For the past few weeks, I have been thinking a lot about how HT+Bookworm can be used in the classroom, in the context of undergraduate teaching. I started talking about this in the previous blog post, in which I mentioned that HT+Bookworm was about to be tried out in Prof. Christi Merrill’s undergraduate Comparative Literature class at the University of Michigan as part of a classroom activity.
We had the students carry out that in-class activity on November 19, 2015. What follows is a description of what happened, and a reflection — incorporating feedback from the HTRC user community during the conference call — of what we learned from it.
Christi had thoughtfully prepared an assignment that she wanted the students in the class to turn in by the evening, after they had had a chance to explore HT+Bookworm in class during the day. The assignment, which had students working in small groups of three or four each, was this:
- Each group of students has to create a document with screenshots of HT+Bookworm plots for at least five queries (corresponding to five keywords that they get to choose — the only restriction is that the keywords must be related to “translation” in some way, as that is the subject of the class).
- From the HT+Bookworm plots, the group then identifies source texts (each source text is a volume from the HathiTrust Digital Library) that they find to be of particular interest to them. (Recall that by clicking on particular points on the plot, students can display on the screen the digitized source texts that contributed to that point in the plot — as HT+Bookworm’s Graphical User Interface makes it possible to drill down to them from the usage trend plot.) The student group is asked to identify at least nine such source texts.
- Using the Translation Network Builder tool (developed by Christi’s team at Michigan), the students then create a network illustrating relationships between the source texts from the HathiTrust Digital Library that they have identified. This being a polyglot class around the theme of translation, Christi provided some additional requirements/constraints on the assignment. These were the following:
- The nine source texts should encompass at least three different languages between them
- These nine source texts should contain all the five query words between them
- The group then adds to the network a quotation from each of three sources, chosen such that each quotation is in a different language. The quotations should help illustrate the thematic content of the network.
- The group turns in the URL for this translation network they created, to the instructor. Here is an example of a network, which was turned in by one of the groups in the class.
What was the pedagogical utility of using HT+Bookworm in this way? One student wrote something in the post-class survey that is of interest here — (s)he wrote:
It helped me think about… the context of my source work beyond the moment of its creation.
Last but not the least, what additional affordances did the Translation Network Builder built by Christi’s team provide for this student exercise, and what synergy did it create with HT+Bookworm? The Translation Network Builder team has an in-built functionality that comes in handy — this functionality is that it allows you to build a graph in which, when you are intending to add a new node (i.e., a new vertex) to the network graph, then, if that new node is a volume contained in the HathiTrust Digital Library, then you can choose to create what the Translation Network Builder calls a “work” node. (By “work” it means what means what, in library parlance, one would call a “volume”.) You can do this by selecting “Search from Record” from the contextual menu when seeking to create the new node using the Translation Network Builder. This is helpful to the students, as, once they have identified, from their use of HT+Bookworm, which source texts from the HathiTrust Digital Library they want to put in their network, adding those volumes as vertices in the graph is easy: as long as the student group members know the volumeID of the volume (which they can note when they identified the volume while using HT+Bookworm), they can bring up that volume’s representation (a graphic showing that book’s cover from the HathiTrust Digital Library — a graphic that can be made clickable in the future in order to bring up the actual digitized text on-click) and add it as a node/vertex to the network graph.
However, an important thing to remember is that Christi’s Translation Network Builder tool is primarily intended to build networks in which quite a few of the nodes/graphs stand in a relationship of “translation” to each other (i.e. are translations of each other). In the case of this student exercise, however, this is not the case — the students, in this exercise, are finding volumes from multiple languages, conceptually linked to each other by relations that the students can describe (and annotate on the edges between the graphs, with those edges representing the relations). However, these relations, in the case of this particular exercise, will not simply be one of translation (in the sense of the volumes being translations of each other). So, the translation-specific functionalities of the Translation Network Builder tool (such as one that makes it easy to add “translated-from” and “translated-to” types of links/edges between nodes) will be greatly underutilized or non-utilized during this particular student exercise.