Exploring the Confounding Factors of Academic Career Success: An Empirical Study with Deep Predictive Modeling

TL;DR

This paper employs deep predictive modeling to analyze various factors influencing academic career success, focusing on IEEE and ACM Fellow distinctions, revealing gender disparities and the limited impact of fellowship on productivity.

Contribution

It introduces a quantitative, empirical approach to understanding academic success factors, highlighting the role of co-author networks and gender differences.

Findings

Influential co-authors are crucial early in careers.

Females need more effort to achieve Fellow status.

Fellowship does not significantly boost citations or productivity.

Abstract

Understanding determinants of success in academic careers is critically important to both scholars and their employing organizations. While considerable research efforts have been made in this direction, there is still a lack of a quantitative approach to modeling the academic careers of scholars due to the massive confounding factors. To this end, in this paper, we propose to explore the determinants of academic career success through an empirical and predictive modeling perspective, with a focus on two typical academic honors, i.e., IEEE Fellow and ACM Fellow. We analyze the importance of different factors quantitatively, and obtain some insightful findings. Specifically, we analyze the co-author network and find that potential scholars work closely with influential scholars early on and more closely as they grow. Then we compare the academic performance of male and female Fellows. After comparison, we find that to be elected, females need to put in more effort than males. In addition, we also find that being a Fellow could not bring the improvements of citations and productivity growth. We hope these derived factors and findings can help scholars to improve their competitiveness and develop well in their academic careers.