Technology adoption among older adults has increased significantly in recent years. Yet, as new technologies proliferate and the demographics of aging shift, continued attention to older adults’ adoption priorities and learning preferences is required. Through semi-structured interviews, we examine the factors adults 65+ prioritize in choosing new technologies, the challenges they encounter in learning to use them, and the human and material resources they employ to support these efforts. Using a video prototype as a design probe, we present scenarios to explore older adults’ perceptions of adoption and learning new technologies within the lens of health management support, a relevant and beneficial context for older adults. Our results reveal that participants appreciated self-paced learning, remote support, and flexible learning methods, and were less reliant on instruction manuals than in the past. This work provides insight into older adults’ evolving challenges, learning needs, and design opportunities for next generation learning support.

The availability of voice-user interfaces (VUIs) has grown dramatically in recent years. As more capable systems invite higher expectations, the conversational interactions that VUIs support introduce ambiguity in accountability: a user's or system's obligation or willingness to be responsible for the outcome of user-delegated tasks. When misconstrued, impact ranges from inconvenience to deadly harm. This project explores how users' accountability perceptions and expectations can be managed in voice interaction with smart home appliances. To explore links between degree of automation, system accountability and user satisfaction, we identified key design factors for VUI design through an exploratory study, articulated them in video prototypes of four new VUI mechanisms showing a user commanding an advanced appliance and encountering a problem, and deployed them in a second study. We found that participants were more satisfied with automated systems, but also saw them as more accountable. Results suggest that this perceived system accountability was reduced in mechanisms that justified their recommendations. Our findings motivate the development of transitional mechanisms which gradually provide less guidance as users learn to operate the system.

A rapid proliferation of specialized consumer tech gadgets (e.g., Fitbits) demands attention to the challenges that individuals face in learning to use them. Help Kiosk (HK) was initially conceived to support older adults in independently learning to use a smartphone, but it was only ever examined for this one context. Its design also required users to divide their attention between different areas of an augmented display and their smartphone. We introduce HK 2.0, which leverages a 40" tabletop display to integrate support into one visual space, while also supporting older adults in learning how to use multiple smart devices that must be configured to work together (e.g., a smartwatch synched to a tablet). Our new system also builds on the self-directed learning environment of the initial HK by introducing a collaborative learning feature to provide remote support by family and friends. We share the design of HK 2.0, including design requirements, features, and learning topics.

Older adults have difficulty using and learning to use smart phones, in part because the displays are too small to provide effective interactive help. Our work explores the use of a large display to temporarily augment the small phone display to support older adults during learning episodes. We designed and implemented a learning system called Help Kiosk which contains unique features to scaffold the smart phone learning process for older adults. We conducted a mixed-methods user study with 16 older adults (55+) to understand the impact of this unique design approach, comparing it with the smart phone's official printed instruction manual. We found Help Kiosk gave participants more confidence that they were doing the tasks correctly, and helped minimize the need to switch their attention between the instructions and their phone.

Maintaining an awareness of collaborators' actions is critical during collaborative work, including during collaborative visualization activities. Particularly when collaborators are located at a distance, it is important to know what everyone is working on in order to avoid duplication of effort, share relevant results in a timely manner and build upon each other's results. Can a person's brushing actions provide an indication of their queries and interests in a data set? Can these actions be revealed to a collaborator without substantially disrupting their own independent work? We designed a study to answer these questions in the context of distributed collaborative visualization of tabular data. Participants in our study worked independently to answer questions about a tabular data set, while simultaneously viewing brushing actions of a fictitious collaborator, shown directly within a shared workspace. We compared three methods of presenting the collaborator's actions: brushing & linking (i.e. highlighting exactly what the collaborator would see), selection (i.e. showing only a selected item), and persistent selection (i.e. showing only selected items but having them persist for some time). Our results demonstrated that persistent selection enabled some awareness of the collaborator's activities while causing minimal interference with independent work. Other techniques were less effective at providing awareness, and brushing & linking caused substantial interference. These findings suggest promise for the idea of exploiting natural brushing actions to provide awareness in collaborative work.

Scatterplot matrices (SPLOMs), parallel coordinates, and glyphs can all be used to visualize the multiple continuous variables (i.e., dependent variables or measures) in multidimensional multivariate data. However, these techniques are not well suited to visualizing many categorical variables (i.e., independent variables or dimensions). To visualize multiple categorical variables, 'hierarchical axes' that 'stack dimensions' have been used in systems like Polaris and Tableau. However, this approach does not scale well beyond a small number of categorical variables. Emerson et al. [8] extend the matrix paradigm of the SPLOM to simultaneously visualize several categorical and continuous variables, displaying many kinds of charts in the matrix depending on the kinds of variables involved. We propose a variant of their technique, called the Generalized Plot Matrix (GPLOM). The GPLOM restricts Emerson et al.'s technique to only three kinds of charts (scatterplots for pairs of continuous variables, heatmaps for pairs of categorical variables, and barcharts for pairings of categorical and continuous variable), in an effort to make it easier to understand. At the same time, the GPLOM extends Emerson et al.'s work by demonstrating interactive techniques suited to the matrix of charts. We discuss the visual design and interactive features of our GPLOM prototype, including a textual search feature allowing users to quickly locate values or variables by name. We also present a user study that compared performance with Tableau and our GPLOM prototype, that found that GPLOM is significantly faster in certain cases, and not significantly slower in other cases.

For a very large number of adults, tasks such as reading. understanding, and using everyday items are a challenge. Although many community-based organizations offer resources and support for adults with limited literacy skills. current programs have difficulty reaching and retaining those that would benefit most. In this paper we present the findings of an exploratory study aimed at investigating how a technological solution that addresses these challenges is received and adopted by adult learners. For this, we have developed a mobile application to support literacy programs and to assist low-literacy adults in today's information-centric society. ALEX© (Adult Literacy support application for Experiential learning) is a mobile language assistant that is designed to be used both in the classroom and in daily life in order to help low-literacy adults become increasingly literate and independent. Through a long-term study with adult learners we show that such a solution complements literacy programs by increasing users' motivation and interest in learning, and raising their confidence levels both in their education pursuits and in facing the challenges of their daily lives.

Mobile devices offer much potential to support older adults (age 65+). However, older adults have been relatively slow to adopt mobile devices. Although much ongoing HCI research has examined usability problems to address this issue, little work has looked at whether existing graphical icons are harder to use for this population compared with younger adults. We conducted a qualitative exploratory study and a follow-up experimental study to determine which icon characteristics help initial icon usability for older adults. We found that our older participants did have more problems using existing mobile device icons, but that particular icon characteristics – semantically close meaning (i.e. natural, close link between depicted objects and associated function), familiar, labelled and concrete (i.e. those depicting real-world objects) – improved icon usability for them. We discuss how these findings can help icon designers to create mobile device icons that are more suited to the abilities and technology experience of older adults.

Basic literacy skills are fundamental building blocks of education, yet for a very large number of adults tasks such as understanding and using everyday items is a challenge. While research, industry, and policy-making is looking at improving access to textual information for low-literacy adults, the literacy-based demands of today's society are continually increasing. Although many community-based organizations offer resources and support to adults with limited literacy skills, current programs have difficulties reaching and retaining those that would benefit most from them. To address these challenges, the National Research Council of Canada is proposing a technological solution to support literacy programs and to assist low-literacy adults in today's information-centric society: ALEX© - Adult Literacy support application for EXperiential learning. ALEX© has been created together with low-literacy adults, following guidelines for inclusive design of mobile assistive tools. It is a mobile language assistant that is designed to be used both in the classroom and in daily life, in order to help low-literacy adults become increasingly literate and independent.

In this paper, we reflect on the design and deployment process of MAGICBoard, a public display deployed in a university setting that solicits the electronic votes and opinions of bystanders on trivial but amusing topics. We focus on the consequences of our design choices with respect to encouraging bystanders to interact with the public display. Bystanders are individuals around the large display who may never fully engage with the application itself, but are potential contributors to the system. Drawing on our recent experiences with MAGICBoard, we present a classification of bystanders, and then discuss three design themes relevant to the design of systems for bystander use: graduated proximal engagement, lowering barriers for interaction and supporting covert engagement.

Adding expressive haptic feedback to mobile devices has great potential to improve their usability, particularly in multitasking situations where one's visual attention is required. Piezoelectric actuators are emerging as one suitable technology for rendering expressive haptic feedback on mobile devices. We describe the design of redundant piezoelectric haptic augmentations of touchscreen GUI buttons, progress bars, and scroll bars, and their evaluation under varying cognitive load. Our haptically augmented progress bars and scroll bars led to significantly faster task completion, and favourable subjective reactions. We further discuss resulting insights into designing useful haptic feedback for touchscreens and highlight challenges, including means of enhancing usability, types of interactions where value is maximized, difficulty in disambiguating background from foreground signals, tradeoffs in haptic strength vs. resolution, and subtleties in evaluating these types of interactions.