Be the Data: Embodied Visual Analytics

Xin Chen, Jessica Zeitz Self, Leanna L. House, John Wenskovitch, Maoyuan Sun, Nathan Wycoff, Scotland C. Leman, Chris North

Abstract

With the rise of big data, it is becoming increasingly important to educate groups of students at many educational levels about data analytics. In particular, students without a strong mathematical background may have an unenthusiastic attitude towards high-dimensional data and find it challenging to understand relevant complex analytical methods, such as dimension reduction. In this paper, we present an embodied approach for visual analytics designed to teach students about exploring alternative 2D projections of high-dimensional data points using weighted multidimensional scaling. We propose a novel concept, Be the Data, to explore the possibilities of using human's embodied resources to learn from high-dimensional data. In our implemented system, each student embodies a data point, and the position of students in a physical space represents a 2D projection of the high-dimensional data. Students physically move within the room with respect to each other to collaboratively construct alternative projections and receive visual feedback about relevant data dimensions. In this way, students can pose hypotheses about the data to discover the statistical support as well as learn about complex concepts such as high-dimensional distance. We conducted educational workshops with students in various age groups inexperienced in complex data analytical methods. Our findings indicate that Be the Data provided the necessary engagement to enable students to quickly learn about high-dimensional data and analysis processes despite their minimal prior knowledge.