This section deals with the didactic principles and the overall planning of the course. The following section analyzes the “performance” that actually took place in the classroom.
Content: Developing the content of the course was based on the two prevailing conditions: first, to cover the editorial process as a whole, beginning with the selection of the material and ending only with the publication on the internet, and second, rather than using separate exercises, for the single parts to employ one object of study and process it through the various stages of what we regarded as a typical workflow. This required that the results of one phase should influence the course as a whole, by becoming the input for the next phase. For instance, the agreement on a model for encoding textual features needs to be observed in the actual encoding; the definition of metadata made within the course provides the basis for the customization of the database etc.
54 Digital Humanities Pedagogy
As a model for the project workflow, we followed the digitization process used by the Brown University Women Writers Project or WWP (http://www.wwp.brown.edu/), in many ways typical of XML-based digital humanities projects, and modified it to suit the particular needs and scope of the workshop (Figure 2).
Figure 2. Reference process for digital editing.
Data Modeling: This is understood as the core process aimed at creating digital resources, such as an edition.8 In our model workflow, data modeling occurs in two phases:9 one models the representation10 of the source data as input (in this case, texts and images of the documents) into the digital medium (i.e. it describes the “real world”), and the other models the presentation of the data as output (Learning Goal 4).11 Here we follow Willard McCarty’s definition:
By “modeling” I mean the heuristic process of constructing and manipulating models; a “model” I take to be either a representation of something for purposes of study, or a design for realizing something new.12
Modeling the representation of data means analyzing the material with the project’s objectives and deriving from both—by applying modeling techniques such as abstraction and generalization—the rules
8 Theorizing data modeling, developing models and managing the workflow around them can be regarded as the main reasoning behind digital humanities as a discipline.
9 See also Figure 1 in which we differentiate the outcome of the two modeling phases also technically: the XML schema for the representation of the source data and (mainly) the stylesheets (XSLT, CSS) for the presentation of the texts in the web (or any other medium).
10 For a definition of data representation and presentation in digital editing, see Patrick Sahle, “Zwischen Mediengebundenheit und Transmedialisierung. Anmerkungen zum Verhältnis von Edition und Medien,” editio 24 (2010): 30.
11 In the German language, model theory distinguishes nicely between the two functions of models: an Abbild (of the reality)—the model of—on the one hand, and a Vorbild (the blueprint)—the model for—on the other; see Herbert Stachowiak, Allgemeine Modelltheorie (Wien: Springer, 1973).
12 Willard McCarty, “Modeling: A Study in Words and Meanings,” in A Companion to Digital Humanities, ed. Susan Schreibman, Ray Siemens, and John Unsworth (Malden:
Blackwell, 2004), 255, emphasis original.
2. Hands-On Teaching Digital Humanities 55 for transforming the material into the digital medium. This includes its metadata, to cover the corpus of letters and to allow for database integration, as well as defining rules for text encoding.
In data modeling, a two-step approach suggested by Alexander Mehler and others was envisaged.13 Beginning with the analysis of the material and the requirements, we would develop a conceptual model (expressed in natural language) and derive from this a formalized (logical) model (expressed in a formal language). We decided on entity-relationship models for metadata and XML-schema for textual data.14
Transcription and Encoding: In the context of this course, we understand transcription as the process of transforming the texts carried in the letters as physical objects into machine-readable format, basically as a sequence of characters. Encoding is the next step of enriching this (raw) data into processable information by making explicit structural and semantic features.15 We chose to use the P5 Guidelines of the Text Encoding Initiative as the basis for both encoding metadata and text,16 and to enable us to teach their basics (Learning Goal 5).
Publishing: This encompasses the design of the to-be-created digital edition in terms of data presentation or visualization (Learning Goal 6) and the interaction between the user and the data by means of a user interface (human-computer interaction). We used the WWP as a reference and, following its strong emphasis on quality assurance in the model, we took this also as part of the publishing workflow.
Modeling the presentation of the data necessitates analyzing (or reconsidering) the models developed for data representation as well as the project’s objectives in order to identify the design of the online resource, i.e.
to create a blueprint for the digital edition. The purpose of a digital edition consists in the interplay of these two models: “modeling of something readily turns into modeling for better or more detailed knowledge of it.”17 These considerations led us to emphasize data modeling strongly in this course.
13 See Peter Langmann, “Einführung in die Datenmodellierung in den Geisteswissenschaften,”
xlab, n.d., http://www.xlab.at/wordbar/definitionen/datenmodellierung.html.
14 On the entity-relationship model, see Peter Pin-Shan Chen, “The Entity-Relationship Model: Toward a Unified View of Data,” ACM Transactions on Database Systems 1, no. 1 (1976): 9–36.
15 For a more elaborate definition of transcription and text encoding in the context of the creation of electronic texts, see Allen H. Renear, “Text Encoding,” in A Companion to Digital Humanities, ed., Susan Schreibman, Ray Siemens, and John Unsworth, 218–39.
16 TEI Consortium, TEI P5: Guidelines for Electronic Text Encoding and Interchange, version 1.9.1, March 5, 2011, http://www.tei-c.org/release/doc/tei-p5-doc/en/html/.
17 McCarty, “Modeling,” 257.
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Digital Editing Infrastructure: This infrastructure is meant to support the whole workflow as much as possible. This includes the production of the digital edition as well as serving as the “host” for its publication. The role such infrastructure plays in both the production and publication process must be taught (Learning Goal 3).
The outline of the course contents helped us to sharpen learning goals so that in addition to the main objectives (as described in the previous section) some second-order goals (documented in Table 2) could be achieved.
Goal Learning Outcome
8 To gain insight into the different approaches of digital editing, to know selected sample projects and to assess them in terms of claim, audience, functionality, methods of data representation, data presentation and human-computer interaction.
9 To know selected tools supporting the various phases in digital editing.
10 To know some of the major principles as well as basic techniques of data modeling.
11 To learn fundamentals of Entity-Relationship modeling and to be capable of applying this method to an easy example.
12 To understand transcription as part of a digitization process and to know different approaches and their advantages and disadvantages.
13 To understand XML as a means for data representation and XSLT as a means for data presentation.
14 To know and be able to apply the principles and basic rules of XML;
15 To learn the main principles of the TEI and to gain insight into selected chapters of the TEI guidelines.
16 To become aware of the necessity to agree on and use standards.
17 To understand the necessity of quality assurance in a scholarly project.
Table 2. Second-order learning goals.
2. Hands-On Teaching Digital Humanities 57 To provide the students with an appropriate background, we decided to teach the additional theoretical lessons as documented in Table 3.
Lesson Description
1 History and types of (scholarly) editions since the eighteenth century.
2 Examples of digital editions: the German Text Archive (http://
www.deutschestextarchiv.de/), the kundige bok digital edition (http://kundigebok.stadtarchiv.goettingen.de/), the Thomas MacGreevy Archive (http://www.macgreevy.org/), and the Diary of Robert Graves 1935-39 (http://graves.uvic.ca/graves/).
3 Digital editions based on the SADE-infrastructure.
4 Modeling in general (following Stachowiak 1973) and its principles (such as classification, structuring, abstraction), modeling as a process, and entity-relationship modeling as an example (Chen 1976).
5 Basic principles and rules of XML.
6 Functionality of oXygen XML editor as an example of specialized XML editing software.
7 History and scope of the Text Encoding Initiative, and essential rules of the TEI Guidelines.
8 Schema, ODD, and ROMA.
9 Metadata according to TEI (teiHeader).
10 Pros and cons of different transcription approaches (e.g. OCR, double-keying).
11 Transcription of primary sources according to TEI.
12 Theory of information visualization and its role in digital editing.
13 Examples of visualizing textual data: the Republic of Letters project (http://toolingup.stanford.edu/rplviz/), Wendel Piez’s Overlapping project (http://www.piez.org/wendell/dh2010/
clix-sonnets/), and Ben Fry’s Preservation of Favoured Traces visualization of Darwin’s On the Origin of Species (http://
www.benfry.com/traces/).
Table 3. Additional theoretical lessons.
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All these considerations led to the outline of the course as shown in Table 4.
Each module was initially assigned a session of 90 minutes. We started with some “team building” to get to know each other and to learn more about the cultural and disciplinary background of the participants and then introduced the course design and the material. Most of the time in the workshop was spent with a series of theoretical, methodological and practical sessions that went step-by-step through the intended digital editorial process.
The workshop concluded with project consultations and a course evaluation.
Module Title Learning
Goal Content
M1 Introduction Course objectives and outline (T) Team building (P) Introducing the material (T) Document analysis I (P)
M2 Digital Editing Exemplary overview of printed and digital editions
Discussing the project’s (Ehrlich letters) objectives (P)
Phases in digital editorial projects (T)
M3 Digital Editing
Infrastructure I 3 The SADE infrastructure I (T) Overview of techniques / technologies involved in digital editing (T)
Accessing SADE (P)
M4 Data Modeling I 4 Theory of data modeling I (T) Document analysis II:
Metadata (P)
Developing the metadata (P)
M5 Text Encoding I 5 XML Basics (T)
Tools for encoding (P) TEI Basics I (T)
M6 Data Modeling II 4 Document analysis III: text (P) Theory of data modeling II (T) Schema development (P) M7 Text Encoding II 5 TEI Basics II (T)
Transcribing and encoding (P)
2. Hands-On Teaching Digital Humanities 59 M8 Text Encoding III 5 TEI Basics III (T)
Transcribing and encoding (P) M9 Text Encoding IV 5, 7 Transcribing and encoding (P)
Quality assurance (P) Discussion of selected problems (T)
M10 Digital Editing
Infrastructure II 3 The SADE infrastructure II (T) Putting it all together (P)
M11 Visualization 6 Theory of information
visualization (T) Introduction to CSS (P) CSS Visualization (P)
M12 User Interface 3 Implementation of
functionality into SADE (T/P) M13 Quality Assurance 7 Quality assurance (P)
M14 Wrap-Up Recommendations for further
reading and practice (T) Feedback and discussion (T) M15 Project
consultation Project consultation (P) Conclusion (P)
Table 4. Course set-up details, indicating theory (T) and practical (P) sessions.