Seek for Success: A Visualization Approach for Understanding the Dynamics of Academic Careers
Published in IEEE Transactions on Visualization and Computer Graphics, 2022
Recommended citation: Wang, Y., Peng, T. Q., Lu, H., Wang, H., Xie, X., Qu, H. M., & Wu, Y. C. (2022). Seek for Success: A Visualization Approach for Understanding the Dynamics of Academic Careers. IEEE Transactions on Visualization and Computer Graphics, 28(1), 475–485. https://doi.org/10.1109/TVCG.2021.3114790
Abstract
How to achieve academic career success has been a long-standing research question in social science research. With the growing availability of large-scale well-documented academic profiles and career trajectories, scholarly interest in career success has been reinvigorated, which has emerged to be an active research domain called the Science of Science (i.e., SciSci). In this study, we adopt an innovative dynamic perspective to examine how individual and social factors will influence career success over time. We propose ACSeeker , an interactive visual analytics approach to explore the potential factors of success and how the influence of multiple factors changes at different stages of academic careers. We first applied a Multi-factor Impact Analysis framework to estimate the effect of different factors on academic career success over time. We then developed a visual analytics system to understand the dynamic effects interactively. A novel timeline is designed to reveal and compare the factor impacts based on the whole population. A customized career line showing the individual career development is provided to allow a detailed inspection. To validate the effectiveness and usability of ACSeeker , we report two case studies and interviews with a social scientist and general researchers.
Keywords
Career Analysis, Academic Profiles, Science of Science, Publication Data, Citation Data, Sequence Analysis