Enhancing decision-making in user-centered web development: a methodology for card-sorting analysis

The World Wide Web has become a common platform for interactive software development. Most web applications feature custom user interfaces used by millions of people every day. Information architecture addresses the structural design of information to build quality web applications with improved usability of content, navigation, and findability. One of the most frequently utilized information architecture methods is card sorting—an affordable, user-centered approach for eliciting and evaluating categories and navigable items. Card sorting facilitates decision-making during the development process based on users’ mental models of a given application domain. However, although the qualitative analysis of card sorts has become common practice in information architecture, the quantitative analysis of card sorting is less widely applied. The reason for this gap is that quantitative analysis often requires the use of customized techniques to extract meaningful information for decision-making. To facilitate this process and support the structuring of information, we propose a methodology for the quantitative analysis of card-sorting results in this paper. The suggested approach can be systematically applied to provide clues and support for decisions. These might significantly impact the design and, thus, the final quality of the web application. Therefore, the approach includes proper goodness values that enable comparisons among the results of the methods and techniques used and ensure the suitability of the analyses performed. Two publicly available datasets were used to demonstrate the key issues related to the interpretation of card sorting results and the overall suitability and validity of the proposed methodologyThis work was partially supported by the Spanish Government [grant number RTI2018-095255-B-I00]; and the Madrid Research Council [Grant Number P2018/TCS-4314

Visual Anxiolytics: developing theory and design guidelines for abstract affective visualizations aimed at alleviating episodes of anxiety

Visual Anxiolytics is a novel term proposed to describe affective visualizations of which affective quality is predetermined and designed to alleviate anxiety and anxious pathology. This thesis presents ground theory and visual guidelines to inform the design of screen-based interfaces to give users aspects of a restorative and anxiolytic environment at a time when attention restoration is least likely and anxiety highly probable; during sedentary screen-time. Visual Anxiolytics are introduced as an affective layer of the interface capable of communicating affect through aesthetic, abstract, ambient emotion visualizations existing in the periphery of the screen and users’ vision. Their theory is brought into the field of Visual Communication Design from a number of disciplines; primarily Affective Computing, Human-Computer Interaction, Psychology, and Neuroscience. Visual Anxiolytics attempt to alleviate anxiety through restoration of attentional cognitive resources by rendering the digital environment restorative and by elicitation of positive emotions through affect communication. Design guidelines analyse and describe properties of anxiolytic affective visual attributes color, shape, motion, and visual depth, as well as compositional characteristics of Visual Anxiolytics. Potential implications for future research in emotion visualization and affect communication are discussed

Dynamic Visual Abstraction of Soccer Movement

Trajectory-based visualization of coordinated movement data within a bounded area, such as player and ball movement within a soccer pitch, can easily result in visual crossings, overplotting, and clutter. Trajectory abstraction can help to cope with these issues, but it is a challenging problem to select the right level of abstraction (LoA) for a given data set and analysis task. We present a novel dynamic approach that combines trajectory simplification and clustering techniques with the goal to support interpretation and understanding of movement patterns. Our technique provides smooth transitions between different abstraction types that can be computed dynamically and on-the-fly. This enables the analyst to effectively navigate and explore the space of possible abstractions in large trajectory data sets. Additionally, we provide a proof of concept for supporting the analyst in determining the LoA semi-automatically with a recommender system. Our approach is illustrated and evaluated by case studies, quantitative measures, and expert feedback. We further demonstrate that it allows analysts to solve a variety of analysis tasks in the domain of soccer

Analyzing the Information Search Behavior and Intentions in Visual Information Systems

Visual information search systems support different search approaches such as targeted, exploratory or analytical search. Those visual systems deal with the challenge of composing optimal initial result visualization sets that face the search intention and respond to the search behavior of users. The diversity of these kinds of search tasks require different sets of visual layouts and functionalities, e.g. to filter, thrill-down or even analyze concrete data properties. This paper describes a new approach to calculate the probability towards the three mentioned search intentions, derived from users’ behavior. The implementation is realized as a web-service, which is included in a visual environment that is designed to enable various search strategies based on heterogeneous data sources. In fact, based on an entered search query our developed search intention analysis web-service calculates the most probable search task, and our visualization system initially shows the optimal result set of visualizations to solve the task. The main contribution of this paper is a probability-based approach to derive the users’ search intentions based on the search behavior enhanced by the application to a visual system

Quantifying, Modeling and Managing How People Interact with Visualizations on the Web

The growing number of interactive visualizations on the web has made it possible for the general public to access data and insights that were once only available to domain experts. At the same time, this rise has yielded new challenges for visualization creators, who must now understand and engage a growing and diverse audience. To bridge this gap between creators and audiences, we explore and evaluate components of a design-feedback loop that would enable visualization creators to better accommodate their audiences as they explore the visualizations. In this dissertation, we approach this goal by quantifying, modeling and creating tools that manage people’s open-ended explorations of visualizations on the web. In particular, we: 1. Quantify the effects of design alternatives on people’s interaction patterns in visualizations. We define and evaluate two techniques: HindSight (encoding a user’s interaction history) and text-based search, where controlled experiments suggest that design details can significantly modulate the interaction patterns we observe from participants using a given visualization. 2. Develop new metrics that characterize facets of people’s exploration processes. Specifically, we derive expressive metrics describing interaction patterns such as exploration uniqueness, and use Bayesian inference to model distributional effects on interaction behavior. Our results show that these metrics capture novel patterns in people’s interactions with visualizations. 3. Create tools that manage and analyze an audience’s interaction data for a given visualization. We develop a prototype tool, ReVisIt, that visualizes an audience’s interactions with a given visualization. Through an interview study with visualization creators, we found that ReVisIt make creators aware of individual and overall trends in their audiences’ interaction patterns. By establishing some of the core elements of a design-feedback loop for visualization creators, the results in this research may have a tangible impact on the future of publishing interactive visualizations on the web. Equipped with techniques, metrics, and tools that realize an initial feedback loop, creators are better able to understand the behavior and user needs, and thus create visualizations that make data and insights more accessible to the diverse audiences on the web

Designing for empowerment

Technology bears the potential to empower people - to help them tackle challenges they would otherwise give up on or not even try, to make experiences possible they did not have access to before. One type of such technologies - the application area of this thesis - is health and wellbeing technology (HWT), such as digital health records, physical activity trackers, or digital fitness coach applications. HWTs often claim to empower people to live healthier and happier lives. However, there is reason to challenge and critically reflect on these claims and underlying assumptions as more and more researchers are finding that technologies aiming or claiming to be empowering often turn out to be disempowering. This critical reflection is the starting point of this thesis: Can HWTs really empower people in their everyday lives? If so, how should we go about designing them to foster empowerment and avoid disempowerment? To this aim, this thesis makes three main contributions: First, it presents a framework of empowering technologies that aims to introduce conceptual and terminological clarity of empowerment in the field of Human-Computer Interaction (HCI). As a literature review conducted for this thesis reveals, the understandings of empowerment in HCI diverge substantially, rendering the term a subsumption of diverse research endeavors. The presented framework is informed by the results of the literature review as well as prior work on empowerment in social sciences, psychology, and philosophy. It aims to help other researchers to analyze conceptual differences between their own work and others’ and to position their research projects. In the same way, this thesis uses the proposed framework to analyze and reflect on the conducted case studies. Second, this thesis explores how HWT can empower people in a number of studies. Technologies that are investigated in these studies are divided into three interaction paradigms (derived from Beaudouin-Lafon’s interaction paradigms): Technologies that follow the computer-as-tool paradigm include patient-controlled electronic health records, and physical activity trackers; technologies in the computer-as-partner paradigm include personalized digital fitness coaches; and technologies in the computer-as-intelligent-tool paradigm includes transparently designed digital coaching technology. For each of these paradigms, I discuss benefits and shortcomings, as well as recommendations for future work. Third, I explore methods for designing and evaluating empowering technology. Therefore, I analyze and discuss methods that have been used in the different case studies to inform the design of empowering technologies such as interviews, observations, personality tests, experience sampling, or the Theory of Planned Behavior. Further, I present the design and evaluation of two tools that aimed to help researchers and designers evaluate empowering technologies by eliciting rich, contextualized feedback from users and fostering an empathic relationship between users and designers. I hope that my framework, design explorations, and evaluation tools will serve research on empowering technologies in HCI to develop a more grounded understanding, a clear research agenda, and inspire the development of a new class of empowering HWTs.Technologie für Empowerment — im Deutschen am besten mit Befähigung oder Ermächtigung übersetzt: diese Vision ist sowohl in medizinischen und technischen Fachkreisen als auch in der wissenschaftlichen Literatur im Feld Mensch-Maschine Interaktion (MMI) weit verbreitet. Technologie kann — laut dieser Vision — Menschen helfen Herausforderungen zu meistern, die sie sonst nicht schaffen oder nicht mal versuchen würden, oder Ihnen komplett neue Erfahrungen ermöglichen. Eine Art von “empowernden”, also befähigenden Technologien sind Technologien für Gesundheit und Wohlbefinden (health and wellbeing technologies, HWT), wie beispielsweise digitale Krankenakten, Schrittzähler, oder digitale Fitnesstrainer. Sowohl Werbung als auch Forschung über HWTs preist diese häufig als Schlüssel zu einem gesünderen und glücklicheren Leben an. Es gibt aber durchaus Gründe diesen Behauptungen kritisch gegenüberzustehen. So haben bereits einige Forschungsprojekte über vermeintlich “empowernde” Technologien ergeben, dass diese eher entmächtigen — also Ihre Nutzer mehr einschränken als Ihnen mehr Möglichkeiten zu verschaffen. Eine kritische Reflexion der Annahme, dass HWTs ihre Nutzer empowern stellt den Ausgangspunkt dieser Dissertation dar: Können HWTs ihre Nutzer wirklich empowern? Falls dem so ist, wie sollten sie am besten gestaltet werden? Der Beitrag meiner Dissertation zur Beantwortung dieser Fragen wird in drei Teilen präsentiert: Im ersten Teil stelle ich ein konzeptuelles Framework vor, mit dem Ziel terminologische Klarheit im Bereich Empowerment in MMI zu fördern. Eine Literaturanalyse im Rahmen dieser Dissertation hat ergeben, dass die Verwendungen des Begriffs “Empowerment” in der MMI Literatur sehr stark voneinander abweichen. Beispielsweise wird der Begriff in Literatur über Technologien für Barrierefreiheit anders verstanden als in Literatur über Technologien für bürgerliches Engagement. Folglich schert das Schlagwort “Technologien für Empowermen”, das in Präsentationen und Denkschriften weit verbreitet ist, komplett unterschiedliche Ansätze über einen Kamm. Das Framework, das in dieser Dissertation vorgestellt wird, zeigt die Unterschiede und Gemeinsamkeiten bei der Verwendung des Empowermentbegriffs auf. Es entstand als Resultat der Literaturanalyse und integriert gleichzeitig Erkenntnisse von Empowermenttheorien die in Sozialwissenschaften, Psychologie und Philosophie diskutiert wurden. In dieser Dissertation wird das vorgestellte Framework verwendet, um die präsentierten Studien über HWTs einzuordnen und zu diskutieren. Im zweiten Teil präsentiere ich verschiedene empirische und technische Studien mit dem Ziel zu verstehen wie HWTs Menschen empowern können. Die Technologien, die dabei untersucht werden teile ich in drei Interaktionsparadigmen ein (die von den Interaktionsparadigmen von Beaudouin-Lafon abgeleitet sind): Technologien im Paradigma Computerals- Werkzeug sind beispielsweise digitale Krankenakten und Schrittzähler; Technologien im Paradigma Computer-als-Partner sind beispielsweise digitale personalisierte Fitnesstrainer und Technologien im Paradigma Computer-als-intelligentes-Werkzeug sind beispielsweise transparent gestaltete digitale personalisierte Gesundheitsberater oder Fitnesstrainer. Vorund Nachteile von Technologien in diesen drei Paradigmen werden diskutiert und Empfehlungen für zukünftige Forschung in diesen Bereichen abgeleitet. Im dritten Teil, untersuche ich, welche Methoden für die Gestaltung und Evaluierung von empowernden Technologien geeignet sind. Einerseits diskutiere ich die Vor- und Nachteile der Methoden, die in den einzelnen Untersuchungen von HWTs (im zweiten Teil) verwendet wurden, wie zum Beispiel Interviews, Observationen, die Experience Sampling Methode oder Fragebögen basierend auf der Theorie des geplanten Verhaltens. Andererseits berichte ich über die Gestaltung und Entwicklung von zwei Applikationen mit dem Ziel Forschern und Designern die Evaluation von empowernden Technologien zu erleichtern. Konkret hat die erste Applikation das Ziel es Testnutzern zu ermöglichen immer und überall für sie wichtige Aspekte des Nutzererlebnisses an das Entwicklungsteam weiterzugeben. Bei der Entwicklung der zweiten Applikation stand dagegen die Förderung von Empathie zwischen Nutzern und Designern im Vordergrund. Ich hoffe, dass das vorgestellte Framework, die Studien über HWTs und Evaluationswerkezeuge die Forschung über empowernde Technologien voranbringen, zu einer klaren Forschungsagenda beitragen, und die Entwicklung von neuartigen HWTs anregen werden

Automatic generation of software interfaces for supporting decisionmaking processes. An application of domain engineering & machine learning

[EN] Data analysis is a key process to foster knowledge generation in particular domains or fields of study. With a strong informative foundation derived from the analysis of collected data, decision-makers can make strategic choices with the aim of obtaining valuable benefits in their specific areas of action. However, given the steady growth of data volumes, data analysis needs to rely on powerful tools to enable knowledge extraction. Information dashboards offer a software solution to analyze large volumes of data visually to identify patterns and relations and make decisions according to the presented information. But decision-makers may have different goals and, consequently, different necessities regarding their dashboards. Moreover, the variety of data sources, structures, and domains can hamper the design and implementation of these tools. This Ph.D. Thesis tackles the challenge of improving the development process of information dashboards and data visualizations while enhancing their quality and features in terms of personalization, usability, and flexibility, among others. Several research activities have been carried out to support this thesis. First, a systematic literature mapping and review was performed to analyze different methodologies and solutions related to the automatic generation of tailored information dashboards. The outcomes of the review led to the selection of a modeldriven approach in combination with the software product line paradigm to deal with the automatic generation of information dashboards. In this context, a meta-model was developed following a domain engineering approach. This meta-model represents the skeleton of information dashboards and data visualizations through the abstraction of their components and features and has been the backbone of the subsequent generative pipeline of these tools. The meta-model and generative pipeline have been tested through their integration in different scenarios, both theoretical and practical. Regarding the theoretical dimension of the research, the meta-model has been successfully integrated with other meta-model to support knowledge generation in learning ecosystems, and as a framework to conceptualize and instantiate information dashboards in different domains. In terms of the practical applications, the focus has been put on how to transform the meta-model into an instance adapted to a specific context, and how to finally transform this later model into code, i.e., the final, functional product. These practical scenarios involved the automatic generation of dashboards in the context of a Ph.D. Programme, the application of Artificial Intelligence algorithms in the process, and the development of a graphical instantiation platform that combines the meta-model and the generative pipeline into a visual generation system. Finally, different case studies have been conducted in the employment and employability, health, and education domains. The number of applications of the meta-model in theoretical and practical dimensions and domains is also a result itself. Every outcome associated to this thesis is driven by the dashboard meta-model, which also proves its versatility and flexibility when it comes to conceptualize, generate, and capture knowledge related to dashboards and data visualizations

On intelligible multimodal visual analysis

Analyzing data becomes an important skill in a more and more digital world. Yet, many users are facing knowledge barriers preventing them to independently conduct their data analysis. To tear down some of these barriers, multimodal interaction for visual analysis has been proposed. Multimodal interaction through speech and touch enables not only experts, but also novice users to effortlessly interact with such kind of technology. However, current approaches do not take the user differences into account. In fact, whether visual analysis is intelligible ultimately depends on the user. In order to close this research gap, this dissertation explores how multimodal visual analysis can be personalized. To do so, it takes a holistic view. First, an intelligible task space of visual analysis tasks is defined by considering personalization potentials. This task space provides an initial basis for understanding how effective personalization in visual analysis can be approached. Second, empirical analyses on speech commands in visual analysis as well as used visualizations from scientific publications further reveal patterns and structures. These behavior-indicated findings help to better understand expectations towards multimodal visual analysis. Third, a technical prototype is designed considering the previous findings. Enriching the visual analysis by a persistent dialogue and a transparency of the underlying computations, conducted user studies show not only advantages, but address the relevance of considering the user’s characteristics. Finally, both communications channels – visualizations and dialogue – are personalized. Leveraging linguistic theory and reinforcement learning, the results highlight a positive effect of adjusting to the user. Especially when the user’s knowledge is exceeded, personalizations helps to improve the user experience. Overall, this dissertations confirms not only the importance of considering the user’s characteristics in multimodal visual analysis, but also provides insights on how an intelligible analysis can be achieved. By understanding the use of input modalities, a system can focus only on the user’s needs. By understanding preferences on the output modalities, the system can better adapt to the user. Combining both directions imporves user experience and contributes towards an intelligible multimodal visual analysis