Krista A. Murchison is a lecturer in medieval literature at Leiden University, in The Netherlands. Her research centers around the popular vernacular literature of England and the productive ways in which contemporary digital culture and medieval textual culture illuminate each other. Her experience with digital pedagogy includes leading her students’ production of a web edition of two Middle English lyrics and co-designing an adaptive platform for learning Old English.
There are several great platforms—including an app— for teaching transcription, the process of copying out text from a medieval manuscript. But what if you want to give students experience working with one unique scribal hand or transcription situation? I recently designed a pair of quick interactive modules that does just this, using the ubiquitous learning management system Blackboard Learn. This post will explain the pedagogical motivation behind the modules and give a step-by-step guide for anyone interested in designing one.
The modules were created for a digital project-based MA course on the theory and practice of textual editing. The course, which was designed and taught as a collaboration between me, Prof. Erik Kwakkel, and Dr. Thijs Porck, was focused on an online multimedia project on one of Leiden’s most renowned medieval manuscripts, a fifteenth-century collection of John Lydgate’s poetry (Leiden University Library, Voss. Germ. Gall. Q. 9). The project was aimed at encouraging students to think through the complexities of textual transmission (both medieval and modern) and at giving them sought-after skills in editing, collaborative project management, and web-based communication, while producing a work of lasting value to the wider scholarly community.
A key component of this project was a collaborative transcription of texts in the Lydgate manuscript. Groups of students were responsible for transcribing several manuscript folia, and these completed transcriptions were combined for the (forthcoming) final project. Our students had a range of prior experience with medieval textual culture, and it was crucial that they develop some expertise with reading manuscript texts on their own.
Transcription, Precision, and Computers
Traditionally, a transcription requires a strong fidelity to the text on the page—one that occasionally calls for a willingness to set aside one’s ideas of “correct” Middle English, abbreviations, or letter forms. Indeed, it has been remarked that one of the better early transcriptions of the classic Old English poem Beowulf, known as Thorkelin A, was done by a man who knew no Old English at all, his unfamiliarity with the language preventing him from interfering with the text of the poem. In order to reduce the need for judgment calls in any transcription, a comprehensive policy needs to be in place for how to approach the occasionally chaotic reality of medieval manuscript texts, which can include scribal omission, marginal additions, and damage caused by woodworm snacking.
Such consistency was especially key in the context of this digital project, since all parts of the collaborative transcription had to match. My goal, then, was not just to teach students how to transcribe accurately, but also to teach them how to follow our established transcription policies with precision and aplomb. This level of accuracy is one computers are particularly well equipped to handle—and, with this in mind, I designed a set of Blackboard modules to help train our students.
The first module was an interactive introduction to the intricacies of transcription and the particularities of the manuscript and our transcription policy (which we based on that described by Raymond Clemens and Timothy Graham in their Introduction to Manuscript Studies). This module was structured around a simple multiple-choice quiz. Students were given a bit of information at a time about manuscript transcription and then asked to answer a question based on what they had just read.
The second module was designed to give students experience transcribing a larger portion of text without help. Students were given a particular folio (110v) to transcribe and then asked to enter their transcriptions into the Blackboard module.
Since the module graded the transcriptions automatically, students received instant feedback on their transcriptions and could see immediately where their transcriptions diverged from the answers.
A transcription that matches the answer set is marked like this:
A transcription that doesn’t match is marked like this:
Because both modules were designed for training purposes, the grades were not counted toward anything; instead, students were encouraged to look at and learn from any mistakes that the modules identified so that they would be prepared for the collaborative class transcription activities.
How to Build a Transcription Module
To create a similar module in Blackboard Learn, select “Tests, Surveys and Pools” (under “Course Tools”). Then select “Tests” and click on “Build Test.” Blackboard will prompt you to enter a description and instructions; whatever instructions you enter here will appear during the module, so this is where I included information about the transcription guidelines. It’s also a good place to include any special characters students may need. My instructions looked like this:
Step 1: Dividing the transcription text into segments
Blackboard can automatically check short answers against a template, but it isn’t really designed for checking longer passages like a transcribed folio, so we have to get a bit creative by dividing the text up into short segments and treating each segment as its own “question.”
For simplicity’s sake, I divided my text by stanza and made each stanza into its own “question.”
Once you’ve decided how to break up your text, you will need images of it. Cut these to correspond with your segment divisions using Paint or other image software and save them as separate files.
Step 2: Building the questions
From the “Create Question” menu, choose “Fill in Multiple Blanks.” This will be where students enter their transcriptions for the first segment of text. Give this first segment an easily identifiable title (I chose “Stanza 1”). Then insert the image for the first segment of text and align it to the right.
The next step is to create a text box for any portion of the text that students will transcribe. Use square brackets to mark anywhere the text boxes should go. I created a text box for each line of text in the segment as so:
Step 3: Supplying possible answers
On the next page, supply the desired transcription for each of the text boxes created. If more than one transcription of a given line is possible, select the number of possibilities that are acceptable and enter each valid transcription separately.
For line 4 here, I wanted the module to accept both “a child” and “achild” to account for variations in students’ transcriptions, so I entered the line as so:
On the next page you can enter feedback for correct or incorrect answers; I left this section blank and hit submit.
Step 4: Adding the other text segments
Once you have completed one segment of the text, repeat steps 2 and 3 for each segment of the text remaining. The whole process took me about 5 minutes for four stanzas, though it will take longer for a longer portion of text. When you’re done, click “OK.”
Step 5: Using the module
To use the module for a course, choose where in your Blackboard site you want to include it, select “Assessments,” and choose “Test.” Choose the module you have created from the list and click “Submit.”
The next page lists options for deploying the module; since mine was designed for practice purposes, I chose “allow multiple attempts”; this option allows students to try a module multiple times and learn from their mistakes immediately.
Did it work?
Everyone finished the training and many tried the transcription module multiple times until their transcriptions matched the answer set. After completing the modules, students proved relatively adept at transcribing folia on their own and they all followed the established system for recording abbreviations. In this respect, the modules were a success.
But it must be remarked that the layout of the modules—what we might call, borrowing a term from manuscript studies, their ordinatio—is not particularly elegant. And there are more pragmatic problems with this system as well; since it grades each text box as a unit, it does not point to the exact location of a mistake within a line, and a student must identify it. It would be possible to fix this system by giving each word of the transcription its own text box, but to do so would be time consuming. It will perhaps not surprise anyone to learn that Blackboard was apparently not designed with medieval transcription exercises in mind.
In addition to these drawbacks, the instructor must invest some time into the design of the module, since all possible transcriptions must be entered into the answer set. The possibility of multiple acceptable versions of a transcription, and the extra work these entail, highlights that transcription, while purportedly aimed at a straightforward, impersonal, and objective translation of manuscript text into print, nevertheless involves a degree (however slight) of personal judgment. The classic scholar of editing A. E. Housman once claimed that textual editing is both a science and—under the editor’s discerning eye—an art; these modules show that transcription, too, involves some degree of art.
Krista A. Murchison, Leiden University