Investigating the direct manipulation of ranking tables for time navigation

We introduce a novel time navigation technique to update ranking tables by direct manipulation. The technique allows users to drag a table's cells to change the time period, while a line chart overlays on top of the table to provide an overview of the changes. The line chart is also a visual hint to control the pace at which data are updated. We explore the design and usability of this technique for table variations in size, time spans and data variability. We report the results of a usability study, using academic citation rankings and economic complexity datasets, and discuss design implications coming with real-world scenarios such as missing data and affordance

User-centered design of a dynamic-autonomy remote interaction concept for manipulation-capable robots to assist elderly people in the home

In this article, we describe the development of a human-robot interaction concept for service robots to assist elderly people in the home with physical tasks. Our approach is based on the insight that robots are not yet able to handle all tasks autonomously with sufficient reliability in the complex and heterogeneous environments of private homes. We therefore employ remote human operators to assist on tasks a robot cannot handle completely autonomously. Our development methodology was user-centric and iterative, with six user studies carried out at various stages involving a total of 241 participants. The concept is under implementation on the Care-O-bot 3 robotic platform. The main contributions of this article are (1) the results of a survey in form of a ranking of the demands of elderly people and informal caregivers for a range of 25 robot services, (2) the results of an ethnography investigating the suitability of emergency teleassistance and telemedical centers for incorporating robotic teleassistance, and (3) a user-validated human-robot interaction concept with three user roles and corresponding three user interfaces designed as a solution to the problem of engineering reliable service robots for home environments

Dynamic Composite Data Physicalization Using Wheeled Micro-Robots

This paper introduces dynamic composite physicalizations, a new class of physical visualizations that use collections of self-propelled objects to represent data. Dynamic composite physicalizations can be used both to give physical form to well-known interactive visualization techniques, and to explore new visualizations and interaction paradigms. We first propose a design space characterizing composite physicalizations based on previous work in the fields of Information Visualization and Human Computer Interaction. We illustrate dynamic composite physicalizations in two scenarios demonstrating potential benefits for collaboration and decision making, as well as new opportunities for physical interaction. We then describe our implementation using wheeled micro-robots capable of locating themselves and sensing user input, before discussing limitations and opportunities for future work

Sports Tournament Predictions Using Direct Manipulation

An advanced interface for sports tournament predictions uses direct manipulation to allow users to make nonlinear predictions. Unlike previous interface designs, the interface helps users focus on their prediction tasks by enabling them to first choose a winner and then fill out the rest of the bracket. In real-world tests of the proposed interface (for the 2014 FIFA World Cup tournament and 2015/2016 UEFA Champions League), the authors validated the use of direct manipulation as an alternative to widgets. Using visitor interaction logs, they were able to determine the strategies people use to perform predictions and identify potential areas of improvement for further prediction interfaces

User perspective on AM-enabled mass customisation toolkits

Mass Customisation (MC) toolkits are powerful user interfaces that enable customers to engage in the design of their own products. This research follows design research methodology (integrated with design process) to research the user perspective on AM-enabled MC toolkits. This research proposes and validates a design framework to guide designers and software developers in designing a user-centred AM-enabled MC toolkits, enabled using digital fabrication technologies such as Additive Manufacturing (AM). This framework includes pre-implementation assessment, and implementation stages. An initial literature review revealed a lack of standard or universal norms for these user interfaces, and a lack of consistency in their design, in which web objects such as logo, product image, prices, etc. are not shared commonly among toolkits, nor occupy a frequent position. Furthermore, an optimum number of degree of freedom for MC toolkits is lacking from current design knowledge. This research focuses on AM-enabled Mass Customisation toolkits as a means to enable customers design; its concentration is on users. A first quantitative study was conducted to compare and rank of a collection of features. More detailed user requirements regarding the content and layout of MC toolkits were revealed in a workshop. As a part of the second study, four different CAD systems (software programs and 3D-enabling libraries) were used to create MC toolkits. This provided an understanding of the pros and cons of each system, and demonstrated Three.js to be the best system amongst each one s feasibility and application. Based on previous findings, and as a part of the UX-design process, a prototype web-based MC toolkit was constructed, utilising the Three.js library. The prototype was used for a second study as a platform to investigate, the user interaction and usability of the toolkit, to validate the toolkit design as well as provide insights for its improvement. Findings and reflections from all the studies were then visualised and communicated in an interactive design framework. A final study, conducted with professional users (N=4) assessed the usability and technicality of the framework tool and led to a number of suggested improvements. The main contributions to knowledge are: 1- a table was produced to compare the features of four different system, by which Three.js was identified as the most suitable among them 2- most important and expected features for the content were obtained from the user rankings, most frequent location of features for the layout was identified based on the users, and user insights were reflected based on the evaluation of the prototype 3- the UI needs to be flexible in term of degrees of freedom, in another words, each customer (novice or professional) is able to adjust the number of options presented. 4- a framework was proposed through reviewing and adapting existing guidelines and findings from this research

Evaluating advanced search interfaces using established information-seeking model

When users have poorly defined or complex goals search interfaces offering only keyword searching facilities provide inadequate support to help them reach their information-seeking objectives. The emergence of interfaces with more advanced capabilities such as faceted browsing and result clustering can go some way to some way toward addressing such problems. The evaluation of these interfaces, however, is challenging since they generally offer diverse and versatile search environments that introduce overwhelming amounts of independent variables to user studies; choosing the interface object as the only independent variable in a study would reveal very little about why one design out-performs another. Nonetheless if we could effectively compare these interfaces we would have a way to determine which was best for a given scenario and begin to learn why. In this article we present a formative framework for the evaluation of advanced search interfaces through the quantification of the strengths and weaknesses of the interfaces in supporting user tactics and varying user conditions. This framework combines established models of users, user needs, and user behaviours to achieve this. The framework is applied to evaluate three search interfaces and demonstrates the potential value of this approach to interactive IR evaluation

Search engine effects on news consumption: Ranking and representativeness outweigh familiarity in news selection

While individuals' trust in search engine results is well-supported, little is known about their preferences when selecting news. We use web-tracked behavioral data across a 2-month period (280 participants) and we analyze three competing factors, two algorithmic (ranking and representativeness) and one psychological (familiarity), that could influence the selection of search results. We use news engagement as a proxy for familiarity and investigate news articles presented on Google search pages (n = 1221). We find a significant effect of algorithmic factors but not of familiarity. We find that ranking plays a lesser role for news compared to non-news, suggesting a more careful decision-making process. We confirm that Google Search drives individuals to unfamiliar sources, and find that it increases the diversity of the political audience of news sources. We tackle the challenge of measuring social science theories in contexts shaped by algorithms, demonstrating their leverage over the behaviors of individuals

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