TY - JOUR T1 - Be the Data: Embodied Visual Analytics JF - IEEE Transactions on Learning Technologies Y1 - 2018 A1 - Xin Chen A1 - Self, Jessica Zeitz A1 - House, Leanna A1 - Wenskovitch, John A1 - Sun, Maoyuan A1 - Nathan Wycoff A1 - Jane Robertson Evia A1 - Leman, Scotland A1 - North, Chris VL - 11 IS - 1 ER - TY - JOUR T1 - Observation-Level and Parametric Interaction for High-Dimensional Data Analysis JF - ACM Transactions on Interactive Intelligent Systems Y1 - 2018 A1 - Self, Jessica Zeitz A1 - Michelle Dowling A1 - Wenskovitch, John A1 - Ian Crandell A1 - Ming Wang A1 - House, Leanna A1 - Leman, Scotland A1 - North, Chris VL - 8 IS - 2 ER - TY - CONF T1 - Bringing Interactive Visual Analytics to the Classroom for Developing EDA Skills T2 - Proceedings of the 33rd Annual Consortium of Computing Sciences in Colleges (CCSC) Eastern Regional Conference Y1 - 2017 A1 - Self, Jessica Zeitz A1 - Self, Nathan A1 - House, Leanna A1 - Jane Robertson Evia A1 - Leman, Scotland A1 - North, Chris JF - Proceedings of the 33rd Annual Consortium of Computing Sciences in Colleges (CCSC) Eastern Regional Conference ER - TY - CONF T1 - Be the Data: A New Approach for Immersive Analytics T2 - IEEE Virtual Reality 2016 Workshop on Immersive Analytics Y1 - 2016 A1 - Xin Chen A1 - Self, Jessica Zeitz A1 - House, Leanna A1 - North, Chris JF - IEEE Virtual Reality 2016 Workshop on Immersive Analytics ER - TY - CONF T1 - Be the Data: An Embodied Experience for Data Analytics T2 - 2016 Annual Meeting of the American Educational Research Association (AERA) Y1 - 2016 A1 - Xin Chen A1 - House, Leanna A1 - Self, Jessica Zeitz A1 - Leman, Scotland A1 - Jane Robertson Evia A1 - James Thomas Fry A1 - North, Chris JF - 2016 Annual Meeting of the American Educational Research Association (AERA) ER - TY - CONF T1 - Be the Data: Social Meetings with Visual Analytics T2 - International Workshop on Visualization and Collaboration (VisualCol 2016) Y1 - 2016 A1 - Xin Chen A1 - Self, Jessica Zeitz A1 - Sun, Maoyuan A1 - House, Leanna A1 - North, Chris JF - International Workshop on Visualization and Collaboration (VisualCol 2016) ER - TY - CONF T1 - Bridging the Gap between User Intention and Model Parameters for Data Analytics T2 - SIGMOD 2016 Workshop on Human-In-the-Loop Data Analytics (HILDA 2016) Y1 - 2016 A1 - Self, Jessica Zeitz A1 - Vinayagam, R.K. A1 - James Thomas Fry A1 - North, Chris JF - SIGMOD 2016 Workshop on Human-In-the-Loop Data Analytics (HILDA 2016) ER - TY - CONF T1 - Designing Usable Interactive Visual Analytics Tools for Dimension Reduction T2 - CHI 2016 Workshop on Human-Centered Machine Learning (HCML) Y1 - 2016 A1 - Self, Jessica Zeitz A1 - Hu, Xinran A1 - House, Leanna A1 - Leman, Scotland A1 - North, Chris JF - CHI 2016 Workshop on Human-Centered Machine Learning (HCML) ER - TY - RPRT T1 - Andromeda: Observation-Level and Parametric Interaction for Exploratory Data Analysis Y1 - 2015 A1 - Self, Jessica Zeitz A1 - House, Leanna A1 - Leman, Scotland A1 - North, Chris AB - Exploring high-dimensional number of dimensions in datasets increases, it becomes harder to discover patterns and develop insights. Dimension reduction algorithms, such as multidimensional scaling, support data explorations by reducing datasets to two dimensions for visualization. Because these algorithms rely on underlying parameterizations, they may be tweaked to assess the data from multiple perspectives. Alas, tweaking can be difficult for users without a strong knowledge base of the underlying algorithms. We present Andromeda, an interactive visual analytics tool we developed to enable non-experts of statistical models to explore domain- specific, high-dimensional data. This application implements interactive weighted multidimensional scaling (WMDS) and allows for both parametric and observation- level interaction to provide in-depth data exploration. In this paper, we present the results of a controlled usability study assessing Andromeda. We focus on the comparison of parametric interaction, observation-level interaction and a combination of the two. PB - Virginia Tech CY - Blacksburg ER - TY - RPRT T1 - Bringing Interactive Visual Analytics to the Classroom for Developing EDA Skills Y1 - 2015 A1 - Self, Jessica Zeitz A1 - Self, Nathan A1 - House, Leanna A1 - Jane Robertson Evia A1 - Leman, Scotland A1 - North, Chris KW - dimension reduction KW - education KW - multidimensional scaling KW - multivariate analysis KW - Visual Analytics AB - This paper addresses the use of visual analytics in education for teaching what we call cognitive dimensionality (CD) and other EDA skills. We present the concept of CD to characterize students' capacity for making dimensionally complex insights from data. Using this concept, we build a vocabulary and methodology to support a student’s progression in terms of growth from low cognitive dimensionality (LCD) to high cognitive dimensionality (HCD). Crucially, students do not need high-level math skills to develop HCD. Rather, we use our own tool called Andromeda that enables human-computer interaction with a common, easy to interpret visualization method called Weighted Multidimensional Scaling (WMDS) to promote the idea of making high-dimensional insights. In this paper, we present Andromeda and report findings from a series of classroom assignments to 18 graduate students. These assignments progress from spreadsheet manipulations to statistical software such as R and finally to the use of Andromeda. In parallel with the assignments, we saw students' CD begin low and improve. PB - Virginia Tech CY - Blacksburg ER - TY - RPRT T1 - Designing for Interactive Dimension Reduction Visual Analytics Tools to Explore High-Dimensional Data Y1 - 2015 A1 - Self, Jessica Zeitz A1 - Hu, Xinran A1 - House, Leanna A1 - Leman, Scotland A1 - North, Chris AB - Exploring high-dimensional data is challenging. As the number of dimensions in datasets increases, the harder it becomes to discover patterns and develop insights. Dimension reduction algorithms, such as multidimensional scaling, support data explorations by reducing datasets to two dimensions for visualization. Because these algorithms rely on underlying parameterizations, they may be tweaked to assess the data from multiple perspectives. Alas, tweaking can be difficult for users without a strong knowledge base of the underlying algorithms. In this paper, we present principles for developing interactive visual analytic systems that enable users to tweak model parameters directly or indirectly so that they may explore high-dimensional data. To exemplify our principles, we introduce an application that implements interactive weighted multidimensional scaling (WMDS). Our application, Andromeda, allows for both parametric and object-level interaction to provide in-depth data exploration. In this paper, we describe the types of tasks and insights that users may gain with Andromeda. Also, the final version of Andromeda is the result of sequential improvements made to multiple designs that were critiqued by users. With each critique we uncovered design principles of effective, interactive, visual analytic tools. These design principles focus on three main areas: (1) layout, (2) semantically visualizing parameters, and (3) designing the communication between the interface and the algorithm. PB - Virginia Tech CY - Blacksburg ER - TY - RPRT T1 - Improving Students' Cognitive Dimensionality through Education with Object-Level Interaction Y1 - 2014 A1 - Self, Jessica Zeitz A1 - Self, Nathan A1 - House, Leanna A1 - Leman, Scotland A1 - North, Chris KW - multivariate data analysis KW - object level interaction KW - Visual Analytics AB - This paper addresses the use of visual analytics techniques in education to advance students' cognitive dimensionality. Students naturally tend to characterize data in simplistic one dimensional terms using metrics such as mean, median, mode. Real- world data, however, is more complex and students need to learn to recognize and create high-dimensional arguments. Data exploration methods can encourage thinking beyond traditional one dimensional insights. In particular, visual analytics tools that afford object-level interaction (OLI) allow for generation of more complex insights, despite inexperience with multivariate data. With these tools, students’ insights are of higher complexity in terms of dimensionality and cardinality and built on more diverse interactions. We present the concept of cognitive dimensionality to characterize students' capacity for dimensionally complex insights. Using this concept, we build a vocabulary and methodology to support a student’s progression in terms of growth from low to high cognitive dimensionality. We report findings from a series of classroom assignments with increasingly complex analysis tools. These assignments progressed from spreadsheet manipulations to statistical software such as R and finally to an OLI application, Andromeda. Our findings suggest that students' cognitive dimensionality can be improved and further research on the impact of visual analytics tools on education for cognitive dimensionality is warranted. PB - Virginia Tech CY - Blacksburg ER - TY - JOUR T1 - VisPorter: facilitating information sharing for collaborative sensemaking on multiple displays JF - Personal and Ubiquitous Computing Y1 - 2014 A1 - Chung, Haeyong A1 - North, Chris A1 - Self, Jessica Zeitz A1 - Chu, Sharon A1 - Francis Quek KW - collaborative sensemaking KW - Display ecology KW - multiple displays KW - text analytics KW - Visual Analytics PB - Springer London VL - 18 UR - http://dx.doi.org/10.1007/s00779-013-0727-2 IS - 5 ER - TY - CONF T1 - Auto-Highlighter: Identifying Salient Sentences in Text T2 - 2013 IEEE International Conference on Intelligence and Security Informatics (ISI) Y1 - 2013 A1 - Self, Jessica Zeitz A1 - Zeitz, Rebecca A1 - North, Chris A1 - Breitler, Alan L. JF - 2013 IEEE International Conference on Intelligence and Security Informatics (ISI) PB - IEEE CY - Seattle, WA, USA SN - 978-1-4673-6214-6 ER - TY - CONF T1 - How analysts cognitively “connect the dots” T2 - 2013 IEEE International Conference on Intelligence and Security Informatics (ISI) Y1 - 2013 A1 - Lauren Bradel A1 - Self, Jessica Zeitz A1 - Endert, Alex A1 - Hossain, M. Shahriar A1 - North, Chris A1 - Ramakrishnan, Naren JF - 2013 IEEE International Conference on Intelligence and Security Informatics (ISI) PB - IEEE CY - Seattle, WA, USA SN - 978-1-4673-6214-6 ER - TY - CONF T1 - Pixel-oriented Treemap for multiple displays T2 - VAST Challenge 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) Y1 - 2012 A1 - Chung, Haeyong A1 - Cho, Yong Ju A1 - Self, Jessica Zeitz A1 - North, Chris KW - large display KW - multiple displays KW - physical navigation KW - pixel-oriented visualization KW - treemap AB - We have developed a Pixel-oriented Treemap visualization intended for use on multiple displays with collaborating users. It visualizes the health and status of about a million devices with a Treemap layout. In this paper we describe how we found useful pieces of the VAST 2012 Challenge MC1dataset and discuss how users interacted with this visualization during the analysis. JF - VAST Challenge 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) PB - IEEE CY - Seattle, WA, USA SN - 978-1-4673-4752-5 ER -