While most of the courses associated with the Keck DCMP, including those discussed above, are firmly grounded within specific academic departments, the “core” courses by design are intended to teach students across many disciplines. This approach has its advantages, but it also results in a number of methodological hurdles. For example, the Core B Course, centered on lab-style digital technology instruction, is designed to engage students actively in creating individual projects using digital mapping software such as GIS, virtual globes, and three-dimensional modeling. Students enrolled in the first year of the program came from seven different majors, with interests that ranged across the globe and spanned ancient to modern times. While it would likely be easier to teach this course simply as a series of technological tutorials, it is vital to our mission that students grapple with the technological and Humanistic questions simultaneously. How does one evaluate projects with such a diverse focus successfully?
In this class, we found a solution in the fundamentals of humanistic inquiry, which essentially do not change across department or disciplines. Be it history, archaeology, cultural studies or English, we follow similar rules for research problem design, demand the same rigor in data collection, and expect the same critical analysis of our results.
With this in mind, we asked students to learn and experiment with the capabilities of the various technologies taught in class, focusing on what each platform could and could not do well, and how the visualization of the data differed from one to the other. As the course progressed, and students began to distinguish strengths and weaknesses of the different platforms, they were asked to create a series of projects on a topic within their own major field of interest. Each project’s design was required to take advantage of the strengths of each platform, capitalizing on the platform’s organizational and visualization capabilities to address a research question (Figures 12 and 13). Datasets must be appropriate for answering the desired question, and visualized in a clear and well-designed manner for expressing one’s argument. Students presented their digital projects in class or posted them to the class website, and fellow students were asked to critique each other’s projects for design, clarity and strength of conclusions.
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Figure 12. Screenshot of a student project investigating the cultural and economic impact of New York City’s High Line Park.
Figure 13. Iterative GIS maps of the growth of art galleries related to the opening of the High Line Park show its cultural influence; a three-dimensional model examines its relationship with the
neighborhood.
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Essentially, we discovered that the constant focus on digital design in practice—such as color, size and location for objects and lines added to a two-dimensional web-based mapping project, or the folder organization in a virtual globe project allowing different groups of objects/lines/polygons to be turned “on” or “off” collectively—led to added focus on the initial research design of the project. By thinking through how the project would be organized and appear in the digital environment, students had to think critically about the individual and collective meaning of each piece of information brought into the project: What is its meaning as entered?
Could it be interpreted or represented in a different way? How would changing its representation affect results? How does it relate to other types of data in the project? Here, then, lies an opportunity to integrate our study of the humanities and social sciences with the digital toolbox, and our means to evaluate such a wide-range of student research projects.
By assessing whether research design is (1) appropriate to the platform used, and (2) appropriate to the research question asked, both the instructor and fellow students can provide a broad critique of work outside our own areas of interest. This cuts to the heart of what it means to teach digital literacy and, in effect, offered a practical approach to teaching critical cartography.
In the case of this class, the platform was a digital one, but the skills honed to develop a research question and match it to an appropriate means of analysis and presentation of data apply to research in many formats and disciplines. By continually addressing questions of how research design and presentation influence the outcome of a project—both within their own projects and in their review of fellow students’ projects—students sharpened their critical thinking skills and made real strides in identifying problems in argumentation or visualization.
A more difficult area for critique lay in the evaluation of a student’s dataset. Indeed, it is impossible for any instructor (in the case of the first offering of Core Course B, Elaine Sullivan, an Egyptologist) to have in-depth knowledge of the types of sources available in a myriad of disciplines, which, in one class, included Chinese archaeology, modern American architecture, and ethnicity in early twentieth century Los Angeles. Student projects could not be assessed for completeness of the dataset, as the bibliography of source material used in most cases could not be fully evaluated by a non-specialist. The spatial aspect of each of the datasets gave both students and instructor a set of similar points on which to begin a critique. Initial questions spurred by the visualization of the dataset included issues of change over time, techniques of data collection, gaps in
5. Teaching Digital Humanities through Digital Cultural Mapping 145 the datasets, size and completeness of the dataset, and reliability of data. In some cases the collaborative nature of the projects, which underwent a series of reviews by fellow students in class and on the online forums, provided even more substantive analysis. Students with majors or minors in the same fields gave subject-specific critiques and recommendations on sources used for their peers’ projects. The varied nature of the data also meant that students (and the instructor) encountered dataset issues unfamiliar to them from their own fields. Class participants had to try to solve data collection issues creatively, often leading to inventive and insightful strategies that may not have occurred to those within the same discipline.
On the whole, we approached this skills class as a testing ground for incorporating digital methodologies into traditional humanistic questions. We hoped that students would take the technological and critical-thinking skills back into their departmental coursework, where future projects could and would be evaluated by subject specialists.
Thus, while questions of dataset conceptualization and visualization are of utmost importance, for this approach we left the assessment of the accuracy and fullness of the dataset itself to another time. In fact, a number of our students mapped out projects for which the gathering of a complete dataset would take more than the few weeks available in UCLA’s ten-week quarter system. They saw their final projects as only preliminary results, providing examples of how more data-rich projects could appear. What is vital for this course is not the gathering of such rich sets of data, but the articulation of what should be in the dataset, how this information should be organized and visualized, what cannot or should not be included (e.g., due to the quality and nature of the original data, the definition of the problem to be solved, or the level of representational granularity stated in the project design), and a demonstration of what these preliminary results would look like.
The final step in each mapping project asked students to evaluate the results of their data visualization. Early in the quarter, the entire class was provided with a large dataset of published archaeological finds from an ancient city. Each student mapped a section of the dataset in order to investigate a specific question about the possible function of different sections of the city. They then drew conclusions based on their digital map.
When presented to their peers, the students were amazed by how many different conclusions about neighborhood function emerge from the same set of data (Figures 14, 15, and 16). This exercise was specifically designed to spur their thinking about how data is interpreted.
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Figures 14, 15, and 16. Screenshots of three student projects mapping the same dataset. One student chose to map the full dataset by function, a second by material, the third mapped out only
selective data related to a specific topic.
5. Teaching Digital Humanities through Digital Cultural Mapping 147 A large part of the evaluation of each student project rests on how they have created and supported their argument with the data. In digital projects, this often means including screenshots of one’s GIS maps or views of one’s virtual globe as figures within a traditional written paper. In other cases, students add their thesis directly to the digital platform and create an argument their viewers must follow within the digital space. In both cases, projects are critiqued on the basic premise of good scholarship: has the author successfully argued that the data supports their conclusions?
Ultimately, the visual nature of these digital platforms leads students to engage with the data in a more sophisticated way, encouraging better analysis and more thoughtful conclusions, more interesting papers and projects, and what we hope to be real, “immersive” learning that transfers to other aspects of their academic lives.11