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Understanding the advisor–advisee relationship via scholarly data analysis

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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.

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  1. https://www.aminer.cn.

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Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group NO (RG-1438-027). Xiangjie Kong is supported by Fundamental Research Funds for the Central Universities under Grant NO (DUT18JC09), and China Scholarship Council under Grant NO (201706060067).

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Authors and Affiliations

  1. Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, Dalian, China

    Jiaying Liu, Xiangjie Kong & Feng Xia

  2. Chengdu College, University of Electronic Science and Technology of China, Chengdu, China

    Tao Tang

  3. Computer Science Department, Community College, King Saud University, Riyadh, Saudi Arabia

    Amr Tolba & Zafer AL-Makhadmeh

  4. Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Shibin El Kom, Egypt

    Amr Tolba

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  1. Jiaying Liu
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  2. Tao Tang
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  3. Xiangjie Kong
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  4. Amr Tolba
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  5. Zafer AL-Makhadmeh
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  6. Feng Xia
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Correspondence to Xiangjie Kong.

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Liu, J., Tang, T., Kong, X. et al. Understanding the advisor–advisee relationship via scholarly data analysis. Scientometrics 116, 161–180 (2018). https://doi.org/10.1007/s11192-018-2762-2

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