships for academic newcomers, but it is incremental as it applies existing analysis methods to new scholarly data.
This paper investigates the correlation between advisors' academic characteristics and advisees' performance in Computer Science, finding that advisees' performance initially grows, sustains, and then declines with advisors' academic age, and that high-level advisors lead to better performance and higher h-index rankings for advisees.
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.