Joel Schneier is a doctoral candidate in the Communication Rhetoric & Digital Media program at NC State University.
Timothy Stinson is Associate Professor of English and a University Faculty Scholar at North Carolina State University.
Matthew Davis is a Postdoctoral Fellow in the Sherman Centre for Digital Scholarship at McMaster University.
This is the source
This paper examines the potentialities of N = 8) engaged with BigDIVA’s networked browsing in comparison
to use of a search engine such as Google. In doing so, we situate our study within
performative conceptualizations of human-computer interfaces generous interfacing
that emphasizes browsing as
a tool for exploring relationships between nodes in archives, as well as
Shneiderman’s (1996) joyous experience
for interfacing
with the web.
Examines the potentialities of
Conducting a search query through Google or scholarly archives is a performance. It
requires curation of key terms or phrases from memory, fingered articulation upon a
keyboard, recall from the database, selection and then uptake from a list of results.
This ubiquitous everyday performance, while providing information access and
retrieval across the world, is nonetheless a narrowly defined operation in which a
given search query is matched to a given resource. As Mitchell Whitelaw (2015)
argues, the process of browsing limits a user to understanding and exploring the
scores of information available online. If an online archive is analogous to a
museum, then Whitelaw argues that starting with a search query is the equivalent to
being told to wait at the entrance to the Louvre until you explicitly request to see
the Mona Lisa. Citing Ben Shneiderman’s two-decade-old appeal to make information
exploration a joyous experience
generous interfaces
in order to allow users to explore relationships
between nodes of information across the web. This paper examines BigDIVA (short for
Big Data Infrastructure Visualization Application) as one such instantiation of a
generous interface
that invokes performative
materiality for an online database cataloguing archives in the Advanced Research
Consortium (ARC).
BigDIVA
This paper will explore two questions that situate BigDIVA within ongoing discussions
of interfaces (see how do human participants use BigDIVA in
comparison to a more traditional browser interface such as Google? In the
simplest terms, networked browsing disrupts the expectations of traditional web
browsing. As such, these disruptions will necessarily manifest behaviorally (i.e.,
facial gestures, mouse clicks, vocalizations) and temporally (i.e., time spent on
each search task). Such distinctions in usage may suggest how new users might
interact with BigDIVA’s interface as well as networked browsing. Second, how does usage of BigDIVA’s interface (as well as self-reports of
usage) afford and evidence generous interfacing or archival liveness? While
design choices may be theoretically grounded, those choices may not always be
transparent to the intended users and thus the interface still risks becoming
functionally fixed. Thus, we intend to explore how users' experiences — as well as
perceptions of their experiences — of BigDIVA may explicitly reflect interfacing that
is theoretically grounded in the notion of performative materiality, wherein various
bodies and distributed components meet at the point of the interface to perform a
search.
This paper will proceed with a background discussion concerning performative materiality and interfaces, the technical background concerning networked browsing and BigDIVA, results from the usability study, and lastly a discussion and conclusion section.
A performative conception of human and machine interfacing places the utmost
importance on what subjects are doing in a given assemblage, and how their actions
co-constitute one another. Questions are more concerned with what a subject does,
rather than what it is The move toward performative alternatives to
representationalism shifts the focus from questions of correspondence between
descriptions and reality (e.g., do they mirror nature or culture?) to matters
of practices/doings/actions.
Barad argues that in casting aside representational concerns about inherent boundaries and properties,
bodies are instead
material-discursive phenomena
that engage in unique
performances that create meaning through their performances Human
bodies are not inherently different from
nonhuman
ones. What constitutes the human
(and the
nonhuman
) is not a fixed or pregiven notion, but nor is it a
free-floating ideality
Barad’s framework, which draws attention to how meaning is made through bodies
performing together, is crucial for digital media scholars in order to understand
that when humans and technology intra-act that the range of potential meanings,
realities, and uses are not predetermined. Drawing on groundbreaking principles of
quantum physics, particularly the philosophy-physics of Niels Bohr, Barad uses the
term apparatuses are not mere static arrangements in the world, but
rather apparatuses are dynamic (re)configurings of the world, specific agential
practices/intra-actions/performances through which specific exclusionary
boundaries are enacted.
To return to our previous question: how does a performative materiality reveal the
material-discursive phenomena at play within the interface? First, it is worth noting
that Johanna Drucker (2013) argues that the interface should be conceptualized as a
a space of affordances and possibilities structured into
organization for use
is a process or active threshold mediating between two
states
Drucker (2013), drawing upon JeanFrançois Blanchette, argues that all of these
components that meet in digital media interfaces are complex cybernetic assemblages
that constitutes a locked into relations with each other that are governed by
their material design and constraints in ways that have an effect on the costs
and efficient operation of the system
performance)
might be most evident at the point of interface, but Drucker argues that the various
components that are locked into
that performance may be
distributed at points quite distant from the point of interface. This is not so
surprising considering that ergonomic design traditions for complex computer
interfaces frequently encourage concealment of the elaborate distributed components
that make up the machine desktop
).
In the case of a website, then, the distributed components include the computer
screen, the mouse, the site interface, the network that connects the computer to the
site, the server that hosts the site, the cooling system that prevents the servers
from overheating, etc. With so many components locked together in play, Drucker
argues that [e]very person produces a work as an individual experience,
according to their disposition and capacity
Between Barad’s conceptualization of the apparatus, which emphasizes meaning-making
through intra-activity, and Drucker’s conceptualization of the interface, which
emphasizes cybernetic assemblages as a space that enfolds a distributed materiality,
it is possible to understand the potentialities of human interaction with digital
media through attention to
In this section we provide background into discussions about the application of
design principles to web browsing and online interfaces — much of which comes from
fields such as design, human-computer interaction (HCI), and user experience (UX). We
additionally make an attempt to couch this material within the contexts of ongoing
discussions in the digital humanities about how to understand how individuals
experience and
In 1996, Brian Shneiderman made a plea to designers that information exploration should be a joyous
experience
visual presentation
as well as direct-manipulation
of information would be critical for
users to be able to deliberately sort through the slew of search results without
experiencing cognitive overload. The visual
presentation
of different types of data would therefore be critical to
managing user experience, and Shneiderman outlined several data types in order to
aide how designers conceptualized the user’s relationship between data and
directly manipulating this data. His taxonomy included data that he considered
1-dimensional (e.g., textual data), 2-dimensional (e.g. maps), 3-dimensional
(e.g.,
In the last two decades, much of Shneiderman’s taxonomy is highly visible in
various iterations of web-based browsing applications and design literature,
particularly dynamic queries which are frequently represented as faceted
searching. Fagan (2010) noted that contemporary instantiations of faceted search
tools aimed to represent data types and provide tools for users to narrow
information search results. A typical example might start with a single search
term, and then facet by the type or resource, the date range, or even availability
of digitized copies (see Figure
2 for an example of faceted search). After examining numerous studies
that empirically investigated faceted searching, Fagan (2010) reported on a bevy
of benefits to faceted search, perhaps most notably that faceted search may
contribute to users finding a greater amount of relevant results as well as
creating mental navigation structures and specifically benefits user’s ability to
search within specific time-frames or by specific authors
Xiao et al. (2009) demonstrated that successfully navigating and interacting with
an online search tool, such as Yahoo.com, on a mobile device stresses the
importance of points of interest.
In
this way, at each point of faceting the user is presented with a different
dimensionality of the interface so that they may go from map, to list, to detailed
visual, to detailed text. Such an example of faceted searching that incorporates
multidimensional data, Kleinen et al. argue, may yield more enjoyable search
experiences.
Regardless of the device, it is apparent that online activities like web-browsing
and searching are unique performances that are configured with the apparatus that
user and site interface through (as well as how that interface is reconfigured
with the apparatus). These usability studies further reveal representations and
visualizations of dimensions of faceted searching may inhibit or encourage
alternate forms of exploration. In particular, an undercurrent of the studies
reported in a-priori
Our study is not the first within the digital humanities to draw upon the idea of
usability and to consider user experience in ways similar to the studies described
in the previous section. For example,
Since the DH ethos values both the evolution of traditional research practices
from humanities scholars as well as knowledge production in humanities classrooms,
it is reasonable that DH scholars seeking better understanding and improved
designs of digital media are concerned with a wider context than that of the
interface. Burkick & Willis (2011) have argued that 21st-century literacy practices are takes into account not only how an application is used but
also the kinds of subject positions, world-views, and models it
affords
more transitory than revolutionary
Warrick (2012) has additionally argued that studying users in context is key to
understanding how scholars and students of varying expertise use digital tools and
media as part of their broader scholarly practices. This encourages
While this surely requires an extended discussion on its own, it is worth noting
that the tension between a
In Shneiderman’s (1996) taxonomy of data types, network data accounts for
relationships between and among items in an online archive. The configuration of
these relationships, or
BigDIVA operationalizes faceted searching through presenting a model of the network
through one of four user-defined facets: resource, genre, discipline, and format. While resource is the primary representation
by default, users can choose to begin with one of the other three. This is
accomplished without extensive revision to the existing schema because the ARC
catalog is a fairly shallow example of faceted search, with its categories largely
existing as sister nodes under a central tree. The tool takes advantage of this
functionality to create ad-hoc trees based on the user’s defined needs and ARC
catalog schema.
Once a user has selected their initial facet, they are then presented with a group of
sub-nodes that consist of the various recognized categories in the schema. For
example, a user that selects Genre as their initial limitation
will be presented with sub-nodes with categories such as Law, Scripture, and Drama.
From there, they can select the desired category, whereupon they are presented with
the total number of items under that category in a sidebar and the ability to refine
further based on either the remaining categories or the individual records. This
refinement can be narrowed further by repeating the process with the newly-refined
category until all four aspects of the site have been selected upon.
Regardless of the level at which they are selected, the individual resources provide a truncated version of the catalog information and, most importantly, a link for the user to go to that resource, which will then appear in a new window. At the same time, the user can see the entire path between their initial category and the individual item, maintaining a visual representation of the mental model they were operating under while making that selection. Furthermore, they can select different nodes and combinations of categories to visualize multiple mental pathways at the same time, a functionality that is not possible in most faceted search systems. In all instances, the nodes are color-coded to help the user to understand at a glance what selections have been made, while unavailable choices are greyed out. This maintains both the full picture of the ARC catalog while simultaneously foregrounding those items that are of most import to the researcher. Finally, because the ARC catalog deals with historical objects, a timeline is provided at the bottom of the workspace, which allows a user who works in a particular period to view only those items of interest within that period.
Since BigDIVA therefore allows users to manipulate the data visualizations through multiple means simultaneously, i.e., the user-defined facets, a timeline, as well as more direct manipulation of the network nodes, it may afford a unique browsing experience. While simply overlaying multiple types of interactive data is not alone a cure-all, the combination of a networked graph of the entire catalogue, combined with the ability to expand the network graph through faceting based on category as well as time-line may serve as complementary tasks. However, the inclusion of a standard search bar as a way to filter out archival items from the network visual may serve as a source of confusion since one is a standard search feature while the other is not. In other words, a key aspect of understanding BigDIVA’s usability as well as the experiences of users will be the perceived relationship between these starkly different data types, in Shneiderman’s terms.
In order to take first steps to understand the potential uses and experiences that
BigDIVA’s networked browsing has to offer, we designed a usability study intended to
compare networked browsing with more traditional web browsing. In doing so, we hoped
to observe not only whether individuals could successfully use BigDIVA as a search
tool without any training, but also how BigDIVA’s networked browsing engaged
individuals in unique acts of online information searching. In short, we sought to
understand BigDIVA’s operational
This study was conducted entirely within a labspace designed specifically for
usability studies in the library of a Mid-Atlantic U.S.-based university. This lab
was outfitted with a desktop computer running Morae software
Participants were guided through the study tasks through a Google Forms file that provided instructions for, as well as logged performance of, each task. First, participants were asked to complete a brief pre-study questionnaire to self-report expertise with generic information search, as well as fields of expertise. Next, participants were guided through three search tasks that required using Google to find a famous work by William Blake, a relief etching by William Blake, and an etching of Blake’s
William Blake,an individual result from this search, meta-data from a specific result for Blake’s
chastity.It should be noted that all of the search tasks are listed in the Appendix, and that the BigDIVA search tasks were intentionally longer simply to observe users interacting with BigDIVA for longer. Following the search tasks, the first author engaged the participants in a brief follow-up interview to solicit self-reported experiences with BigDIVA.
Participants (N = 13) were recruited from networks
connected to a Mid-Atlantic university in order to engage participants that were
likely familiar with web-based archival search tools beyond Google. All
participants were currently enrolled in graduate coursework or had completed
graduate coursework, and were from a variety of fields and academic positions
including tenured professors, graduate teaching/research assistants, library &
information scientists, industry workers, and one individual currently between
jobs. A small group of participants (N = 5) piloted the
study design during the spring of 2015, which led to subsequent adjustments to the
study’s task design (i.e., particular phrasing of task instructions, and
streamlining of the Google Forms interface). The remaining participants (N = 8) engaged in the study between late 2015 and early
2016, and will make up the primary participant pool to be discussed throughout the
remainder of this paper.
Analysis was conducted using Morae Manager to code and extract data, and
visualizations were produced in R Studio
Participants spent a total of 30.51 minutes on the three Google tasks, and 144.78
minutes on the seven BigDIVA tasks (see Table 2 for
breakdown of TOT). When looking at TOT for each individual task, as shown in Figure 4 below, we can see that, while in many cases
the TOT for BigDIVA tasks varied within the same range as the Google tasks, the
BigDIVA tasks witnessed greater variability in terms of TOT. In particular, the
two BigDIVA tasks that required the most specific information (Task 2_3a and
Task2_4) generally took the longest. A paired samples t-test comparing participants’ mean TOT for Google and BigDIVA tasks
demonstrated that mean TOT for BigDIVA was significantly longer ( t (6) = -2.85, p = .029).
Since all participants self-assessed themselves as Very Good
or
Excellent
Google-users, it is certainly reasonable that they would need
to spend less time on the Google search tasks. Directly comparing aggregate TOT
for Google and BigDIVA tasks does not account for time necessary to adapt to an
unfamiliar search tool; therefore, understanding the value of aggregating TOT
across tasks and search platforms requires more nuance. For example, based on
comparison of TOT for BigDIVA tasks only, it becomes apparent that following the
first BigDIVA task (Task 2_1a), some participants appeared to become more
time-efficient (particularly for Task 2_2b and 2_3b), which suggests that they
overcame a learning curve and adjusted to aspects of networked browsing. Further,
some participants that exhibited similar difficulties with search tasks
(particularly Task 2_3a-b and 2_4) spent more time on each task. For example,
participant B06 and B08 were both unable to complete these three tasks for similar
reasons (as will be discussed below), but B06 spent much longer (5.66 minutes on
all three tasks) than B08 (3.74 minutes on all three tasks). This suggests that
successful or unsuccessful adaptation to BigDIVA is not necessarily dependent on
how much time a user spends on task.
In order to look deeper than TOT data, the AWT data is able to provide more
fine-grained comparisons of what participants did for each task with the time
allotted. Figure 5 provides a timeline for each
participant colored for any occurrence of an AWT action type. This provides a
window into the interplay and sequencing between multiple action types over the
course of the entire study, and shows that a typical action sequence for Google
tasks involved entering a search query, adjusting for search dimension (i.e.,
Image, News, Scholarly), opening a result (maybe two), and then eventually
entering an answer for the task. For BigDIVA, these sequences were more complex as
participants were required to explore and manipulate interface elements they were
previously unfamiliar with. Interestingly, at first glance of BigDIVA’s
force-directed network, all participants found the search bar and entered a query
in a matter of seconds. However, once a search query was entered, participants
variably played with the network nodes, switched dimensions (e.g., Genre or Format), enlarged the
screens, faceted, etc. In other words, the sequence from search query to facet to
search result was broken. Entering or modifying the search query makes up a much
smaller portion of AWT actions for BigDIVA tasks (with the exception of Tasks 2_3a
and 2_4), while experimenting and playing with the faceting features and the nodes
were much more frequent. The greater frequency of these action types suggests that
participants’ unfamiliarity with BigDIVA — specifically the aspects of networked
browsing — are likely indicative of a sense of fascination and playfulness, or
even confusion and frustration, with these features in order to understand their
function. The novelty of these features may have encouraged participants to spend
time playing with these features in order to better understand them, which may
explain how some participants spent less time for later tasks.
Log-File actions additionally present a complex picture. While Mouse and cursor actions dominate input actions during the Google tasks, the first four BigDIVA tasks witnessed a steady decrease in mouse action as participants engaged with the keyboard and various other AWT action types more frequently. While Tasks 2_3a-b appear to revert to Google-like behavior, the frequency of mouse and cursor actions glaringly decreases for the BigDIVA tasks, despite the fact that BigDIVA’s functionality is primarily dependent on the cursor (with the exception of the search bar).
Lastly, since not all participants were able to complete every search task, it is worth looking more closely at those participants and what their actions were. Furthermore, in order to have a comparison of these Non-Finisher participants to those that did, we made a subgroup of the two Non-Finishers (B06 and B08) and two Finishers (B01 and B04). This sub-group was determined by developing a rudimentary scoring system for each task in which participants received 1 point for completing the task as designed, 0.5 points for completing the task not as designed, and 0 points for not completing the task at all. After rating all participants, B06 and B08 scored the lowest, while B01 and B04 scored the highest.
In terms of AWT action types, Figure 8 demonstrates both groups overall were not strikingly different. Besides the action type QUIT, both the Finishers and Non-Finishers engaged in similar distribution of AWTs as the Finishers. The primary distinction is for the action type DIMENSIONSWITCH, as the Finishers switched dimensions in BigDIVA more than half as much as the Non-Finishers. After looking more closely at when and how these participants switched dimensions, it was observed that both groups had developed distinct understandings of this feature’s function: the Finishers would switch dimensions and then enter a search query, whereas the Non-Finishers would enter a search query and then switch dimensions. While a seemingly inconsequential distinction in Google, this sequence of action in BigDIVA is the difference between filtering results based on a search query and resetting the force-directed network visualization of the archive to another dimension. Thus, when the Non-Finishers entered a search query, and then switched dimensions, they effectively negated their query and reset the visualization to view all archive content — albeit in a different dimension. In other words, the Non-Finishers were very likely unable to finish all of the BigDIVA tasks because they had failed to adjust to this specific feature of BigDIVA’s functionality, and instead appeared to transfer their understanding of this functionality directly from Google.
Furthermore, when comparing TOT data for the Finishers and Non-Finishers, the Non-Finishers appear to take more time in general to adjust to BigDIVA search tasks. Indeed, by Task 2_3a, the Non-Finishers demonstrated so much difficulty in using BigDIVA in order to complete each search task that they appeared more inclined to abandon the tasks rather than adjust their understanding of the functionality; whereas the Finishers — who had done a better job of understanding the functionality — were willing to spend more time in order to complete the tasks.
While audio-video data captured from the webcam does coordinate on-screen actions
with affective reaction as gauged by facial expression, it is beyond the scope of
this current study to adequately or appropriately surmise how using BigDIVA
resulted in participants’ affective reactions. After all, since BigDIVA was new to
the participants while Google was used daily, some manner of novelty effect would
be expected. Further, since BigDIVA did require some manner of learning curve (and
at least two participants experienced delays due to server interruptions), such
analysis would likely have been biased towards affects such as frustration or
confusion What,
and B08’s brow furrowed. In other
words, the novelty of networked browsing appeared to induce affectations — perhaps
even joyous
in some cases — that in and of itself may
offer participants hope of a new manner of searching.
Indeed, based on the self-report questionnaire following the search tasks,
participants were largely in agreement about the potentialities of networked
browsing. While many elaborated on early frustrations with the new functionalities
of the search tools, or the initial learning curve, all participants remarked
about BigDIVA’s ability to establish an understanding of the relationship between
the collections and their individual archive sources. In particular, three common
themes were apparent from the participants’ self-reports: that the novelty of
BigDIVA’s networked browsing was either fun
or interesting,
that BigDIVA could be most useful for
specialized archives or use in scholarly settings, and that the networked
visualizations may be pedagogically valuable for demonstrating relationships
between scholarly sources (see Table 3 below).
Another unique theme expressed by participants related to the nature of web-based search queries altogether. In particular, while participants noted that after a brief adjustment period they were able to successfully search and find specific information using BigDIVA, that they would still use a more typical visualization such as Google — even if networked browsing were an option with Google. Participants justified this preference with the fact that a text-based list of results still might be simpler and quicker if the user already knows what they are looking for; whereas networked browsing would be preferable if the user was not certain what they were looking for. In other words, networked browsing would be more suitable for exploring the contents of an archive without a predisposition for specific results.
This study set out to explore how networked browsing allows subjects to explore and
interpret relations between archived resources. Our guiding research questions were:
how do participants use BigDIVA in comparison to a more
traditional browser interface such as Google, and how does
BigDIVA’s interface afford and evidence generous interfacing or archival
liveness? While our small and preliminary usability study certainly does not
exhaustively answer either of these questions, we are left with valuable evidence
that point towards the potential range of experiences and materialities that
networked browsing has to offer.
In response to our first research question, the BigDIVA usability study provided evidence of how the process of searching with BigDIVA compares to a more traditional search tool like Google. According to our analysis of users’ input-related actions, we observed that despite the fact that BigDIVA employs traditional faceted search functionality (albeit with networked visualization) participants experienced browsing differently for Google and BigDIVA. Participants took significantly longer to complete BigDIVA tasks, in some cases were unable to complete certain tasks, and in some cases attempted to transfer functionalities from Google. Additionally, participants generally used the search bar less, the mouse and cursor less, and the faceting features more when completing BigDIVA tasks compared to Google tasks. It was therefore apparent that, perhaps because of the comparative study design, when participants shifted from using Google to BigDIVA that they experienced a performative disruption. The spike in mouse clicks for the first BigDIVA task, which subsequently leveled out for participants, exemplified the learning curve or adaptation to the distinctive performances engendered by networked browsing.
Analysis of input actions or completion of search tasks on their own, while key
components in understanding the cybernetic assemblage, do not tell the whole story of
how — in Barad’s and Drucker’s terms — a human intra-acting with the distributed
machinic components is made discursively meaningful. Self-reports of users’
experiences and perceptions regarding BigDIVA therefore revealed the most surprising
observations and indications of what it is like to perform networked browsing.
Participants described their initial impressions of networked browsing as fun,
interesting,
or different
—
regardless of their success or failure in the search tasks. They additionally
reflected on the potential scholarly and pedagogical applications, and yet, most
surprising, participants remained skeptical of networked browsing’s usefulness for
everyday web browsing (i.e., in place of typical search interfaces like Google);
however, numerous participants suggested it would be more fitting functionality for
finite archives (i.e., such as the ARC catalog).
That participants generally reported such positive experiences and perceptions of
BigDIVA, in spite of its more specialized use, is a promising indication that
networked browsing may indeed engender Shneiderman’s (1996) joyous experience
for interfacing with the web. Indeed, such experiences
are difficult to observe by examining input-related actions alone and without facial
recognition software to analyze affect. In future study, we therefore intend to place
greater emphasis on
Of course, as implied already, our study was not without its limitations. In
addition to a small sample size, we wish to emphasize that the comparative design
of our study is one such limitation, because, as participants self-reported,
BigDIVA prioritized a different kind of browsing than Google. Finding information
in BigDIVA did not require a scavenger hunt amongst a hierarchical text-based list
that required prior knowledge encoded in the form of a query. Instead, BigDIVA
made information available without a search query, thus begging participants to
visually untangle and play with the networked catalog. Our study design admittedly
prioritized specific search tasks in order to target use of specific features as
well as to allow easy comparison to search tasks in Google. Open-ended tasks or
longitudinal observation, per
At the same time, comparative methods are still valuable for design and experiential considerations of these interfaces. After all, if networked browsing indeed affords unique performative understandings and meaning-making experiences compared to other operationalizations of faceted search functions, then more detailed comparative studies could reveal the distinctions in those processes, particularly comparing similar scholarly faceted search tools such as a university library database. As suggested above, such a comparative study would do well to not solely rely on input-based behavioral measures that privilege the machine components of the interface, and instead privilege the human components in the interface by observing affect, metacognitive processes, as well as self-report measures to emphasize users’ perceptions of their experiences. In particular, such methods could potentially parse out the distinction between users’ understanding of search functionality from archive architecture, as well as how, in their own terms, they may find joy in their browsing experiences.
Based on the observations from our study, networked browsing may indeed be
suggestive of Whitelaw’s (2015) generous interfacing
that emphasizes browsing as a tool for exploring relationships between nodes in
archives. The possibilities for exploration and play are evidenced not only in how
archives may be accessed and searched for specific scholarly aims, but also for
configuring new relational understandings of the distributed materiality of an
archive itself. In other words, networked browsing may help users conceptualize
archives as agential or living material bodies. In doing so, searching through an
archive is no longer a linear act of retrieval, but perhaps an exploratory and
contingent act of meaning-making.
Of course, our study’s observations and what they suggest only yield more questions
about networked browsing, faceted search functions, and generous interfacing. As
mentioned above, further research design would do well to observe how individuals
might use BigDIVA over a longer span of time, involve more open-ended tasks, and
involve multidimensional analysis. In particular, future research should question
what could users learn or discover with BigDIVA when simply given time and freedom to
experiment with networked browsing and explore the archive? Would they come to a more
detailed understanding of its components (i.e., collections, individual resources,
faceting structures)? Would they become more fluent with the interface’s
functionality? Such questions, which we see as central to the study of
knowledge-production in the digital humanities, would require attention to