Prior Knowledge

Fagan began her survey in 2019, testing a digital workflow using free and open-source software and tablets and phones that were deliberately chosen because they were not the most current versions and therefore less expensive (Figure 1). In the first season, the hardware included three Samsung Galaxy Tab As from 2016, with prices between $212 and $227 at the time of purchase in 2019. In the second season, which was in 2022, the team tested the accuracy of a 2020 Xiaomi 10T Lite mobile phone that was purchased in Armenia in 2022 for around $300 and a 2021 Google Pixel 5a phone that was purchased in the United States in 2022 for $449. The software used included QGIS, QField, Open Data Kit (ODK), Google Drive, GPSTest, and GPX Viewer. Fagan purchased GPX Viewer Pro for $4.99 for her tablet so that she could record transects if needed, but all other software was free. The workflow involved using QGIS to make transect grids, exporting them to GPX Viewer (which had better visibility in the field than QField), collecting data using an individualized form that Fagan designed in Open Data Kit, storing and organizing the data in Google Drive, and then uploading .csv data files back into QGIS (Figure 2). Her goal was to test whether a financially sustainable digital workflow would have sufficient accuracy to justify the use of older, more economically priced tools. The positional accuracy of the hardware ranged from 7 m to 8 m for the tablets, and from 3 m to 5 m for the smartphones. Fagan's second project in 2019 was a two-day GIS workshop that she ran for colleagues at the Institute of Archaeology and Ethnography of the National Academy of Sciences, Republic of Armenia, in Yerevan. This workshop demonstrated the necessity of explicitly assessing prior knowledge.

Figure 1. Selected results from the 2019 and 2022 seasons of survey, focusing on entrances to the Azat River valley near the site of Garni. (Map sources: QField Satellite; Open Street Map.)

Figure 2. Screenshots of preparations for working in the field: (left) screenshot from the Xiaomi 10T Lite of the GPX Viewer Pro transects; (right) collection form built in Open Data Kit's form builder, which was retired in February, 2024. They now direct users to their built-in XLSForm template.

In the scholarship of teaching and learning, prior knowledge is discussed as a key to effective instruction, because it helps learners construct their new knowledge (Bransford et al. Reference Bransford, Brown and Cocking2000:10, who cite decades’ worth of pedagogical studies). Assessing prior knowledge is a pedagogical technique that we all do to varying degrees, consciously or unconsciously. Every time we write a syllabus for classes beyond introductory levels, we have to consider what we think students will bring to the class. For example, when Fagan teaches research classes, she gives questionnaires at the beginning of each semester to learn what the students’ prior research experience has been so that she can tailor the curriculum. Consequently, for the GIS workshop in Armenia, Fagan first considered what knowledge participants ought to have acquired by the time they leave the workshop. Second, the principle of prior knowledge helped her structure the content and delivery after she attempted to anticipate the possible degrees of prior knowledge regarding GIS.

The workshop had two goals: (1) it was designed so that participants would understand the utility and range of capabilities of GIS, and (2) it introduced key resources, including where to find local datasets and how to access the online QGIS community and training modules so that participants could continue to learn on their own. It was expected that there would be technical challenges and, potentially, very little prior knowledge about GIS. These expectations turned out to be correct: there were technical difficulties with laptops that did not work well with QGIS, and there was a spectrum of familiarity with the technology and its underlying concepts such as map projections.

However, there were also unanticipated gaps in prior knowledge: for example, some participants lacked clarity about where their computers stored downloaded basemaps and their newly produced shapefiles. In other words, the file paths were obscured. For those of us who received our educations as computers developed, it is common to conceptualize files as existing in nested folders, available as long as we follow the file tree from top-level categories such as the Documents folder to the file's ultimate destination (e.g., Documents→Archaeology→Mapping→Armenia). But in an era of tagging files or simply searching for them, knowledge of file trees is less necessary, so perhaps less understood. This was a type of prior knowledge that Fagan had assumed everyone would share, but it is likely that she was blinded by her own expertise, as often happens when we become experts in something.

Knowledge Frameworks

This particular type of absent prior knowledge is also a helpful metaphor for thinking about the second pedagogical principle that we mentioned: the situated character of our own knowledge frameworks—our mental “file trees.” Experts use their knowledge frameworks to accomplish a number of analytical moves. We use them to identify meaningful patterns in data, to “chunk” our own knowledge into broader-scale pieces that can encapsulate more information, and to retrieve useful data or processes efficiently for the problem at hand (Bransford et al. Reference Bransford, Brown and Cocking2000:31, 50; National Academies of Sciences, Engineering, and Medicine [NASEM] 2018:90–92). We construct our frameworks out of our prior knowledge, and as we acquire new knowledge, it too is entered into (and alters) the frameworks.

And of course, how we construct these frameworks, and how we use them, is as culturally situated as we are. The editors of How People Learn II define culture as “the learned behavior of a group of people that generally reflects the tradition of that people and is socially transmitted from generation to generation through social learning; it is also shaped to fit circumstances and goals (Dirette Reference Dirette2014; Hofstede Reference Hofstede1997; see also Nasir et al. Reference Nasir, Rosebery, Warren, Lee and Keith Sawyer2006)” (NASEM 2018:22). Culture impacts our mental processes as we learn and understand the world around us, including memory and perceptions, which are also impacted by our prior knowledge (NASEM 2018:25; see Cobb et al. [Reference Cobb, Cobb and Azizbekyan2022] about culture and field projects specifically). This is helpful when we consider any type of collaboration, because it means that we bring different frameworks for understanding and interpreting data to the project.

Fagan's experiences on survey in 2022 offered a clear demonstration of the different knowledge frameworks among team members. The survey was designed to illuminate the temporal landscape in the Azat River valley, in particular, around the classical and medieval period site of Garni (Figure 3). In 2022, we were joined by Inesa Karapetyan and Armine Gabrielyan, both of the Institute of Archaeology and Ethnography in Yerevan. Dr. Gabrielyan, accustomed to making maps in Google Earth, brought her knowledge of digital mapping and technology to the field, so after we translated the collection tool into Armenian, she quickly and fully understood the workflow and collected data. Dr. Karapetyan has a deep understanding of the geographical, floral, and historical landscape context but is less accustomed to using digital tools to get a bird's-eye view of the landscape. As she walked alongside another team member, we were able to capture her wealth of information in the survey notes. Both colleagues had distinct expertise, so they each contributed to the project according to their individual developed knowledge framework.

Figure 3. Looking upriver toward the source of the Azat River in the Geghama Mountains. The classical period temple at Garni is visible on the promontory. (Photo by Elizabeth Fagan.)

Training Traditions

At that time, however, Fagan was thinking more generally in terms of “strengths” or “skills” that the members of the team brought rather than the knowledge frameworks into which they might fit the survey's digital workflow. In 2024, when the authors began to interrogate digital archaeology, and then collaboration in Armenia, our discussion turned to training traditions. Diana Mirijanyan is a perfect example of the results of distinct training traditions. She began her training at Yerevan State University, where the model of learning was for students to hear directly from the scholars who excavated and knew the material most intimately and then thoroughly absorb information they heard. She also attended the University of Chicago for one quarter, where she discovered that she was expected to read all the material first and use class time to discuss it. In our conversations about her training and about collaboration, she brought up Flannery's [Reference Flannery1982] article about his trowel, which made an impression on her in that he could write an article about archaeology yet not about archaeology at the same time. She talked about how even the definition of archaeology—not just the way it is taught—could be distinct in the eyes of American scholars and Armenian scholars. When she later attended UCLA for a semester, she found an environment that blended Chicago and Yerevan in some ways: she was able to focus directly on medieval archaeology in Armenia, which had been impossible in Chicago. However, as in Chicago, she realized that her work at UCLA needed not only a theoretical foundation but also a broader contextualization than solely within the scope of medieval archaeology in Armenia. Mirijanyan's experiences in distinct environments demonstrated to her that each training tradition has advantages and disadvantages. Her experience excavating in Armenia with international collaborators has illuminated how frequently we take for granted both our own traditions and the knowledge frameworks we create from them. She has witnessed how this implicit use of frameworks can lead to collaborations where the possibilities of interpretation were more limited than they could have been.

One example Mirijanyan brought up stems from her work with a team of archaeologists from France, with whom she excavated the fortress of Dashtadem in northwest Armenia. She pointed out that the heads of the expedition were experts in the archaeology of the Armenian city of Ani, now in eastern Türkiye, and that they were trained as architects. This background led them to focus on the structures and architecture of the fortress, leading to useful publications about the fortress in the context of its relationship to Ani. She wondered, though, what publications might have been produced if an archaeologist trained in the Armenian tradition had instead focused more on stratigraphy and the excavated material culture. If she and the international scholars had discussed their different sets of expertise and used their distinct knowledge frameworks to interpret all the data—architectural, stratigraphic, and material—what might have been the result? Similarly, if Fagan had more carefully examined her GIS workshop participants’ prior knowledge, or more thoroughly sought to make sure everyone on her survey had the same level of data literacy (see Kansa and Kansa Reference Kansa and Kansa2021), what else might they all have accomplished (Figure 4)?

Figure 4. Looking downriver toward the Ararat Plain, site of the ancient capital of Artashat, with Mt. Ararat on the horizon. (Photo by Elizabeth Fagan.)