Academic support network reflects doctoral experience and productivity

Seçkin, Özgür Can (2022) Academic support network reflects doctoral experience and productivity. [Thesis]

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Current practices of quantifying academic performance by productivity raise serious concerns about the psychological well-being of graduate students. These efforts often neglect the influence of researchers’ environment. Acknowledgments subsections in dissertations shed light on this environment by providing an opportunity for students to thank the people who supported them. We analysed 26,236 acknowledgments to create an "academic support network" that reveals five distinct communities supporting students along the way: Academic, Administration, Family, Friends & Colleagues, and Spiritual. We show that female students mention fewer people from each of these communities, with the exception of their families, and that their productivity is slightly lower than that of males when considering the number of publications alone. This is critically important because it means that studying the doctoral process may help us better understand the adverse conditions women face early in their academic careers. Our results also suggest that the total number of people mentioned in the acknowledgements allows disciplines to be categorised as either individual science or team science as their magnitudes change. We show that male students who mention more people from their academic community are associated with higher levels of productivity. University rankings are also found to be positively correlated with productivity and the size of academic support networks. However, neither university rankings nor students’ productivity levels correlate with the sentiments students express in their acknowledgements. Our results point to the importance of academic support networks by explaining how they differ and how they influence productivity.
Item Type: Thesis
Uncontrolled Keywords: science of science. -- network science. -- text mining. -- natural language processing. -- machine learning. -- ağ bilimi. -- metin madenciliği. -- doğal dil işleme. -- makine öğrenmesi.
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics > TK7885-7895 Computer engineering. Computer hardware
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Computer Science & Eng.
Faculty of Engineering and Natural Sciences
Depositing User: Dila Günay
Date Deposited: 10 Jul 2023 16:05
Last Modified: 13 Nov 2023 15:09

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