Computer Science

Chartjunk is a popular yet contentious topic. Previous studies have shown that extreme minimalism is not always best, and that visual embellishments can be useful depending on the context. While more knowledge is being developed regarding the effects of embellishments on users, less attention has been given to the perspectives of practitioners regarding how they design with embellishments. We conducted semi-structured interviews with 20 data visualization practitioners, investigating how they understand chartjunk and the factors that influence how and when they make use of embellishments. Our investigation uncovers a broad and pluralistic understanding of chartjunk among practitioners, and foregrounds a variety of personal and situated factors that influence the use of chartjunk beyond context. We highlight the personal nature of design practice, and discuss the need for more practice-led research to better understand the ways in which concepts like chartjunk are interpreted and used by practitioners.

Monitoring of students behavior in school needs further consideration in order to lessen the number of casualties in every term. The study designs a data driven decision support on students behavior utilizing Fuzzy Based Approach. The study successfully produces common behavioral problems of the student and able to give interventions for the improvement of students behavior. Student behavioral problems identified were absenteeism, tardiness and poor academic performance.

We introduce the notion of a data-first design study which is triggered by the acquisition of real-world data instead of specific stakeholder analysis questions. We propose an adaptation of the design study methodology framework to provide practical guidance and to aid transferability to other data-first design processes. We discuss opportunities and risks by reflecting on two of our own data-first design studies. We review 64 previous design studies and identify 16 of them as edge cases with characteristics that may indicate a data-first design process in action.

Most mobile health apps employ data visualization to help people view their health and activity data, but these apps provide limited support for visual data exploration. Furthermore, despite its huge potential benefits, mobile visualization research in the personal data context is sparse. This work aims to empower people to easily navigate and compare their personal health data on smartphones by enabling flexible time manipulation with speech. We designed and developed Data@Hand, a mobile app that leverages the synergy of two complementary modalities: speech and touch. Through an exploratory study with 13 long-term Fitbit users, we examined how multimodal interaction helps participants explore their own health data. Participants successfully adopted multimodal interaction (i.e., speech and touch) for convenient and fluid data exploration. Based on the quantitative and qualitative findings, we discuss design implications and opportunities with multimodal interaction for better supporting visual data exploration on mobile devices.

Preattentive visual features such as hue or flickering can effectively draw attention to an object of interest -- for instance, an important feature in a scientific visualization. These features appear to pop out and can be recognized by our visual system, independently from the number of distractors. Most cues do not take advantage of the fact that most humans have two eyes. In cases where binocular vision is applied, it is almost exclusively used to convey depth by exposing stereo pairs. We present Deadeye, a novel preattentive visualization technique based on presenting different stimuli to each eye. The target object is rendered for one eye only and is instantly detected by our visual system. In contrast to existing cues, Deadeye does not modify any visual properties of the target and, thus, is particularly suited for visualization applications. Our evaluation confirms that Deadeye is indeed perceived preattentively. We also explore a conjunction search based on our technique and show that, in contrast to 3D depth, the task cannot be processed in parallel.

Visualization recommendation (VisRec) systems provide users with suggestions for potentially interesting and useful next steps during exploratory data analysis. These recommendations are typically organized into categories based on their analytical actions, i.e., operations employed to transition from the current exploration state to a recommended visualization. However, despite the emergence of a plethora of VisRec systems in recent work, the utility of the categories employed by these systems in analytical workflows has not been systematically investigated. Our paper explores the efficacy of recommendation categories by formalizing a taxonomy of common categories and developing a system, Frontier, that implements these categories. Using Frontier, we evaluate workflow strategies adopted by users and how categories influence those strategies. Participants found recommendations that add attributes to enhance the current visualization and recommendations that filter to sub-populations to be comparatively most useful during data exploration. Our findings pave the way for next-generation VisRec systems that are adaptive and personalized via carefully chosen, effective recommendation categories.

Early detection of mild cognitive impairment and dementia is vital as many therapeutic interventions are particularly effective at an early stage. A self-administered touch-based cognitive screening instrument, called DemSelf, was developed by adapting an examiner-administered paper-based instrument, the Quick Mild Cognitive Impairment (Qmci) screen. We conducted five semi-structured expert interviews including a think-aloud phase to evaluate usability problems. The extent to which the characteristics of the original subtests change by the adaption, as well as the conditions and appropriate context for practical application, were also in question. The participants had expertise in the domain of usability and human-machine interaction and/or in the domain of dementia and neuropsychological assessment. Participants identified usability issues in all components of the DemSelf prototype. For example, confirmation of answers was not consistent across subtests. Answers were sometimes logged directly when a button is tapped and cannot be corrected. This can lead to frustration and bias in test results, especially for people with vision or motor impairments. The direct adoption of time limits from the original paper-based instrument or the simultaneous verbal and textual item presentation also caused usability problems. DemSelf is a different test than Qmci and needs to be re-validated. Visual recognition instead of a free verbal recall is one of the main differences. Reading skill level seems to be an important confounding variable. Participants would generally prefer if the test is conducted in a medical office rather than at a patient's home so that someone is present for support and the result can be discussed directly.

Visual representations are becoming important in science communication and education. This explorative study investigates the perception of STEM researchers, without any specific visual design background, and the value of visual representations as tools to support the communication of technical and scientific knowledge among academics and a wider non-technical community. Early findings show that visual representations can positively support scientists to share research outcomes in a more compelling, visually clear, and impactful manner, reaching a wider audience across different disciplines.

The main goal of this paper is to discuss how to integrate the possibilities of crowdsourcing platforms with systems supporting workflow to enable the engagement and interaction with business tasks of a wider group of people. Thus, this work is an attempt to expand the functional capabilities of typical business systems by allowing selected process tasks to be performed by unlimited human resources. Opening business tasks to crowdsourcing, within established Business Process Management Systems (BPMS) will improve the flexibility of company processes and allow for lower work-load and greater specialization among the staff employed on-site. The presented conceptual work is based on the current international standards in this field, promoted by Workflows Management Coalition. To this end, the functioning of business platforms was analysed and their functionality was presented visually, followed by a proposal and a discussion of how to implement crowdsourcing into workflow systems.

Data visualization is becoming an increasingly popular field of design practice. Although many studies have highlighted the knowledge required for effective data visualization design, their focus has largely been on formal knowledge and logical decision-making processes that can be abstracted and codified. Less attention has been paid to the more situated and personal ways of knowing that are prevalent in all design activity. In this study, we conducted semi-structured interviews with data visualization practitioners during which they were asked to describe the practical and situated aspects of their design processes. Using a philosophical framework of design judgment from Nelson and Stolterman [23], we analyzed the transcripts to describe the volume and complex layering of design judgments that are used by data visualization practitioners as they describe and interrogate their work. We identify aspects of data visualization practice that require further investigation beyond notions of rational, model- or principle-directed decision-making processes.