DevLan/vispubs huggingface dataset:
Group Spinner: Recognizing and Visualizing Learning in the Classroom for Reflection, Communication, and Planning
Authors: Ahmed Kharrufa, Sally Rix, Timur Osadchiy, Anne Preston, Patrick Olivier
Published at CHI, 2017

Group Spinner is a digital visual tool intended to help teachers observe and reflect on children's collaborative technology-enhanced learning activities in the classroom. We describe the design of Group Spinner, which was informed by activity theory, previous work and teachers' focus group feedback. Based on a radar chart and a set of indicators, Group Spinner allows teachers to record in-class observations as to different aspects of group learning and learning behaviors, beyond the limited knowledge acquisition measures. Our exploratory study involved 6 teachers who used the tool for a total of 23 classes in subjects ranging from Maths and Geography to Sociology and Art. Semi-structured interviews with these teachers revealed a number of different uses of the tool. Depending on their experience and pedagogy, teachers considered Group Spinner to be a valuable tool to support awareness, reflection, communication, and/or planning.
Has Instagram Fundamentally Altered the 'Family Snapshot'?
Authors: Effie Le Moignan, Shaun W. Lawson, Duncan A. Rowland, Jamie Mahoney, Pam Briggs
Published at CHI, 2017

This paper considers how parents use the social media platform Instagram to facilitate the capture, curation and sharing of 'family snapshots'. Our work draws upon established cross-disciplinary literature relating to film photography and the composition of family albums in order to establish whether social media has changed the way parents visually present their families. We conducted a qualitative visual analysis of a sample of 4,000 photographs collected from Instagram using hashtags relating to children and parenting. We show that the style and composition of snapshots featuring children remains fundamentally unchanged and continues to be dominated by rather bland and idealised images of the happy family and the cute child. In addition, we find that the frequent taking and sharing of photographs via Instagram has inevitably resulted in a more mundane visual catalogue of daily life. We note a tension in the desire to use social media as a means to evidence good parenting, while trying to effectively manage the social identity of the child and finally, we note the reluctance of parents to use their own snapshots to portray family tension or disharmony, but their willingness to use externally generated content for this purpose.
Improving Communication Between Pair Programmers Using Shared Gaze Awareness
Authors: Sarah D'Angelo, Andrew Begel
Published at CHI, 2017

Remote collaboration can be more difficult than collocated collaboration for a number of reasons, including the inability to easily determine what your collaborator is looking at. This impedes a pair's ability to efficiently communicate about on-screen locations and makes synchronous coordination difficult. We designed a novel gaze visualization for remote pair programmers which shows where in the code their partner is currently looking, and changes color when they are looking at the same thing. Our design is unobtrusive, and transparently depicts the imprecision inherent in eye tracking technology. We evaluated our design with an experiment in which pair programmers worked remotely on code refactoring tasks. Our results show that with the visualization, pairs spent a greater proportion of their time concurrently looking at the same code locations. Pairs communicated using a larger ratio of implicit to explicit references, and were faster and more successful at responding to those references.
Is Two Enough?: ! Studying Benefits, Barriers, and Biases of Multi-Tablet Use for Collaborative Visualization
Authors: Thomas Plank, Hans-Christian Jetter, Roman Rädle, Clemens Nylandsted Klokmose, Thomas Luger, Harald Reiterer
Published at CHI, 2017

A sizable part of HCI research on cross-device interaction is driven by the vision of users conducting complex knowledge work seamlessly across multiple mobile devices. This is based on the Weiserian assumption that people will be inclined to distribute their work across multiple ``pads' if such are available. We observed that this is not the reality today, even when devices were in abundance. We present a study with 24 participants in 12 dyads completing a collaborative visualization task with up to six tablets. They could choose between three different visualization types to answer questions about economic data. Tasks were designed to afford simultaneous use of tablets, either with linked or independent views. We found that users typically utilized only one tablet per user. A quantitative and qualitative analysis revealed a ``legacy bias' that introduced barriers for using more tablets and reduced the overall benefit of multi-device visualization.
iSphere: Focus+Context Sphere Visualization for Interactive Large Graph Exploration
Authors: Fan Du, Nan Cao 0001, Yu-Ru Lin, Panpan Xu, Hanghang Tong
Published at CHI, 2017

Interactive exploration plays a critical role in large graph visualization. Existing techniques, such as zoom-and-pan on a 2D plane and hyperbolic browser facilitate large graph exploration by showing both the details of a focal area and its surrounding context that guides the exploration process. However, existing techniques for large graph exploration are limited in either providing too little context or presenting graphs with too much distortion. In this paper, we propose a novel focus+context technique, iSphere, to address the limitation. iSphere maps a large graph onto a Riemann Sphere that better preserves graph structures and shows greater context information. We conduct extensive experiment studies on different graph exploration tasks under various conditions. The results show that iSphere performs the best in task completion time compared to the baseline techniques in link and path exploration tasks. This research also contributes to understanding large graph exploration on small screens.
Live Physiological Sensing and Visualization Ecosystems: An Activity Theory Analysis
Authors: Tamara L. Clegg, Leyla Norooz, Seokbin Kang, Virginia Byrne, Monica Katzen, Rafael Valez, Angelisa C. Plane, Vanessa Oguamanam, Thomas Outing, Jason C. Yip 0001, Elizabeth M. Bonsignore, Jon Froehlich
Published at CHI, 2017

Wearable sensing poses new opportunities to enhance personal connections to learning and authentic scientific inquiry experiences. In our work, we leverage the body and physical action as an engaging platform for learning through live physiological sensing and visualization (LPSV). Prior research suggests the potential of this approach, but was limited to single-session evaluations in informal environments. In this paper, we examine LPSV tools in a classroom environment during a four-day deployment. To highlight the complex interconnections between space, teachers, curriculum, and tool use, we analyze our data through the lens of Activity Theory. Our findings show the importance of integrating model-based representations for supporting exploration and analytic representations for scaffolding scientific inquiry. Activity Theory highlights leveraging life-relevant connections available within a physical space and considering policies and norms related to learners' physical bodies.
Narratives in Crowdsourced Evaluation of Visualizations: A Double-Edged Sword?
Authors: Evanthia Dimara, Anastasia Bezerianos, Pierre Dragicevic
Published at CHI, 2017

We explore the effects of providing task context when evaluating visualization tools using crowdsourcing. We gave crowdsource workers i) abstract information visualization tasks without any context, ii) tasks where we added semantics to the dataset, and iii) tasks with two types of backstory narratives: an analytic narrative and a decision-making narrative. Contrary to our expectations, we did not find evidence that adding data semantics increases accuracy, and further found that our backstory narratives can even decrease accuracy. Adding dataset semantics can however increase attention and provide subjective benefits in terms of confidence, perceived easiness, task enjoyability and perceived usefulness of the visualization. Nevertheless, our backstory narratives did not appear to provide additional subjective benefits. These preliminary findings suggest that narratives may have complex and unanticipated effects, calling for more studies in this area.
PathViewer: Visualizing Pathways through Student Data
Authors: Yiting Wang, Walker M. White, Erik Andersen 0001
Published at CHI, 2017

Analysis of student data is critical for improving education. In particular, educators need to understand what approaches their students are taking to solve a problem. However, identifying student strategies and discovering areas of confusion is difficult because an educator may not know what queries to ask or what patterns to look for in the data. In this paper, we present a visualization tool, PathViewer, to model the paths that students follow when solving a problem. PathViewer leverages ideas from flow diagrams and natural language processing to visualize the sequences of intermediate steps that students take. Using PathViewer, we analyzed how several students solved a Python assignment, discovering interesting and unexpected patterns. Our results suggest that PathViewer can allow educators to quickly identify areas of interest, drill down into specific areas, and identify student approaches to the problem as well as misconceptions they may have.
PersaLog: Personalization of News Article Content
Authors: Eytan Adar, Carolyn Gearig, Ayshwarya Balasubramanian, Jessica Hullman
Published at CHI, 2017

Content personalization automatically modifying text and multimedia features within articles based on the reader's individual features'is evolving as a new form of journalism. Informed by constraints articulated through a survey of journalists, we have implemented PersaLog, a novel system for creating personalized content (e.g., text and interactive visualizations). Because crafting, and validating, personalized content can be challenging to scale across articles (unlike feed personalization), we offer a simple Domain Specific Language (DSL), and editing environment, to support this task. PersaLog is particularly designed to support the personalization of existing text and visualizations. Our work provides guidelines for personalization as well as a system that allows for both subtle and dramatic personalization-driven content changes. We validate PersaLog using case and lab studies.
Pressure-Based Gain Factor Control for Mobile 3D Interaction using Locally-Coupled Devices
Authors: Lonni Besançon, Mehdi Ammi, Tobias Isenberg 0001
Published at CHI, 2017

We present the design and evaluation of pressure-based interactive control of 3D navigation precision. Specifically, we examine the control of gain factors in tangible 3D interactions using locally-coupled mobile devices. By focusing on pressure as a separate input channel we can adjust gain factors independently from other input modalities used in 3D navigation, in particular for the exploration of 3D visualisations. We present two experiments. First, we determined that people strongly preferred higher pressures to be mapped to higher gain factors. Using this mapping, we compared pressure with rate control, velocity control, and slider-based control in a second study. Our results show that pressure-based gain control allows people to be more precise in the same amount of time compared to established input modalities. Pressure-based control was also clearly preferred by our participants. In summary, we demonstrate that pressure facilitates effective and efficient precision control for mobile 3D navigation.
Regression by Eye: Estimating Trends in Bivariate Visualizations
Authors: Michael Correll, Jeffrey Heer
Published at CHI, 2017

Observing trends and predicting future values are common tasks for viewers of bivariate data visualizations. As many charts do not explicitly include trend lines or related statistical summaries, viewers often visually estimate trends directly from a plot. How reliable are the inferences viewers draw when performing such regression by eye? Do particular visualization designs or data features bias trend perception? We present a series of crowdsourced experiments that assess the accuracy of trends estimated using regression by eye across a variety of bivariate visualizations, and examine potential sources of bias in these estimations. We find that viewers accurately estimate trends in many standard visualizations of bivariate data, but that both visual features (e.g., "within-the-bar" bias) and data features (e.g., the presence of outliers) can result in visual estimates that systematically diverge from standard least-squares regression models.
Scalable Annotation of Fine-Grained Categories Without Experts
Authors: Timnit Gebru, Jonathan Krause, Jia Deng 0001, Li Fei-Fei 0001
Published at CHI, 2017

We present a crowdsourcing workflow to collect image annotations for visually similar synthetic categories without requiring experts. In animals, there is a direct link between taxonomy and visual similarity: e.g. a collie (type of dog) looks more similar to other collies (e.g. smooth collie) than a greyhound (another type of dog). However, in synthetic categories such as cars, objects with similar taxonomy can have very different appearance: e.g. a 2011 Ford F-150 Supercrew-HD looks the same as a 2011 Ford F-150 Supercrew-LL but very different from a 2011 Ford F-150 Supercrew-SVT. We introduce a graph based crowdsourcing algorithm to automatically group visually indistinguishable objects together. Using our workflow, we label 712,430 images by ~1,000 Amazon Mechanical Turk workers; resulting in the largest fine-grained visual dataset reported to date with 2,657 categories of cars annotated at 1/20th the cost of hiring experts.
Showing People Behind Data: Does Anthropomorphizing Visualizations Elicit More Empathy for Human Rights Data?
Authors: Jeremy Boy, Anshul Vikram Pandey, John Emerson, Margaret Satterthwaite, Oded Nov, Enrico Bertini
Published at CHI, 2017

We investigate the impact of using anthropomorphized data graphics over standard charts on viewers' empathy for, and prosocial behavior toward suffering populations, in the context of human rights narratives. We present a series of experiments conducted on Amazon Mechanical Turk, in which we compare various forms of anthropomorphized data graphics-ranging from a single human figure that "fills up" to show proportional data, to separated groups of individual human beings-with a standard chart baseline. Each experiment uses two carefully crafted human rights data-driven stories to present the graphics. Contrary to our expectations, we consistently find that anthropomorphized data graphics and standard charts have very similar effects on empathy and prosocial behavior.
Supporting Community Health Workers in India through Voice- and Web-Based Feedback
Authors: Brian DeRenzi, Nicola Dell, Jeremy Wacksman, Scott Lee, Neal Lesh
Published at CHI, 2017

Our research aims to support community health workers (CHWs) in low-resource settings by providing them with personalized information regarding their work. This information is delivered through a combination of voice- and web-based feedback that is derived from data already collected by CHWs. We describe the in situ participatory design approach used to create usable and appropriate feedback for low-literate CHWs and present usage data from a 12-month study with 71 CHWs in India. We show how the system supported and motivated CHWs, and how they used both the web- and voice-based systems, and each of the visualizations, for different reasons. We also show that the comparative feedback provided by the system introduced elements of competition that discouraged some CHWs while motivating others. Taken together, our findings suggest that providing personalized voice- and web-based feedback could be an effective way to support and motivate CHWs in low-resource settings.
Supporting Making Fixations and the Effect on Gaze Gesture Performance
Authors: Howell O. Istance, Aulikki I. Hyrskykari
Published at CHI, 2017

Gaze gestures are deliberate patterns of eye movements that can be used to invoke commands. These are less reliant on accurate measurement and calibration than other gaze-based interaction techniques. These may be used with wearable displays fitted with eye tracking capability, or as part of an assistive technology. The visual stimuli in the information on the display that can act as fixation targets may or may not be sparse and will vary over time. The paper describes an experiment to investigate how the amount of information provided on a display to assist making fixations affects gaze gesture performance. The impact of providing visualization guides and small fixation targets on the time to complete gestures and error rates is presented. The number and durations of fixations made during gesture completion is used to explain differences in performance as a result of practice and direction of eye movement.
The Catch(es) with Smart Home: Experiences of a Living Lab Field Study
Authors: Timo Jakobi, Corinna Ogonowski, Nico Castelli, Gunnar Stevens, Volker Wulf
Published at CHI, 2017

Smart home systems are becoming an integral feature of the emerging home IT market. Under this general term, products mainly address issues of security, energy savings and comfort. Comprehensive systems that cover several use cases are typically operated and managed via a unified dashboard. Unfortunately, research targeting user experience (UX) design for smart home interaction that spans several use cases or covering the entire system is scarce. Furthermore, existing comprehensive and user-centered longterm studies on challenges and needs throughout phases of information collection, installation and operation of smart home systems are technologically outdated. Our 18-month Living Lab study covering 14 households equipped with smart home technology provides insights on how to design for improving smart home appropriation. This includes a stronger sensibility for household practices during setup and configuration, flexible visualizations for evolving demands and an extension of smart home beyond the location.
TouchPivot: Blending WIMP & Post-WIMP Interfaces for Data Exploration on Tablet Devices
Authors: Jaemin Jo, Sehi L'Yi, Bongshin Lee, Jinwook Seo
Published at CHI, 2017

Recent advancements in tablet technology pose a great opportunity for information visualization to expand its horizons beyond desktops. In this paper, we present TouchPivot, a novel interface that assists visual data exploration on tablet devices. With novices in mind, TouchPivot supports data transformations, such as pivoting and filtering, with simple pen and touch interactions, and facilitates understanding of the transformations through tight coupling between a data table and visualization. We bring in WIMP interfaces to TouchPivot, leveraging their familiarity and accessibility to novices. We report on a user study conducted to compare TouchPivot with two commercial interfaces, Tableau and Microsoft Excel's PivotTable. Our results show that novices not only answered data-driven questions faster, but also created a larger number of meaningful charts during freeform exploration with TouchPivot than others. Finally, we discuss the main hurdles novices encountered during our study and possible remedies for them.
Trust, but Verify: Optimistic Visualizations of Approximate Queries for Exploring Big Data
Authors: Dominik Moritz, Danyel Fisher, Bolin Ding, Chi Wang 0001
Published at CHI, 2017

Analysts need interactive speed for exploratory analysis, but big data systems are often slow. With sampling, data systems can produce approximate answers fast enough for exploratory visualization, at the cost of accuracy and trust. We propose optimistic visualization, which approaches these issues from a user experience perspective. This method lets analysts explore approximate results interactively, and provides a way to detect and recover from errors later. Pangloss implements these ideas. We discuss design issues raised by optimistic visualization systems. We test this concept with five expert visualizers in a laboratory study and three case studies at Microsoft. Analysts reported that they felt more confident in their results, and used optimistic visualization to check that their preliminary results were correct.
Understanding Concept Maps: A Closer Look at How People Organise Ideas
Authors: Stefano Padilla, Thomas S. Methven, David A. Robb 0001, Mike J. Chantler
Published at CHI, 2017

Research into creating visualisations that organise ideas into concise concept maps often focuses on implicit mathematical and statistical theories which are built around algorithmic efficacy or visual complexity. Although there are multiple techniques which attempt to mathematically optimise this multi-dimensional problem, it is still unknown how to create concept maps that are immediately understandable to people. In this paper, we present an in-depth qualitative study observing the behaviour and discussing the strategy used by non-expert participants to create, interact, update and communicate a concept map that represents a collection of research ideas. Our results show non-expert individuals create concept maps differently to visualisation algorithms. We found that our participants prioritised narrative, landmarks, abstraction, clarity, and simplicity. Finally, we derive design recommendations from our results which we hope will inspire future algorithms that automatically create more usable and compelling concept maps better suited to the natural behaviours and needs of users.
Visualization Literacy at Elementary School
Authors: Basak Alper, Nathalie Henry Riche, Fanny Chevalier, Jeremy Boy, T. Metin Sezgin
Published at CHI, 2017

This work advances our understanding of children's visualization literacy, and aims to improve it through a novel approach for teaching visualization at elementary school. We first contribute an analysis of data graphics and activities employed in grade K to 4 educational materials, and the results of a survey conducted with 16 elementary school teachers. We find that visualization education could benefit from integrating pedagogical strategies for teaching abstract concepts with established interactive visualization techniques. Building on these insights, we develop and study design principles for novel interactive teaching material aimed at increasing children's visualization literacy. We specifically contribute C'est La Vis, an online platform for teachers and students to respectively teach and learn about pictographs and bar charts, and report on our initial observations of its use in grades K and 2.
Voyager 2: Augmenting Visual Analysis with Partial View Specifications
Authors: Kanit Wongsuphasawat, Zening Qu, Dominik Moritz, Riley Chang, Felix Ouk, Anushka Anand, Jock D. Mackinlay, Bill Howe, Jeffrey Heer
Published at CHI, 2017

Visual data analysis involves both open-ended and focused exploration. Manual chart specification tools support question answering, but are often tedious for early-stage exploration where systematic data coverage is needed. Visualization recommenders can encourage broad coverage, but irrelevant suggestions may distract users once they commit to specific questions. We present Voyager 2, a mixed-initiative system that blends manual and automated chart specification to help analysts engage in both open-ended exploration and targeted question answering. We contribute two partial specification interfaces: wildcards let users specify multiple charts in parallel, while related views suggest visualizations relevant to the currently specified chart. We present our interface design and applications of the CompassQL visualization query language to enable these interfaces. In a controlled study we find that Voyager 2 leads to increased data field coverage compared to a traditional specification tool, while still allowing analysts to flexibly drill-down and answer specific questions.