@article {DOI269, title = {Be the Data: Embodied Visual Analytics}, journal = {IEEE Transactions on Learning Technologies}, volume = {11}, year = {2018}, pages = {81-95}, doi = {10.1109/TLT.2017.2757481}, author = {Xin Chen and Self, Jessica Zeitz and House, Leanna and Wenskovitch, John and Sun, Maoyuan and Nathan Wycoff and Jane Robertson Evia and Leman, Scotland and North, Chris} } @article {self2018observation, title = {Observation-Level and Parametric Interaction for High-Dimensional Data Analysis}, journal = {ACM Transactions on Interactive Intelligent Systems}, volume = {8}, year = {2018}, month = {07/2018}, doi = {10.1145/3158230}, author = {Self, Jessica Zeitz and Michelle Dowling and Wenskovitch, John and Ian Crandell and Ming Wang and House, Leanna and Leman, Scotland and North, Chris} } @conference {DOI265, title = {Bringing Interactive Visual Analytics to the Classroom for Developing EDA Skills}, booktitle = {Proceedings of the 33rd Annual Consortium of Computing Sciences in Colleges (CCSC) Eastern Regional Conference}, year = {2017}, month = {10/2017}, pages = {10}, author = {Self, Jessica Zeitz and Self, Nathan and House, Leanna and Jane Robertson Evia and Leman, Scotland and North, Chris} } @conference {DOI251, title = {Be the Data: A New Approach for Immersive Analytics}, booktitle = {IEEE Virtual Reality 2016 Workshop on Immersive Analytics}, year = {2016}, month = {03/2016}, pages = {6}, author = {Xin Chen and Self, Jessica Zeitz and House, Leanna and North, Chris} } @conference {DOI245, title = {Be the Data: An Embodied Experience for Data Analytics}, booktitle = { 2016 Annual Meeting of the American Educational Research Association (AERA)}, year = {2016}, month = {04/2016}, pages = {20}, author = {Xin Chen and House, Leanna and Self, Jessica Zeitz and Leman, Scotland and Jane Robertson Evia and James Thomas Fry and North, Chris} } @conference {DOI248, title = {Be the Data: Social Meetings with Visual Analytics}, booktitle = {International Workshop on Visualization and Collaboration (VisualCol 2016)}, year = {2016}, month = {11/2016}, pages = {8}, author = {Xin Chen and Self, Jessica Zeitz and Sun, Maoyuan and House, Leanna and North, Chris} } @conference {DOI249, title = {Bridging the Gap between User Intention and Model Parameters for Data Analytics}, booktitle = {SIGMOD 2016 Workshop on Human-In-the-Loop Data Analytics (HILDA 2016)}, year = {2016}, month = {06/2016}, pages = {6}, author = {Self, Jessica Zeitz and Vinayagam, R.K. and James Thomas Fry and North, Chris} } @conference {DOI250, title = {Designing Usable Interactive Visual Analytics Tools for Dimension Reduction}, booktitle = {CHI 2016 Workshop on Human-Centered Machine Learning (HCML)}, year = {2016}, month = {05/2016}, pages = {7}, author = {Self, Jessica Zeitz and Hu, Xinran and House, Leanna and Leman, Scotland and North, Chris} } @article {DOI235, title = {Andromeda: Observation-Level and Parametric Interaction for Exploratory Data Analysis}, year = {2015}, institution = {Virginia Tech}, type = {Technical Report}, address = {Blacksburg}, abstract = {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.}, author = {Self, Jessica Zeitz and House, Leanna and Leman, Scotland and North, Chris} } @article {DOI244, title = {Bringing Interactive Visual Analytics to the Classroom for Developing EDA Skills}, year = {2015}, institution = {Virginia Tech}, type = {Technical Report}, address = {Blacksburg}, abstract = {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{\textquoteright} capacity for making dimensionally complex insights from data. Using this concept, we build a vocabulary and methodology to support a student{\textquoteright}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{\textquoteright} CD begin low and improve.}, keywords = {dimension reduction, education, multidimensional scaling, multivariate analysis, Visual Analytics}, author = {Self, Jessica Zeitz and Self, Nathan and House, Leanna and Jane Robertson Evia and Leman, Scotland and North, Chris} } @article {DOI241, title = {Designing for Interactive Dimension Reduction Visual Analytics Tools to Explore High-Dimensional Data}, year = {2015}, institution = {Virginia Tech}, type = {Technical Report}, address = {Blacksburg}, abstract = {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.}, author = {Self, Jessica Zeitz and Hu, Xinran and House, Leanna and Leman, Scotland and North, Chris} } @article {DOI228, title = {Improving Students{\textquoteright} Cognitive Dimensionality through Education with Object-Level Interaction}, year = {2014}, institution = {Virginia Tech}, type = {Technical Report}, address = {Blacksburg}, abstract = {This paper addresses the use of visual analytics techniques in education to advance students{\textquoteright} 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{\textquoteright} 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{\textquoteright} capacity for dimensionally complex insights. Using this concept, we build a vocabulary and methodology to support a student{\textquoteright}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{\textquoteright} cognitive dimensionality can be improved and further research on the impact of visual analytics tools on education for cognitive dimensionality is warranted.}, keywords = {multivariate data analysis, object level interaction, Visual Analytics}, author = {Self, Jessica Zeitz and Self, Nathan and House, Leanna and Leman, Scotland and North, Chris} } @article {DOI10.1007/s00779-013-0727-2, title = {VisPorter: facilitating information sharing for collaborative sensemaking on multiple displays}, journal = {Personal and Ubiquitous Computing}, volume = {18}, year = {2014}, month = {6/2014}, pages = {1169{\textendash}1186}, publisher = {Springer London}, keywords = {collaborative sensemaking, Display ecology, multiple displays, text analytics, Visual Analytics}, issn = {1617-4909}, doi = {10.1007/s00779-013-0727-2}, url = {http://dx.doi.org/10.1007/s00779-013-0727-2}, author = {Chung, Haeyong and North, Chris and Self, Jessica Zeitz and Chu, Sharon and Francis Quek} } @conference {DOI10.1109/ISI.2013.6578831, title = {Auto-Highlighter: Identifying Salient Sentences in Text}, booktitle = {2013 IEEE International Conference on Intelligence and Security Informatics (ISI)}, year = {2013}, month = {6/2013}, pages = {260 - 262}, publisher = {IEEE}, organization = {IEEE}, address = {Seattle, WA, USA}, isbn = {978-1-4673-6214-6}, doi = {10.1109/ISI.2013.6578831}, author = {Self, Jessica Zeitz and Zeitz, Rebecca and North, Chris and Breitler, Alan L.} } @conference {DOI10.1109/ISI.2013.6578780, title = {How analysts cognitively {\textquotedblleft}connect the dots{\textquotedblright}}, booktitle = {2013 IEEE International Conference on Intelligence and Security Informatics (ISI)}, year = {2013}, month = {6/2013}, pages = {24 - 26}, publisher = {IEEE}, organization = {IEEE}, address = {Seattle, WA, USA}, isbn = {978-1-4673-6214-6}, doi = {10.1109/ISI.2013.6578780}, author = {Lauren Bradel and Self, Jessica Zeitz and Endert, Alex and Hossain, M. Shahriar and North, Chris and Ramakrishnan, Naren} } @conference {DOI10.1109/VAST.2012.6400512, title = {Pixel-oriented Treemap for multiple displays}, booktitle = {VAST Challenge 2012 IEEE Conference on Visual Analytics Science and Technology (VAST)}, year = {2012}, pages = {289 - 290}, publisher = {IEEE}, organization = {IEEE}, address = {Seattle, WA, USA}, abstract = {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.}, keywords = {large display, multiple displays, physical navigation, pixel-oriented visualization, treemap}, isbn = {978-1-4673-4752-5}, doi = {10.1109/VAST.2012.6400512}, author = {Chung, Haeyong and Cho, Yong Ju and Self, Jessica Zeitz and North, Chris} }