1,721,134 research outputs found
Hybrid Online Survey System with Real-Time Moderator Chat
Full text linkOnline surveys present a quick and efficient method to collect user experience data. However, they are less effective at extracting qualitative data. Due to response fatigue when answering open-ended questions, the quality of responses may be poor, and participants can skip answering individual questions, or drop out from the survey entirely, potentially introducing bias. To address these challenges, we developed a prototype tool that enables a test moderator to initiate a chat intervention with a participant at any point during the completion of an online survey. Through this approach, participants can be prompted to return to complete unanswered questions or provide clarification to given answers. A functional prototype system has been implemented and, as future work, will be evaluated with a variety of content and question types
Tangible Explainable AI - an Initial Conceptual Framework
Full text linkArtificial Intelligence (AI) solutions are becoming prevalent in almost all aspects of human life. However, their acceptance may be limited by a lack of transparency of how the AI works. Explainable AI (XAI) aims to provide the users of AI systems with an understanding of why decisions are made, increasing trust in the system. To date, research into XAI has focused on the use of graphical user interfaces, presenting numerical, textual or graphical explanations. However, AI is increasingly being used in systems that include physical devices, and hence the need for explainability in physical or tangible user interfaces (TUI) is also increasing. We present an initial conceptual framework for tangible explainable AI (TangXAI), which identifies the potential approaches of communicating XAI through physical artifacts, using the concepts of data physicalization and tangible interaction. The framework provides a basis into which ongoing research of tangible explainable AI can be mapped and related research gaps identified
Investigating the Effect of Orientation and Visual Style on Touchscreen Slider Performance
No full textSliders are one of the most fundamental components usedin touchscreen user interfaces (UIs). When entering datausing a slider, errors occur due e.g. to visual perception,resulting in inputs not matching what is intended by the user.However, it is unclear if the errors occur uniformly acrossthe full range of the slider or if there are systematic offsets.We conducted a study to assess the errors occurring whenentering values with horizontal and vertical sliders as wellas two common visual styles. Our results reveal significanteffects of slider orientation and style on the precision of theentered values. Furthermore, we identify systematic offsetsthat depend on the visual style and the target value. As theerrors are partially systematic, they can be compensated toimprove users’ precision. Our findings provide UI designerswith data to optimize user experiences in the wide varietyof application areas where slider based touchscreen input isused
Comparing VR and Desktop 360 Video Museum Tours
Full text linkWe investigate the user experience of taking a remote museum tour with 360 video technologies. We compare the experience of viewing a 360 video feed on a laptop screen vs. a 360 virtual reality (VR) video experienced through a head-mounted display (HMD). Our salient findings from a user study (n = 10) highlight that HMD VR provides a better immersion and sense of control for users. However, the HMD VR user experience suffers from the lack of personal contact, such as eye contact with the guide, discontinuities in the visual presentation, and missing multimodal contextual cues. The research contributes to the design of remote tourism services
Break, Repair, Learn, Break Less: Investigating User Preferences for Assignment of Divergent Phrasing Learning Burden in Human-Agent Interaction to Minimize Conversational Breakdowns
No full textConversational agents (CA) occasionally fail to understand the user's intention or respond inappropriately due to natural language complexity. These conversational breakdowns can happen because of low intent and entity prediction confidence scores. A promising repair strategy in such cases is that the CA proposes to users likely alternatives to proceed. If one of these options matches the user's intention, the breakdown is repaired successfully. We propose that successful repairs should be followed by a learning mechanism to minimize future breakdowns. After a successful repair, the CA, user, or both can learn each other's specific phrasing. This prevents similar phrasings from causing reoccurring breakdowns. We compared user preferences for these learning mechanisms in a scenario-based study with manufacturing workers (). Our result showed that users first prefer to share the learning burden with the CA (61.3%), followed by entirely outsourcing the learning burden to the CA (60.7%) as opposed to themselves.Internet of Thing
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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