%0 Journal Article %J IEEE Transactions on Learning Technologies %D 2018 %T Be the Data: Embodied Visual Analytics %A Xin Chen %A Self, Jessica Zeitz %A House, Leanna %A Wenskovitch, John %A Sun, Maoyuan %A Nathan Wycoff %A Jane Robertson Evia %A Leman, Scotland %A North, Chris %B IEEE Transactions on Learning Technologies %V 11 %P 81-95 %N 1 %R 10.1109/TLT.2017.2757481 %0 Journal Article %J ACM Transactions on Interactive Intelligent Systems %D 2018 %T Observation-Level and Parametric Interaction for High-Dimensional Data Analysis %A Self, Jessica Zeitz %A Michelle Dowling %A Wenskovitch, John %A Ian Crandell %A Ming Wang %A House, Leanna %A Leman, Scotland %A North, Chris %B ACM Transactions on Interactive Intelligent Systems %V 8 %8 07/2018 %N 2 %R 10.1145/3158230 %0 Conference Paper %B Proceedings of the 33rd Annual Consortium of Computing Sciences in Colleges (CCSC) Eastern Regional Conference %D 2017 %T Bringing Interactive Visual Analytics to the Classroom for Developing EDA Skills %A Self, Jessica Zeitz %A Self, Nathan %A House, Leanna %A Jane Robertson Evia %A Leman, Scotland %A North, Chris %B Proceedings of the 33rd Annual Consortium of Computing Sciences in Colleges (CCSC) Eastern Regional Conference %P 10 %8 10/2017 %0 Conference Paper %B IEEE Virtual Reality 2016 Workshop on Immersive Analytics %D 2016 %T Be the Data: A New Approach for Immersive Analytics %A Xin Chen %A Self, Jessica Zeitz %A House, Leanna %A North, Chris %B IEEE Virtual Reality 2016 Workshop on Immersive Analytics %P 6 %8 03/2016 %0 Conference Paper %B 2016 Annual Meeting of the American Educational Research Association (AERA) %D 2016 %T Be the Data: An Embodied Experience for Data Analytics %A Xin Chen %A House, Leanna %A Self, Jessica Zeitz %A Leman, Scotland %A Jane Robertson Evia %A James Thomas Fry %A North, Chris %B 2016 Annual Meeting of the American Educational Research Association (AERA) %P 20 %8 04/2016 %0 Conference Paper %B International Workshop on Visualization and Collaboration (VisualCol 2016) %D 2016 %T Be the Data: Social Meetings with Visual Analytics %A Xin Chen %A Self, Jessica Zeitz %A Sun, Maoyuan %A House, Leanna %A North, Chris %B International Workshop on Visualization and Collaboration (VisualCol 2016) %P 8 %8 11/2016 %0 Conference Paper %B SIGMOD 2016 Workshop on Human-In-the-Loop Data Analytics (HILDA 2016) %D 2016 %T Bridging the Gap between User Intention and Model Parameters for Data Analytics %A Self, Jessica Zeitz %A Vinayagam, R.K. %A James Thomas Fry %A North, Chris %B SIGMOD 2016 Workshop on Human-In-the-Loop Data Analytics (HILDA 2016) %P 6 %8 06/2016 %0 Conference Paper %B CHI 2016 Workshop on Human-Centered Machine Learning (HCML) %D 2016 %T Designing Usable Interactive Visual Analytics Tools for Dimension Reduction %A Self, Jessica Zeitz %A Hu, Xinran %A House, Leanna %A Leman, Scotland %A North, Chris %B CHI 2016 Workshop on Human-Centered Machine Learning (HCML) %P 7 %8 05/2016 %0 Report %D 2015 %T Andromeda: Observation-Level and Parametric Interaction for Exploratory Data Analysis %A Self, Jessica Zeitz %A House, Leanna %A Leman, Scotland %A North, Chris %X 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. %I Virginia Tech %C Blacksburg %9 Technical Report %0 Report %D 2015 %T Bringing Interactive Visual Analytics to the Classroom for Developing EDA Skills %A Self, Jessica Zeitz %A Self, Nathan %A House, Leanna %A Jane Robertson Evia %A Leman, Scotland %A North, Chris %K dimension reduction %K education %K multidimensional scaling %K multivariate analysis %K Visual Analytics %X 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. %I Virginia Tech %C Blacksburg %9 Technical Report %0 Report %D 2015 %T Designing for Interactive Dimension Reduction Visual Analytics Tools to Explore High-Dimensional Data %A Self, Jessica Zeitz %A Hu, Xinran %A House, Leanna %A Leman, Scotland %A North, Chris %X 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. %I Virginia Tech %C Blacksburg %9 Technical Report %0 Report %D 2014 %T Improving Students' Cognitive Dimensionality through Education with Object-Level Interaction %A Self, Jessica Zeitz %A Self, Nathan %A House, Leanna %A Leman, Scotland %A North, Chris %K multivariate data analysis %K object level interaction %K Visual Analytics %X 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. %I Virginia Tech %C Blacksburg %9 Technical Report %0 Journal Article %J Personal and Ubiquitous Computing %D 2014 %T VisPorter: facilitating information sharing for collaborative sensemaking on multiple displays %A Chung, Haeyong %A North, Chris %A Self, Jessica Zeitz %A Chu, Sharon %A Francis Quek %K collaborative sensemaking %K Display ecology %K multiple displays %K text analytics %K Visual Analytics %B Personal and Ubiquitous Computing %I Springer London %V 18 %P 1169–1186 %8 6/2014 %U http://dx.doi.org/10.1007/s00779-013-0727-2 %N 5 %R 10.1007/s00779-013-0727-2 %0 Conference Paper %B 2013 IEEE International Conference on Intelligence and Security Informatics (ISI) %D 2013 %T Auto-Highlighter: Identifying Salient Sentences in Text %A Self, Jessica Zeitz %A Zeitz, Rebecca %A North, Chris %A Breitler, Alan L. %B 2013 IEEE International Conference on Intelligence and Security Informatics (ISI) %I IEEE %C Seattle, WA, USA %P 260 - 262 %8 6/2013 %@ 978-1-4673-6214-6 %R 10.1109/ISI.2013.6578831 %0 Conference Paper %B 2013 IEEE International Conference on Intelligence and Security Informatics (ISI) %D 2013 %T How analysts cognitively “connect the dots” %A Lauren Bradel %A Self, Jessica Zeitz %A Endert, Alex %A Hossain, M. Shahriar %A North, Chris %A Ramakrishnan, Naren %B 2013 IEEE International Conference on Intelligence and Security Informatics (ISI) %I IEEE %C Seattle, WA, USA %P 24 - 26 %8 6/2013 %@ 978-1-4673-6214-6 %R 10.1109/ISI.2013.6578780 %0 Conference Paper %B VAST Challenge 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) %D 2012 %T Pixel-oriented Treemap for multiple displays %A Chung, Haeyong %A Cho, Yong Ju %A Self, Jessica Zeitz %A North, Chris %K large display %K multiple displays %K physical navigation %K pixel-oriented visualization %K treemap %X 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. %B VAST Challenge 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) %I IEEE %C Seattle, WA, USA %P 289 - 290 %@ 978-1-4673-4752-5 %R 10.1109/VAST.2012.6400512