Text and data mining (TDM) is a process of increasing interdisciplinary potential and one with many practical applications for music graduate students. TDM, however, remains a topic rarely introduced in the music bibliography course. Understandably, talk of artificial intelligence, algorithms, and programming languages are intimidating to music students, but thanks to software applications, knowledge about these computer science topics are not required to participate in research using TDM. This presentation explores ways to introduce digital humanities to music students through TDM.
In our presentation, we will discuss two approaches to incorporating TDM into the music bibliography course, focusing on two student objectives: increasing ease and engagement with finding research information and discovering patterns and underlying topics of music research. Our experience suggests that students find these tools and approaches both useful and accessible.
Presenter A will discuss their approach to teaching the Google Ngram Viewer and JSTOR Text Analyzer in the graduate music bibliography course at an R1 public institution. Their assignments engage students in new approaches to finding research with these easy-to-use tools. They will update their recently-published survey results regarding student attitudes to these tools that combine powerful TDM with simple interfaces.
Presenter B will discuss the TDM assignment they assigned to students in their Research Methods for Performers course, a small seminar taught at a private, medium-sized R2 institution. Using ProQuest’s TDM Studio tool, this assignment capped off two weeks spent discussing digital humanities and text and data mining research methods and involved using the method of topic mapping to uncover hidden tropes in the dissertations and theses on a given research area.
The learning outcomes for attendees include the following: gaining an understanding of how text and data mining relates to graduate music students’ research practices; understanding some methods for text and data analysis; and learning specific tools that can be easily incorporated into teaching music research methods for graduate students.
Greene, T., & Sampsel, L. (2023, Mar. 2). Text and data mining applications for teaching music bibliography [Conference session]. 2023 Meeting of the Music Library Association and the Theatre Library Association, St. Louis. https://vimeo.com/806953688/77c5f5dbe8