@article {DOWLING201949, title = {Interactive Visual Analytics for Sensemaking with Big Text}, journal = {Big Data Research}, volume = {16}, year = {2019}, month = {July/2019}, pages = {49 - 58}, abstract = {Analysts face many steep challenges when performing sensemaking tasks on collections of textual information larger than can be reasonably analyzed without computational assistance. To scale up such sensemaking tasks, new methods are needed to interactively integrate human cognitive sensemaking activity with machine learning. Towards that goal, we offer a human-in-the-loop computational model that mirrors the human sensemaking process, and consists of foraging and synthesis sub-processes. We model the synthesis loop as an interactive spatial projection and the foraging loop as an interactive relevance ranking combined with topic modeling. We combine these two components of the sensemaking process using semantic interaction such that the human{\textquoteright}s spatial synthesis actions are transformed into automated foraging and synthesis of new relevant information. Ultimately, the model{\textquoteright}s ability to forage as a result of the analyst{\textquoteright}s synthesis activities makes interacting with big text data easier and more efficient, thereby facilitating analysts{\textquoteright} sensemaking ability. We discuss the interaction design and theory behind our interactive sensemaking model. The model is embodied in a novel visual analytics prototype called Cosmos in which analysts synthesize structure within the larger corpus by directly interacting with a reduced-dimensionality space to express relationships on a subset of data. We then demonstrate how Cosmos supports sensemaking tasks with a realistic scenario that investigates the affect of natural disasters in Adelaide, Australia in September 2016 using a database of over 30,000 news articles.}, keywords = {Big data, interactive visual analytics, Semantic interaction, text analytics, Topic modeling, visualization}, issn = {2214-5796}, doi = {https://doi.org/10.1016/j.bdr.2019.04.003}, url = {http://www.sciencedirect.com/science/article/pii/S2214579618302995}, author = {Michelle Dowling and Nathan Wycoff and Brian Mayer and Wenskovitch, John and Leman, Scotland and House, Leanna and Nicholas Polys and North, Chris and Peter Hauck} } @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} }