How Students Approach Visualizations From Large Professionally Collected Data Sets: An Eye-Tracking Study

• In Event: Excellence in Education Research: Early Career Scholars and Their Work

Abstract

Understanding large, professionally collected data sets is important across the STEM (science, technology, engineering, and mathematics) disciplines (Manduca & Mogk, 2002). For example, large professionally collected data sets are used to understand and predict animal migration, weather, natural resource location and renewal, etc. Learning how to understand and analyze “big data” has been identified as an important skill in K-12 and undergraduate education, and is the objective for the major STEM initiative developed by the Education Development Center called the Oceans of Data Institute (Krumhansl, 2013). However, being able to visualize and interpret such data sets can be difficult for novices (e.g, Krumhansl, 2013; Phipps & Rowe, 2010), especially when the data or observations represent a 3D volume (Kali & Orion, 1996; Kastens, et al., 2009; Piburn, et al., 2005).

The current study aims to examine individual differences in strategy and ability that may contribute to understanding the kind of data visualizations that scientists use. Looking strategies (via eye-tracking) and mental models are identified. “Data savviness”, or how well the participant understands the data, is assessed. Given the spatial nature of the task, spatial ability is examined. It is a pedagogical challenge to scaffold students’ data exploration without giving them step-by-step instructions. One promising approach, investigated in this study, is to provide students with an array of candidate hypotheses which they can potentially use to organize their data exploration, much as multiple working hypotheses can help direct an expert’s exploration of new data.

Author

• Ilyse Resnick, University of Delaware