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Quick Understanding Our Engineering Faculty Research Needs Using Topic Modeling

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2019 ASEE Annual Conference & Exposition


Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

June 19, 2019

Conference Session

Informing the Critical Understanding of Our Users: Using Data to Develop New and Diverse Services

Tagged Division

Engineering Libraries

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Paper Authors


Qianjin Zhang University of Iowa Orcid 16x16

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Qianjin (Marina) Zhang is the Engineering & Informatics Librarian at the Lichtenberger Engineering Library, The University of Iowa. As a subject librarian, she manages collection and provides instruction, reference and consultation services for the engineering faculty and students. Her work also focuses on data management education and outreach to engineering students through presenting Data Management topic to an Engineering Ethics course and library workshops. She holds a MA in Information Resources & Library Science from The University of Arizona (Tucson, AZ), and a BS in Biotechnology from Jiangsu University of Science and Technology (Zhenjiang, China).

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Kari Kozak University of Iowa Orcid 16x16

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Kari Kozak is the Head of the Lichtenberger Engineering Library at The University of Iowa. She provides instruction, reference, and consultation services to student, faculty, and staff within the departments and research centers in the College of Engineering as well as the Department of Computer Science. Kari holds bachelor’s degrees in Meteorology and Environmental Studies from Iowa State and a master’s degree in Library Science from the University of North Carolina – Chapel Hill. Before coming to the University of Iowa in November of 2008, she worked at Texas A & M University as a Science & Engineering Librarian.

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As engineering librarians, we recognize that understanding our faculty research needs is an ongoing endeavor. It that is a repeated continuing learning process throughout our time serving engineering faculty with diverse research interests. However, the time-intensive learning process may not efficiently help engineering librarians quickly develop an overall view of the changing and evolving various departments. It’s also challenging for early-career librarians who are new to engineering librarianship or do not have relevant subject background.

In order to tackle the problem, we the authors explored research topics of our faculty’s work using a topic modeling technique called Latent Dirichlet Allocation (LDA) which is a type of statistical topic model and a machine learning algorithm for discovering the research topics from text data. We retrieved thousands of bibliographic records of faculty publications as the text data, especially for the title, abstract and keywords, from Web of Science, removed duplicates and cleaned up the data. Next, we fed the data into the machine, built LDA models and generated research topics from the data. As a result, we determined the optimal research topic number of 25 and interpreted the research topics based on the visualization of the LDA results.

In conclusion, our experiment with the LDA approach not only helped us quickly develop an understanding of faculty research needs,, but also would would provide good evidence from which to make decisions on collection management, reference and library instruction,. and showed the possibility of academic libraries to make use of data and data science techniques in the era of big data.

Zhang, Q., & Kozak, K. (2019, June), Quick Understanding Our Engineering Faculty Research Needs Using Topic Modeling Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--33223

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