Relevant Degree Programs
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G+PS regularly provides virtual sessions that focus on admission requirements and procedures and tips how to improve your application.
Graduate Student Supervision
Doctoral Student Supervision (Jan 2008 - Nov 2019)
Interactive technologies have become prevalent in all aspects of life including managing our tasks, looking for information, and connecting with others. We often adapt our behaviors, consciously or unconsciously, to accommodate the technology. The unique nature of our needs and preferences, and how they change over time are better supported with technologies that are designed to be personalizable. Lack of personalization facilities limits our range of behaviors. In this dissertation, we focus on understanding and supporting differences in individuals’ behaviors through forms of personalization that are beyond choosing among a set of predefined options, by allowing users to author new functionalities. We refer to such personalizations as “advanced personalizations.” Authoring advanced personalizations, when supported, is often time-consuming and requires programming skills. Consequently, either because of lack of ability or time, many users take advantage of personalizations created and shared by others. The overarching goal of this dissertation is to design for authoring and sharing of advanced personalizations. We explore this goal in the domain of personal task management (PTM), where rich individual differences deeply influence user behavior and tool use. First, to gain insights into individual differences in PTM as well as changes in PTM behaviors over time, we conducted a series of studies: a focus group and contextual interviews in an academic setting, a large survey questionnaire with a broader population, and follow-up interviews with some of the survey respondents. These studies provide insights into different types of advanced perosnalizations that a PTM tool needs to support. Next, we designed a personalizable PTM tool with two key components for authoring advanced personalizations, building on ideas from end user programming approaches, and following theoretical guidelines on designing personalizable tools such as meta-design guidelines. A controlled user study of our design revealed opportunities and challenges in supporting advanced personalization, and our detailed design process provides a practical starting point for designing personalizable tools. Finally, through studying personalization sharing practices, we characterized the multi-faceted nature of online personalization sharing ecosystems, which include multiple components for hosting personalizations, discussing, and managing them. Our findings also highlight tradeoffs and design considerations in such ecosystems.
Why do people visualize data? People visualize data either to consume or produce information relevant to a domain-specific problem or interest. Visualization design and evaluation involves a mapping between domain problems or interests and appropriate visual encoding and interaction design choices. This mapping translates a domain-specific situation into abstract visualization tasks, which allows for succinct descriptions of tasks and task sequences in terms of why data is visualized, what dependencies a task might have in terms of input and output, and how the task is supported in terms of visual encoding and interaction design choices. Describing tasks in this way facilitates the comparison and cross-pollination of visualization design choices across application domains; the mapping also applies in reverse, whenever visualization researchers aim to contextualize novel visualization techniques. In this dissertation, we present multiple instances of visualization task abstraction, each integrating our proposed typology of abstract visualization tasks. We apply this typology as an analysis tool in an interview study of individuals who visualize dimensionally reduced data in different application domains, in a post-deployment field study evaluation of a visual analysis tool in the domain of investigative journalism, and in a visualization design study in the domain of energy management. In the interview study, we draw upon and demonstrate the descriptive power of our typology to classify five task sequences relating to visualizing dimensionally reduced data. This classification is intended to inform the design of new tools and techniques for visualizing this form of data. In the field study, we draw upon and demonstrate the descriptive and evaluative power of our typology to evaluate Overview, a visualization tool for investigating large text document collections. After analyzing its adoption by investigative journalists, we characterize two abstract tasks relating to document mining and present seven lessons relating to the design of visualization tools for document data. In the design study, we demonstrate the descriptive, evaluative, and generative power of our typology and identify matches and mismatches between visualization design choices and three abstract tasks relating to time series data. Finally, we reflect upon the impact of our task typology.
Mobile computing devices, such as smart phones, offer benefits that may be especially valuable to older adults (age 65+). However older adults have been shown to have difficulty learning to use these devices, which is a barrier for technology adoption. The main goal of the research reported in this dissertation was to investigate three promising design approaches – increasing the interpretability of graphical icons, incorporating a multi-layered interface, and augmenting the mobile device’s display – to determine whether each can improve the learnability of mobile devices for older adults. We involved both older and younger adults in our studies to uncover benefits unique to older adults. In our investigation of graphical icons, we conducted an experiment to determine which icon characteristics affect initial icon interpretability for older adults. We found that icon interpretability can be improved for older adults by reducing the semantic distance between the objects depicted in the icon and the icon’s function, and by labelling icons. In our investigation of multi-layered interfaces, we prototyped a two-layer smart phone address book and conducted an experiment to assess its learnability over four learning phases. We found that the multi-layered interface, compared to a non-layered full-functionality control interface, provided greater benefits for older participants than for younger participants in terms of faster task completion times during initial learning, lower perceived interface complexity, and greater interface preference for learning. In our investigation of augmenting a mobile device display with a larger display to help older adults learn new devices, we conducted a comprehensive survey of older adults’ learning needs and preferences. Based on the survey findings, we designed and prototyped Help Kiosk, an augmented display system for helping older adults to learn to use a smart phone. An informal evaluation found preliminary evidence that Help Kiosk may be able to assist older adults in performing new mobile phone tasks. Through these three investigations, our research identified and validated design approaches that researchers and developers can use to improve the learnability of mobile devices for older adults, which should increase the chances of technology adoption.
Technology is increasingly being promoted as a means of addressing age-related cognitive and sensory impairments and enabling seniors to live more independently. Pen-based devices such as Personal Digital Assistants and Tablet PCs are appealing platforms for these endeavors because they are small, mobile, and powerful. Relative to the mouse, pen-based devices have been shown to be particularly beneficial for older adults. However, in terms of garnering wide-spread adoption, the mouse has historically dominated, leading researchers to focus chiefly on identifying and addressing its age- and motor-related limitations. In contrast, pen-based limitations for older users have been relatively unexplored. This thesis begins to fill that gap in the literature. Our first experiment, an empirical evaluation of pen-based target acquisition across the adult lifespan, identified three main sources of pen-based target acquisition difficulty—missing-just-below, slipping, and drifting—and demonstrated how these difficulties vary across task situation and age. In addition, this work showed that including older adults as participants can help uncover general pen-interaction problems: the missing-just-below and drifting difficulties were evident in both younger and older users alike. We next developed seven new target acquisition techniques to improve pen-based interaction, specifically addressing the three difficulties identified, and particularly targeting older adults. Our techniques built upon existing mouse-based techniques developed for older users and pen techniques for younger users. In total, we conducted three experiments to evaluate the seven new pen-based techniques: Reassigned and Deactivated (for missing-just-below), Tap and Glide (for drifting), and Steady, Bubble, and Steadied-Bubble (for slipping). Through these evaluations, we established where our proposed designs were successful at reducing errors, and where further refinement is needed. Finally, we reflected on our findings across studies to identify age-related, contextual, and technological factors which contributed to our results. These factors help illuminate the underlying reasons for pen-based targeting difficulties and shed light onto areas still needing attention. Overall, the results of this research support our main thesis that the accessibility of pen-based interfaces can be improved for older adults by first examining the sources of age-related acquisition difficulty, and then using the results of this examination to develop improved techniques.
Personalized graphical user interfaces have the potential to reduce visualcomplexity and improve efficiency by modifying the interface to better suit anindividual user's needs. Working in a personalized interface can make usersfaster, more accurate and more satisfied; in practice, however, personalizationalso comes with costs, such as a reliance on user effort to control thepersonalization, or the introduction of spatial instability when interface itemsare reorganized automatically. We conducted a series of studies to examine boththe costs and benefits of personalization, and to identify techniques andcontexts that would be the most likely to provide an overall benefit.We first interviewed long-term users of a software application that providesadaptable (user-controlled) personalization. A design trade-off that emerged isthat while personalization can increase the accessibility of features useful to auser's current task, it may in turn negatively impact the user's awareness of thefull set of available features. To assess this potential trade-off, we introducedawareness as an evaluation metric to be used alongside more standard performancemeasures and we ran a series of three studies to understand how awareness relatesto core task performance. These studies used two different measures to assessawareness, showing that personalization can impact both the recognition rate ofunused features in the interface and user performance on new tasks requiringthose features. We investigated both adaptive (system-controlled) and adaptablepersonalization techniques to help us understand the generalizability of theawareness concept.In addition to introducing and incorporating awareness into our evaluations, westudied how specific contextual and design characteristics impact the user'sexperience with adaptive interfaces. In one study, we evaluated the impact ofscreen size on performance and user satisfaction with adaptive split menus.Results showed that the performance and satisfaction benefits of spatiallyreorganizing items in the interface are more likely to outweigh the costs whenscreen size is small. We also introduced a new adaptive personalization techniquethat maintains spatial stability, called ephemeral adaptation, and evaluated itthrough two studies. Ephemeral adaptation improves performance over bothanother closely related adaptive technique and a traditional interface.
Master's Student Supervision (2010 - 2018)
In this thesis, we describe our design of Feature Recommender, a Mozilla Firefox browser extension, which proactively recommends features that it predicts will benefit users based on their individual usage behaviors. The goal of these pop-up notifications is to help users discover new features. How to maximize the effectiveness of such notifications while minimizing user interruptions remains a difficult open problem. One approach is to carefully time when the notifications are delivered. In our deployment of Feature Recommender, we study the effect of two delivery timing parameters: delivery rate and the user's context at the moment of delivery. We also investigate the effect of prediction algorithm sensitivity. We conducted three field studies, each about 4 weeks: (1) A preliminary study (N=10) to determine reasonable interruptible-moments; (2) A qualitative study (N=20) to assess the design and effectiveness of our extension; and (3) A near-identical study (N= ~3K) to assess quantitatively the effect of the timing parameters. Across all conditions Feature Recommender helped users adopt on average 18% of the features they were recommended, and as many as 24% when they were delivered at random times with a 1-per-day delivery rate limit. We show that lack of trust in recommendations is a key factor in hindering their effectiveness.
Touchscreens have become a mainstream input device for older adults. We compared performance of touchscreen and mouse input for older adults on both abstract and real-world pointing and dragging tasks: classic Fitts’s law tasks and tasks drawn from C-TOC, a computerized cognitive test being designed for older adults. The abstract and real-world tasks were designed to require equivalent motor skills. Sixteen older adult participants completed both types of tasks using a touchscreen and a mouse. The touchscreen was faster for both task types but somewhat more error-prone. However, the speed advantage of touchscreens for abstract tasks did not translate evenly to the corresponding real-world tasks. A KLM was used to explain the different speed gains in real-world tasks by incorporating both physical and cognitive components. As a self-administered test, C-TOC, would benefit from richer performance measures, beyond speed and accuracy, to compensate for the lack of a clinician observer who is typically present in comparable paper-based cognitive tests. We looked into the movement patterns of a real-world dragging task – the C-TOC Pattern Construction task – and found that older adults naturally adopted different movement patterns between devices: they tended to make shorter moves and a greater number of moves on a touchscreen than with a mouse. This indicates that careful device-based calibration will be needed for new performance metrics in computerized tests.
The HCI community has identified the need to let users adapt their software to their own tasks and preferences. Yet, many users do not customize, or only do so rarely. The de facto standard customization mechanism is the settings panel, which has undergone minimal design improvements since it was introduced along with the graphical user interface in the 1980s. Entirely disconnected from the application UI, these panels afford only very indirect manipulation, as users must rely on often cryptic text labels to identify the settings they want to change. From a developer’s point of view these panels make sense: they are simple graphical representations of traditional UNIX config files. In this thesis, we propose a novel customization approach, designed from the user’s point of view. In Anchored Customization, settings are anchored to conceptually related elements of the application UI. Our Customization Layer instantiates this approach: users can see which UI elements are customizable, and access their associated settings. We designed three variants of customization layer based on multi-layered interfaces, and implemented these variants on top of a popular web application for task management, Wunderlist. A remote experiment conducted on Mechanical Turk, complemented with a face-to-face lab experiment (for a total of 60 participants) showed that the two minimalist variants were 35% faster than Wunderlist’s settings panel. This new approach provides significant benefits for users while requiring little extra work from designers and developers of applications.
This work reports on the design and evaluation of culturally appropriate technology. We investigated cultural differences related to attitudes toward uncertainty between Western Caucasians (more tolerant) and East Asians (less tolerant). Using theory triangulation of cultural attitudes toward uncertainty, we designed information-minimal and information-rich interfaces and hypothesized they would be culturally appropriate for Caucasians and East Asians respectively. Our design context was Cognitive Testing on a Computer (C-TOC): a home-based computerized test under development, intended to screen older adults for cognitive impairments in the absence of a health professional. Using the two interfaces we designed for one C-TOC subtest, we ran an experiment with 36 participants to investigate the effects of cultural attitudes toward uncertainty on performance, preference and experience of anxiety. We found that East Asians preferred the information-rich interface augmented with security elements and learning support: they found it easier to use and felt less anxious with it. By contrast, Caucasians preferred the simpler information-minimal interface with only elements essential for the primary task. Based on our findings, we provide cultural design guidelines for Western Caucasians and East Asians in interaction contexts characterized by uncertainty, such as cognitive testing. We also provide guidelines for using a short uncertainty avoidance questionnaire as a low-cost method for creating adaptive interfaces that cater to varying cultural attitudes toward uncertainty.
Software targeted at children does not typically take into consideration the significant variation in skills and capabilities across age and gender. The overall goal of our research was to design adaptive interfaces that change to accommodate the inherent age and gender differences among children. We conducted two studies towards this goal at Science World with 195 children between ages 3 to 12. In the first exploratory study, we observed how 111 children interacted with Tux Paint, a painting application designed for children, and the difficulties they encountered in general. We were also interested in the possibility of accelerating children's learning of the interface with the least help from adults. Hence, we observed how they used the help system and how they learned by watching their peers. We found that designing an effective help system for children was a tricky proposition fraught with challenges. As for our inquiry into the general difficulties, we identified that dialogs were a significant source of problems for children. We classified the problems with dialogs by age groups and set out to solve them with potential design solutions targeted at three different age groups. In the second observational study, we observed how 84 children interacted with our various dialog box designs embodying 8 design factors. The dialog boxes were designed with the goal of enabling efficient communication of information; children need to understand the information that is communicated and make informed decisions. We found that while some design factors helped achieve effective communication, some did not. We present our results and an analysis of children‟s information consumption behavior, especially with respect to age and gender differences, in the context of their interaction with dialog boxes. We put forth theories and present models on how children of different age and gender consumed information differently from different information channels (textual and non-textual). We discuss the design implications of our findings that could help designers in constructing adaptive interfaces that improve the interaction by taking the age and gender into account.