Krzysztof Z. Gajos

SUPPLE: automatically generating user interfaces

In order to give people ubiquitous access to software applications, device controllers, and Internet services, it will be necessary to automatically adapt user interfaces to the computational devices at hand (eg, cell phones, PDAs, touch panels, etc.). While previous researchers have proposed solutions to this problem, each has limitations. This paper proposes a novel solution based on treating interface adaptation as an optimization problem. When asked to render an interface on a specific device, our supple system searches for the rendition that meets the devices constraints and minimizes the estimated effort for the users expected interface actions. We make several contributions: 1) precisely defining the interface rendition problem, 2) demonstrating how user traces can be used to customize interface rendering to particular users usage pattern, 3) presenting an efficient interface rendering algorithm, 4) performing experiments that demonstrate the utility of our approach.

Opportunity Knocks: A System to Provide Cognitive Assistance with Transportation Services

We present an automated transportation routing system, called “Opportunity Knocks,” whose goal is to improve the efficiency, safety and independence of individuals with mild cognitive disabilities. Our system is implemented on a combination of a Bluetooth sensor beacon that broadcasts GPS data, a GPRS-enabled cell-phone, and remote activity inference software. The system uses a novel inference engine that does not require users to explicitly provide information about the start or ending points of their journeys; instead this information is learned from users’ past behavior. Futhermore, we demonstrate how route errors can be detected and how the system helps to correct the errors with real-time transit information. In addition we present a novel solution to the problem of labeling positions with place names.

Platemate: crowdsourcing nutritional analysis from food photographs

We introduce PlateMate, a system that allows users to take photos of their meals and receive estimates of food intake and composition. Accurate awareness of this information can help people monitor their progress towards dieting goals, but current methods for food logging via self-reporting, expert observation, or algorithmic analysis are time-consuming, expensive, or inaccurate. PlateMate crowdsources nutritional analysis from photographs using Amazon Mechanical Turk, automatically coordinating untrained workers to estimate a meals calories, fat, carbohydrates, and protein. We present the Management framework for crowdsourcing complex tasks, which supports PlateMates nutrition analysis workflow. Results of our evaluations show that PlateMate is nearly as accurate as a trained dietitian and easier to use for most users than traditional self-reporting.

Ability-Based Design: Concept, Principles and Examples

Current approaches to accessible computing share a common goal of making technology accessible to users with disabilities. Perhaps because of this goal, they may also share a tendency to centralize disability rather than ability. We present a refinement to these approaches called ability-based design that consists of focusing on ability throughout the design process in an effort to create systems that leverage the full range of human potential. Just as user-centered design shifted the focus of interactive system design from systems to users, ability-based design attempts to shift the focus of accessible design from disability to ability. Although prior approaches to accessible computing may consider users’ abilities to some extent, ability-based design makes ability its central focus. We offer seven ability-based design principles and describe the projects that inspired their formulation. We also present a research agenda for ability-based design.

The efficacy of Web-based cognitive behavioral interventions for chronic pain: a systematic review and meta-analysis.

UNLABELLED Our objective was to conduct a systematic review and meta-analysis to quantify the efficacy of web-based cognitive behavioral interventions for the treatment of patients with chronic pain. MEDLINE and other databases were searched as data sources. Reference lists were examined for other relevant articles. We included 11 studies that evaluated the effects of web-based interventions on chronic pain using specific scales of pain. The pooled effect size (standardized mean difference between intervention versus waiting-list group means) from a random effects model was .285 (95% confidence interval: .145-.424), favoring the web-based intervention compared with the waiting-list group, although the effect was small. In addition, these results were not driven by any particular study, as shown by sensitivity analysis. Results from funnel plot argue against publication bias. Finally, the average dropout rate was 26.6%. In our meta-analysis, we demonstrate a small effect of web-based interventions, when using pain scale as the main outcome. Despite the minor effects and high dropout rates, the decreased costs and minor risk of adverse effects compared with pharmacological treatments support additional studies in chronic pain patients using web-based interventions. Further studies will be important to confirm the effects and determine the best responders to this intervention. PERSPECTIVE Our findings suggest that web-based interventions for chronic pain result in small pain reductions in the intervention group compared with waiting-list control groups. These results advance the field of web-based cognitive behavioral interventions as a potential therapeutic tool for chronic pain and can potentially help clinicians and patients with chronic pain by decreasing treatment costs and side effects.

Exploring the design space for adaptive graphical user interfaces

For decades, researchers have presented different adaptive user interfaces and discussed the pros and cons of adaptation on task performance and satisfaction. Little research, however, has been directed at isolating and understanding those aspects of adaptive interfaces which make some of them successful and others not. We have designed and implemented three adaptive graphical interfaces and evaluated them in two experiments along with a non-adaptive baseline. In this paper we synthesize our results with previous work and discuss how different design choices and interactions affect the success of adaptive graphical user interfaces.

Human computation tasks with global constraints

An important class of tasks that are underexplored in current human computation systems are complex tasks with global constraints. One example of such a task is itinerary planning, where solutions consist of a sequence of activities that meet requirements specified by the requester. In this paper, we focus on the crowdsourcing of such plans as a case study of constraint-based human computation tasks and introduce a collaborative planning system called Mobi that illustrates a novel crowdware paradigm. Mobi presents a single interface that enables crowd participants to view the current solution context and make appropriate contributions based on current needs. We conduct experiments that explain how Mobi enables a crowd to effectively and collaboratively resolve global constraints, and discuss how the design principles behind Mobi can more generally facilitate a crowd to tackle problems involving global constraints.

Understanding in-video dropouts and interaction peaks inonline lecture videos

With thousands of learners watching the same online lecture videos, analyzing video watching patterns provides a unique opportunity to understand how students learn with videos. This paper reports a large-scale analysis of in-video dropout and peaks in viewership and student activity, using second-by-second user interaction data from 862 videos in four Massive Open Online Courses (MOOCs) on edX. We find higher dropout rates in longer videos, re-watching sessions (vs first-time), and tutorials (vs lectures). Peaks in re-watching sessions and play events indicate points of interest and confusion. Results show that tutorials (vs lectures) and re-watching sessions (vs first-time) lead to more frequent and sharper peaks. In attempting to reason why peaks occur by sampling 80 videos, we observe that 61% of the peaks accompany visual transitions in the video, e.g., a slide view to a classroom view. Based on this observation, we identify five student activity patterns that can explain peaks: starting from the beginning of a new material, returning to missed content, following a tutorial step, replaying a brief segment, and repeating a non-visual explanation. Our analysis has design implications for video authoring, editing, and interface design, providing a richer understanding of video learning on MOOCs.

Automatically generating user interfaces adapted to users' motor and vision capabilities

Most of todays GUIs are designed for the typical, able-bodied user; atypical users are, for the most part, left to adapt as best they can, perhaps using specialized assistive technologies as an aid. In this paper, we present an alternative approach: SUPPLE++ automatically generates interfaces which are tailored to an individuals motor capabilities and can be easily adjusted to accommodate varying vision capabilities. SUPPLE++ models users. motor capabilities based on a onetime motor performance test and uses this model in an optimization process, generating a personalized interface. A preliminary study indicates that while there is still room for improvement, SUPPLE++ allowed one user to complete tasks that she could not perform using a standard interface, while for the remaining users it resulted in an average time savings of 20%, ranging from an slowdown of 3% to a speedup of 43%.

Predicting users' first impressions of website aesthetics with a quantification of perceived visual complexity and colorfulness

Users make lasting judgments about a websites appeal within a split second of seeing it for the first time. This first impression is influential enough to later affect their opinions of a sites usability and trustworthiness. In this paper, we demonstrate a means to predict the initial impression of aesthetics based on perceptual models of a websites colorfulness and visual complexity. In an online study, we collected ratings of colorfulness, visual complexity, and visual appeal of a set of 450 websites from 548 volunteers. Based on these data, we developed computational models that accurately measure the perceived visual complexity and colorfulness of website screenshots. In combination with demographic variables such as a users education level and age, these models explain approximately half of the variance in the ratings of aesthetic appeal given after viewing a website for 500ms only.