HCI is a discipline full of differences. Different researcher backgrounds, orientations, methods and
ideas of what technology should look like. We conducted a diffractive reading,
a concept introduced by physicist feminist Karen Barad, as a way to understand how
differences materialize and matter within HCI. Our approach involved three separate analyses of
data from a single design workshop, followed by a diffractive reading of that set of analysis.
This image shows participants broken up into smaller groups at five different tables which they
stayed in for the duration of the workshop, along with undergraduate members of the research team
who supported them in activities and took notes. The two hour and 45 minute workshops involved a
series of presentations and group work. Group work involved engaging with internet of things
technology toolkit cards, prototyping with an electronic toolkit, and then engaging
in a prototyping activity with design materials. A different researcher took the primary lead for
each analysis. Researchers worked with the methods or orientation that best reflected
their typical approach to research. The first analysis was conducted by
Katie Siek. She did an analysis in accordance with the norms of the sub-community in HCI that studies
Internet of Things technologies for the home. Two themes emerged from her analysis - first,
that there was an interest in supporting awareness of and automating home tasks, and second,
that participants were really interested in connecting remotely using IOT technologies.
Across these two themes, we find that the progression of activities,
starting with a broad overview of IOT, and then interacting directly with electronics led
participants to refine their understanding of IOT. I'm giving a sort of microcosm of the paper here,
just going through one theme to build an example of the diffractive analysis. We saw
how participants' ideas increased in complexity over the course of the workshop. By the end of
the session, their ideas expanded to macro level contextually aware systems requiring information
from multiple sensors throughout a space. As one example, participants at table B
commiserated over the burden of putting up holiday lights each year only to remove them
shortly after and they prototyped a fairly complex system that could automate some of this work.
Now analysis two, conducted by Ben Jelen. Here, the focus is on the target group,
older adults, and the researcher analyzed this data in accordance with the norms
of the sub-community in HCI that studies aging in place and technologies for aging.
The themes that emerged here were that older adults were really interested
in IOT for reminders and IOT for safety. Seasonal decorating emerged as a risky task
that spurred designs at several tables. One participant revealed that they had left their
holiday lights up for three consecutive years, and another was interested in this idea because
his grandson who usually helps him decorate isn't coming home this year for Christmas. The
final design was a bookcase that housed a fully decorated Christmas tree on a Roomba style robot
that rolled outside. Also based on his experience with seasonal decorations, another participant
was motivated to create a design similar to a Life Alert button to notify loved ones if his wife fell
and hurt herself in their home. His motivation for this design was because of his past experiencing
falling over 7.5 meters, or 25 feet, while hanging Christmas lights.
Together, these anecdotes shared by participants and the resulting systems that they designed
indicate an opportunity for researchers to look into automating or assisting seemingly
mundane household tasks which may actually be dangerous or difficult for older adults.
Analysis 3 takes a critical, process oriented approach to analyze the dynamics of older adults'
engagement in IOT workshops. This analysis was led by myself and Alisha Pradhan. As
the analysis process progressed, we began to focus on the dynamics of participation,
the ways that participants responded to prompts and directives, and we ended up finding mismatches
between researcher and attendee expectations. Further, participants subverted what it meant
to be a good participant during these design workshops in two ways. By going through the
motions and by having a good time. Participants resisted the role of being a good participant by
doing the bare minimum needed for an activity, and they found shortcuts to reduce their efforts. For
example, in a low fidelity prototyping activity, one participant managed to get an undergraduate
student to do the work of cutting a bush for a Christmas tree light system by asking them, "if
you were me, how would you make a green bush?". As the undergraduate student responded by cutting
the bush out, the participant said, "You could do it for me, you know. You are doing a good job".
In this mode of participation, the measure of when an activity was done was whether
an individual had done enough to satisfy researchers, not whether they put together
a design that reflected their actual preferences. The next stage of our approach was to engage in a
diffractive reading of the three sets of analyses. Diffractive approaches study difference and we did
so by iteratively revisiting our three analyses to understand where distinctions and boundaries
existed. We created memos while reading findings through one another. In some cases
we had insights about the meaning of differences when we examined data that was included in one
analysis and excluded another in another. In other cases, was when our analysis centered
around ways that the same data was brought into different analyses but took on different meanings.
Here, I'm going to revisit the differences between the ways that holiday decorations appear in our
data. In analysis one, a holiday decorating robot was evidence that participants deepened
their thinking about IOT from micro to macro thinking because of our workshop activities.
In analysis 2, holiday decorations meant something different. They signal a need
for care- the poignancy of a house with lights left up for three years in a row, a grandson
neglecting their annual holiday visit, and the risk of a 25-foot fall for an elderly individual.
In analysis three, the significance of the holiday decorations is in the way
that participants found a way to put minimal effort into the activity by convincing a young
research assistant to do the work for them. If we don't take a diffractive reading approach
we can find new insights which fit within existing ways of doing research in different
sub-disciplines within HCI. Analysis 1, improving the ways in which we conduct user-centered design,
analysis 2, identifying an important area for older adults well-being- these mundane activities
that create risk that are rarely discussed in the body of aging HCI literature. And analysis
3 helps us further understand participation in design research and question our assumption
that participants are always revealing their genuine thoughts in user-centered design.
Reading one analysis through another however, as we did through our diffractive reading
can help make what is at stake in different interpretations clear. When insights related
to holiday decorations from analysis 2 are read through findings from analysis 3 which raise the
prospect of older adult resistance, we might pause at casting mundane daily activities as
an additional criteria to monitor and view through the lens of aging in place. The point here is not
that reading insights through one another as we did here should prevent researchers from studying,
for example, the topic of mundane activities for aging- but that this approach can raise
some practical or ethical considerations that may impact a research agenda down the line,
and are difficult to realize from a single analysis or even triangulating methods.
If analysis 3 was the only analysis reported from our data, our takeaway might be that
none of the ideas that participants shared reflected actual needs. But this was not the case.
One participant's desire to check on his wife if she became injured is one memorable example. So
we argue that probing the places in different analyses that do not sit easily together can
help our research stay closer to what is truthful to and can better serve the populations we study.