Task-Centric User Interfaces

Software applications for design and creation typically contain hundreds or thousands of commands, which collectively give users enormous expressive power. Unfortunately, rich feature sets also take a toll on usability. Current interfaces to feature-rich software address this dilemma by adopting menus, toolbars, and other hierarchical schemes to organize functionality—approaches that enable efficient navigation to specific commands and features, but do little to reveal how to perform unfamiliar tasks. We present an alternative task-centric user interface design that explicitly supports users in performing unfamiliar tasks. A task-centric interface is able to quickly adapt itself to the user’s intended goal, presenting relevant functionality and required procedures in task-specific customized interfaces. To achieve this, task-centric interfaces (1) represent tasks as first-class objects in the interface; (2) allow the user to declare their intended goal (or infer it from the user’s actions); (3) restructure the interface to provide step-by-step scaffolding for the current goal; and (4) provide additional knowledge and guidance within the application’s interface. Our inspiration for task-centric interfaces comes from a study we conducted, which revealed that a valid use case for feature-rich software is to perform short, targeted tasks that use a small fraction of the application’s full functionality. Task-centric interfaces provide explicit support for this use. We developed and tested our task-centric interface approach by creating AdaptableGIMP, a modified version of the GIMP image editor, and Workflows, an iteration on AdaptableGIMP’s design based on insights from a semi-structured interview study and a think-aloud study. Based on a two-session study of Workflows, we show that task-centric interfaces can successfully support a guided-and-constrained problem solving strategy for performing unfamiliar tasks, which enables faster task completion and reduced cognitive load as compared to current practices. We also provide evidence that task-centric interfaces can enable a higher-level form of application learning, in which the user associates tasks with relevant keywords, as opposed to low-level commands and procedures. This keyword learning has potential benefits for memorability, because the keywords themselves are descriptive of the task being learned, and scalability, because a few keywords can map to an arbitrarily complex set of commands and procedures. Finally, our findings suggest a range of different ways that the idea of task-centric interfaces could be further developed

A Systematic and Minimalist Approach to Lower Barriers in Visual Data Exploration

With the increasing availability and impact of data in our lives, we need to make quicker, more accurate, and intricate data-driven decisions. We can see and interact with data, and identify relevant features, trends, and outliers through visual data representations. In addition, the outcomes of data analysis reflect our cognitive processes, which are strongly influenced by the design of tools. To support visual and interactive data exploration, this thesis presents a systematic and minimalist approach. First, I present the Cognitive Exploration Framework, which identifies six distinct cognitive stages and provides a high-level structure to design guidelines, and evaluation of analysis tools. Next, in order to reduce decision-making complexities in creating effective interactive data visualizations, I present a minimal, yet expressive, model for tabular data using aggregated data summaries and linked selections. I demonstrate its application to common categorical, numerical, temporal, spatial, and set data types. Based on this model, I developed Keshif as an out-of-the-box, web-based tool to bootstrap the data exploration process. Then, I applied it to 160+ datasets across many domains, aiming to serve journalists, researchers, policy makers, businesses, and those tracking personal data. Using tools with novel designs and capabilities requires learning and help-seeking for both novices and experts. To provide self-service help for visual data interfaces, I present a data-driven contextual in-situ help system, HelpIn, which contrasts with separated and static videos and manuals. Lastly, I present an evaluation on design and graphical perception for dense visualization of sorted numeric data. I contrast the non-hierarchical treemaps against two multi-column chart designs, wrapped bars and piled bars. The results support that multi-column charts are perceptually more accurate than treemaps, and the unconventional piled bars may require more training to read effectively. This thesis contributes to our understanding on how to create effective data interfaces by systematically focusing on human-facing challenges through minimalist solutions. Future work to extend the power of data analysis to a broader public should continue to evaluate and improve design approaches to address many remaining cognitive, social, educational, and technical challenges

The Macro-structure of Use of Help

Users of help systems often complain that they do not find them useful; while they still use help at least occasionally, they resort to other problem-solving strategies. In this paper, we analyze audiovisual recordings of people using a computer application, to identify (1) transition patterns among problem-solving approaches, and (2)the frequency of these transitions. Our analysis indicates that people switch frequently between consulting help and exploring the interface. Switching between problem-solving approaches appears to be an effective way of succeeding in tasks. Applications and their help systems can be better designed to support users who switch between help and non-help approaches to solving problems