Bridging the Gap

I am so excited that Celeste Sharpe and I have been awarded an ACH microgrant for “Bridging the Gap: Women, Code, and the Digital Humanities”. This grant will help us create a curriculum for workshops aimed at bridging the gender gap between those who code and those who do not in the Digital Humanities

While our first “Rails Girls” event was quite successful, one of the most repeated pieces of feedback we received was that participants were unsure how to connect what they had learned about building a Rails application to their own scholarly work. This is not surprising - the Rails Girls curriculum is aimed at helping women develop the skills necessary to land technical jobs and so focuses on instrumental understandings of code. As our participants reported, this is not the most obviously applicable type of knowledge in the context of humanities research.

What is necessary instead, and what we have been given the grant to develop, is a curriculum that focuses both on technical skills and on computational thinking, the intellectual skill of breaking complex problems into discrete, algorithmically solvable parts.1 It is computational thinking that is necessary to answer the “but what can I do with it?” question. And it is computational thinking, even more than technical know-how, that is necessary for developing interesting research questions that can be solved with computers.

This emphasis on computational thinking, as opposed to instrumental knowledge, is also a response to a growing concern regarding the emphasis on tools within DH. This concern has already been articulated very well by Matthew Lincoln in his post “Tool Trouble” and by Ted Underwood in his talk “Beyond Tools”. To echo some of Matthew’s points, the focus on tool-use in DH runs the risk of hindering rather than developing computational thinking, hiding the computational work behind a (hopefully) pretty interface and deceptively clear and visually interesting results. The emphasis on “tool-use” reinforces a sense of digital humanities methods as ways to get answers to particular questions along the way to constructing traditional humanities arguments. As a result, digital work undertaken in this manner often fails to grapple with the complex, and often problematic, theoretically-inflected models that digital tools produce.

(As a grand sweeping side note, I think it is this tool-centric approach to digital humanities that is most likely to be adopted by the various disciplines. When computational methods are merely tools, they pose little challenge to established modes of thinking, making it reasonable to say that we will all be digital humanists eventually.)

Tools are useful - they provide a means of addressing particular problems efficiently and consistently. But tools are packaged ways to solve particular problems in particular, computational ways, and are designed according to particular theoretical and philosophical assumptions. And they must be interacted with as such, not as “black boxes” or answer generators that tell us something about our stuff.

Moving beyond tool-use requires computational thinking, and is the intentional combining of computational and humanities modes of thinking that, I think, produces the most innovative work in the field. In learning to think computationally, one learns to conceptualize problems differently, to view the object of study in multiple ways and develop research questions driven both by humanities concerns and computational modes of problem solving. One also becomes able to reflect back on those computational modes and evaluate the ways the epistemological assumptions at work shape the models being created.

Rather than teaching coding as one tool among many for answering humanistic questions, it is increasingly clear to us that it is necessary to teach computational thinking through learning to code - to learn to think through problems in computational ways and to ask new questions as the result. It is this pattern of thinking computationally that we are interested in fostering in our workshops.

  1. This is my reformulation of Cuny, Snyder, and Wing’s definition of computational thinking as “… the thought processes involved in formulating problems and their solutions so that the solutions are represented in a form that can be effectively carried out by an information-processing agent.”