go here Utilizing web-based tools to analyze programmatic documents in writing centers
Genie Giaimo (The Ohio State University, US)
In many ways, writing centers are large data repositories. Writing centers collect numerous forms of data including, but not limited to, registration information, intake forms, report forms, surveys, observations, focus groups, etc. However, because of the amount of data writing centers collect, it is not always possible to fully analyze the data, or even store it in accessible ways. Furthermore, it appears that writing center administrators are often left “reinventing the wheel” whenever the need for new documentation arises (i.e. creating new session note questions or creating new assessment of intake forms). All of these features of the modern day documentation of writing center administration and “work” are time consuming and often redundant, and do not necessarily yield immediately relevant results. For example, Bugdal, Reardon and Deans (2016) identify tutors’ frustration with the time consuming nature of session documentation, because, to many, there is no clear reason for filling out session notes, or other forms. It appears that the robust documentation and data collection in our centers is divorced from our practice. Assessment of these documents, then, is a necessary step in closing the loop of recording and tutoring.
We hope to provide a method of analysis using digital tools for other writing center administrators interested in developing their own session notes assessment. Moreover, we believe that our project responds to the call made by Mackiewicz and Thompson (2016) and by Mackiewicz (2017) for large, multi-institutional data sets with controlled data collection methods that will enable generalizable findings about writing center work beyond individual centers and institutions (Mackiewicz, 2017, p. 145). Our project utilizes Voyant, a free, open-access web-based application for performing text analysis on the session notes (completed after a consultation) from four different institutions: Michigan State University, the University of Michigan, The Ohio State University, and Texas A&M University. We will be analyzing documents at the institutions both individually and collectively. For this conference, however, one representative from the research team will be presenting findings from a specific data set, rather than sharing findings from all four institutions.