Older Adults & Their Caregivers: Technology Partnerships
The North American baby boomer and generation x populations are aging, while at the same time, more critical services are moving online. We are increasingly dependent on online systems to manage our health, our retirement funds, our pensions and other governmental benefits, as well as for telecommunications services and social networking. Simultaneously, older adults face declining health, cognitive decline and other issues that impede their ability to engage with online technologies and systems. Older adults rely on friends and family members acting as caregivers, and these caregivers can help with online systems. How these caregivers access online systems on behalf of aging adults is an unexplored area. My investigations into this so far have been restricted to the health care domain, where I have documented serious issues relating to security and privacy when caregivers access the patient portals of older adult patients.
Inclusive Computer Science Education
The figure above shows the pre-College programming exposure of students in a sophomore CS2 class at UNC Charlotte.
The field of computer science suffered a huge loss in diversity between the 1980s and the early 2000s. There used to be many more women majoring and working in computer science and software engineering careers. And while there were never a huge number of racial minorities working in the field to begin with there should be many more people of color helping to develop the technology systems that enrich (and sometimes control) our lives. The K-12 environments have work to do in exposing young girls and people of color to computing education in an inclusive way. The media has a responsibility to represent the field in a way that appeals to women and people of color, rather than propagating the lonely white/asian male geek stereotype of a computer scientist. And in higher education institutions, we have a responsibility to teach computer science in inclusive and welcoming classrooms with content and application contexts that are relevant to a diverse population. I have conducted research into leveraging social and team-based learning, media computation approaches, flipped classes and active learning as a way to engage and welcome a wider variety of people to the computing field.
Currently, I am investigating how multi-modal forms of engagement can help diverse students succeed in computing. With my PhD student Syeda Mazumder, we are investigating visual representations of programming concepts in textbooks, in instructor presentations and in online videos.
Exploration & Satisficing in Complex Software
The images above are two variations on interfaces for image editing – a prototypically complex creativity software. The version on the left represents ‘creamy’ sliders – near continuous options for each parameter, with a combinatorial space of all possible settings that is enormous and impossible to explore. The version on the right represents ‘crunchy’ sliders, with only five possible settings per parameter, creating a still very large but less daunting space of combinatorial possibilities. The crunchy slider interface is designed to help users explore more different and diverse possible combinations of parameter settings and avoid design fixation. The standard ‘creamy’ sliders are better used when fine-tuning. The difference between these two interfaces, which were developed by my former PhD student, Berto Gonzalez, amplify and highlight the issues associated with exploring parameter spaces in complex software.
This research thread involves ideas from the social sciences: the concept of ‘satisficing behaviour’, when a user decides that even though they have looked at all possible options or explored all possible avenues, they have done sufficient exploration and are satisfied with their results. Our goal in this research is to study and measure how much exploration is typically done before a novice or expert user satisfices, and how various interface designs can help users to explore a wider set of possibilities more easily and efficiently before satisficing.
Exploring research ideas and topics is a similar problem space. There are many aspects of any given research topic and all of the researchers working in that general area are investigating some version of the topic which can be thought of as a vector threading through a multi-dimensional space. Thinking about a research topic in this way allows a researcher to categorize existing work and potential ideas and more easily identify gaps in the space. My former PhD student, Stephen MacNeil, developed the Design Space Explorer (DSE) as a tool to help plot and define research spaces. The figure above is a screenshot from the Design Space Explorer, showing a doctoral student’s placement of the most relevant research in machine learning for a specific domain, categorized across a number of dimensions.
Reflection Support Tools
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Dance.Draw
The Dance.Draw project was a 3-year, NSF-funded project that looked at the intersection of dance and interactive technology. We had 3 main research goals: tracking the motions of dancers for the purposes of creating real-time interactive media that can be part of the dance production, researching methods for enhancing audience engagement and interaction, and developing novel IT support for the collaborative performing arts process.
The figure above shows the Video Collaboratory (formerly known as the Choreographer’s Notebook), which is now a commercial product. This web-based software is designed to support small group collaboration around video documents, such as dance rehearsal videos. This patented software is a direct product of the Dance.Draw research project, and was initially developed by my former PhD student, Vikash Singh.
Creativity Support Tools & Evaluation
My former PhD student, Erin Cherry (nee Carroll), investigated how to evaluate digital creativity support tools. We developed the Creativity Support Index, which is a self-report metric modeled after the NASA TLX, but tailored to evaluating how well tools support people in open-ended, creative work.
In addition, we have been investigating how the physiological state of the body changes both in response to being presented with creative work, and during the process of doing creative work. This led to research investigating the use of machine learning to detect states of ‘In-The-Moment-Creativity,’ periods of highly creative experience during the creative work process.
Older Research Projects
Computing Creatively 