Career Success in Academia – A Visualization Approach
IEEE Computer Society Team
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How exactly do academics achieve career success? Researchers have strived to find this answer for quite some time. In a recent paper, some researchers decided to take a more visual analytics approach to understand how individual and social factors can impact an academic’s career success. In this process, they sought to answer questions such as “how do multiple factors differ in their impact on career success” and “how does a researcher’s career path change over time”?
Rather than the usual point-to-point transition perspective, they have focused on how historical events affected future career results in the long term. Below are the following steps they took to find the answers.
To perform the study, they took a sample of scholars and followed their career paths. They measured academic career performance by the number of citations a scholar’s research paper received. They also measured career factors that influenced an academic’s success, such as individual characteristics like job sectors and job titles and social factors like family relationships.
Data was collected from various academics, and Sequence History Analysis (SHA) and Multi-factor Impact Analysis (MIA) were used to understand how the different career factors affected success over time.
For each academic, different sequences were created, such as career sequences (title and citation ranks), domain sequences (research paper venue categories), sector sequences (industry sectors the subject was affiliated with over time), and citation sequences (citation numbers by year).
Data was served visually to make interpretations easier to understand, including a Factor View that showed the analyzed regression results. This view comprised a Horizon Chart Group that showed the inter-factor comparisons on career success and an Impact Timeline that showed the intra-factor overview of how the different factors influenced career success. These views are pinpointed to each factor (rank, industry, social skills, etc.), so anyone can easily observe the effect of the factor over time.
Graphical representations of the data, like the career glyph sunburst graph, showed the title and citation ranks of the study participants, and domain and sector distributions were shown as donut charts to reveal the different categories of each group. These visual graphs, along with other data views (including timelines), reveal what aspects of individual and social factors influence career success the most.
The Case Studies
To validate their findings, the researchers performed two case studies and interviews with social scientists and general researchers.
The first case study sought to determine whether individuals from similar starting points would achieve different levels of career success throughout a long period. It also searched to see how different types of past careers affected their future career success.
The second case study focused on how different combinations of social factors could impact career success, and if historical sectors could influence careers. Both studies revealed interesting results that corresponded with their interviews.
To know the conclusion of this study and discover what factors affected career success in academia for researchers, look to read the full paper here.