TY - CONF T1 - Andromeda in the Classroom: Collaborative Data Analysis for 8th Grade Engineering Design T2 - 2022 ASEE Annual Conference & Exposition Y1 - 2022 A1 - Mia Taylor A1 - Danny Mathieson A1 - House, Leanna A1 - North, Chris JF - 2022 ASEE Annual Conference & Exposition PB - ASEE Conferences CY - Minneapolis, MN N1 - https://peer.asee.org/41168 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 -