Text and Data Mining in the Classroom: Lessons from Wayne State University

Sponsored by ProQuest
Recorded on 07/21/2021

Posted in Library Technology and IT

How can librarians partner with faculty to integrate TDM into the classroom?


Text and data mining (TDM) is an essential skill across disciplines, but historically, it’s only been accessible to those with experience in data collection, data manipulation and coding. In this webinar, you’ll see how Wayne State University library is making TDM accessible to students regardless of their ability to code.

Alexandra Sarkozy, Science and Digital Scholarship Librarian at Wayne State, partnered with ProQuest and Wayne State faculty to integrate TDM into the classroom. She’ll share the approaches the library took to help faculty integrate TDM tools into teaching and learning, providing students with access to data visualizations.

You’ll also hear John Dillon, Ph.D., product manager for TDM Studio, discuss how other universities are incorporating TDM into the classroom. He will demonstrate data visualizations and discuss how they can help faculty, students and researchers gain insights from millions of documents.


  • Alexandra Sarkozy

    Science and Digital Scholarship LibrarianWayne State University

    Alexandra Sarkozy is a Science and Digital Scholarship Librarian at Wayne State University. She works with faculty and students to conduct literature searches, teaches database and citation manager best practices, and brings digital humanities tools and techniques to undergraduate and graduate classrooms. She was a member of the first CUNY Digital Humanities Research Institute cohort of community leaders in 2018 and continues to advance teaching and research in digital scholarship.

  • John Dillon, Ph.D

    Text and Data Mining Product Manager for TDM StudioProQuest

    John Dillon, Ph.D., is the Text and Data Mining Product Manager for TDM Studio at ProQuest. His expertise lies in creating EdTech products which focus on data visualization, natural language processing and data science. He has worked previously as a postdoctoral researcher with the University of Notre Dame, USAID, and IBM Research. John has published research on sentiment analysis, machine learning and learning analytics.