@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} }