Critical Data Literacy in the Humanities Classroom

Brandon T. Locke (blocke@msu.edu), Michigan State University, United States of America

1. Humanities data and data in our daily lives

As our world becomes increasingly data-driven, data skills and literacies (including the ability to assess data gaps and coverage, misleading visualizations, and the ethics surrounding data collection, usage, and sharing) are becoming crucial tools to our lives, both inside and outside of higher education. Scholarship across disciples is moving towards more data-intensive work, and scholars are increasingly expected to include open access to the data collected and used. At the same time, devices and software we use, the platforms we use to communicate, and the places we shop are increasingly enabled by the collection of data about our purchasing habits, web history, and contents of our email inboxes. Governments at all levels are increasingly collecting and using data to alter policies and direct day-to-day activities, ranging from transportation infrastructure to policing.

While much (though certainly not all) data-driven scholarship may seem significantly different from third-party data collection and data-driven policing, the former provides an opportunity to prepare students to understand, critique and improve the latter. Learning about the accurate and ethical and collection and usage of data and algorithms is a crucial part of liberal education that can help students better understand the processes around them, and better prepare them to apply those ethics and practices in the workplace and civic realm after graduation.

While many may think of data literacy as being the work of Computer Science departments, or perhaps library workshops targeted at researchers, the author argues that teaching these skills in the humanities classroom is fruitful for both the development of disciplinary knowledge and for developing crucial skills for use outside of the humanities classroom. Humanities data provides an excellent space to think critically about how people, ideas, and culture can and cannot be captured and analyzed through data. Comparing the data structures of colonial record keeping with the structures communities develop to document themselves provides clear lessons in the power of determining who and what gets documented, in the values that each community holds, and in privacy, ethics, and consent. Text mining novels, government records, or newspapers facilitates critical thinking about the value of metadata, the ability (or lack thereof) to derive meaning from large collections of text, and the use of different algorithms and approaches to ask different questions.

At the same time, the ability to think about humanities sources as data, and to properly curate and analyze them as such, provides a productive way to engage more with the way we conceptualize the sources, the way disciplinary knowledge is constructed and practiced, and the affordances provided by digitized and born-digital resources.

2. Data Challenges in Higher Education

The process of gathering, "cleaning," and organizing data can be incredibly time-consuming and difficult to prepare for. It can be tempting (and in many cases, required), to provide students with pre-prepared data to for analysis. Allotting time, either as in-class instruction or independent, project-based work, can take up weeks of time and can be a grueling disincentive for engagement. However, working critically with data rather than working with pre-packaged, pre-prepared datasets also aides us in the integration of digital humanities methods into the classroom, and better enables us to teach students emerging research methods through the full course of humanities research. Students can get a glimpse of the intellectual labor that goes into data collection, organization, and curation; not just in the final analysis.

There are several data literacy models that have shown success in other contexts. Data curation training often occurs in university-wide workshops or seminars, and are often brief and necessarily divorced from content and community practices (Carlson and Johnston 2015 p. 2-3). The Data Information Literacy (DIL) initiative, led by Jake Carlson and Lisa R. Johnson, is an extension of the ACRL Information Literacy Framework that focuses on both the creation and consumption of data (ibid.). DIL is designed to be integrated into courses and research labs in the context of subject-specific data and domain-based community practices, but is primarily intended for faculty, staff, and graduate students working on peer-reviewed publication (ibid., p. 2-3). The Library-Led DH Pedagogy: Modeling Paths Toward Information and Data Literacy symposium facilitated productive conversations about the topic of data and information literacy in the digital humanities, but has not produced significant scholarship, models, or frameworks (Padilla et al. 2015).

In addition to making the case for teaching critical data literacy in the digital humanities classroom, the author will discuss both practical and theoretical approaches to data literacy in the undergraduate classroom that speak to the impetus behind teaching data literacy in the humanities: for greater disciplinary knowledge and understanding, to better facilitate digital scholarship and knowledge production, and to prepare students to better grasp, interrogate, and work with data in the public and private sector as citizens, employees, and employers.


Appendix A

Bibliography
  1. Carlson, J. and Johnston, L. eds. (2015). Data Information Literacy: Librarians, Data, and the Education of a New Generation of Researchers. Purdue Information Literacy Handbooks. West Lafayette, Indiana: Purdue University Press.
  2. Padilla, T., Smiley, B., Miller, S., and Mooney, H. (2015). "Modeling Approaches to Library–Led DH Pedagogy," DH 2015 Global Digital Humanities Conference Abstract. http://dh2015.org/abstracts/xml/PADILLA_Thomas_George_Modeling_Approaches_to_Libr/PADILLA_Thomas_George_Modeling_Approaches_to_Library_le.html (accessed 15 August 2017).