Using Interactive Data Visualizations for Exploratory Analysis in Undergraduate Genomics Coursework: Field Study Findings and Guidelines

Abstract

Life scientists increasingly use visual analytics to explore large data sets and generate hypotheses. Undergraduate biology majors should be learning these same methods. Yet visual analytics is one of the most underdeveloped areas of undergraduate biology education. This study sought to determine the feasibility of undergraduate biology majors conducting exploratory analysis using the same interactive data visualizations as practicing scientists. We examined 22 upper level undergraduates in a genomics course as they engaged in a case-based inquiry with an interactive heat map. We qualitatively and quantitatively analyzed students’ visual analytic behaviors, reasoning and outcomes to identify student performance patterns, commonly shared efficiencies and task completion. We analyzed students’ successes and difficulties in applying knowledge and skills relevant to the visual analytics case and related gaps in knowledge and skill to associated tool designs. Findings show that undergraduate engagement in visual analytics is feasible and could be further strengthened through tool usability improvements. We identify these improvements. We speculate, as well, on instructional considerations that our findings suggested may also enhance visual analytics in case-based modules.

Investigating Levels of Graphical Comprehension Using the LOCUS Assessments

“…Technological advances have had a universal impact on the ability of various industries and research fields to collect, store, visualize, and analyze large amounts of data (Forbes et al 2014). As a result, data visualization tools have developed over the past ten years to present complex data and information in a succinct way to foster data interpretation in order to discover new relationships or patterns (Keim et al 2010;Forbes et al 2014;Mirel et al 2016). Data visualizations facilitate communication and exploration of the statistical information that saturates daily life (Koparan and Güven 2015;Philip et al 2016).…”

Data Scientists’ Epistemic Thinking for Creating and Interpreting Visualizations

“…In recent years, technological advances have had a universal impact on the ability of various industries and research fields to collect, store, visualize, and analyze large amounts of data (Eilam, 2015;Forbes et al, 2014). As a result, visualization software tools have developed over the past ten years to present complex data and information in a more succinct way so as to foster interpretation and discovery of new relationships or patterns (Forbes et al, 2014;Keim et al, 2010;Mirel et al, 2016). The use of visualization methods and techniques to make sense of large datasets has become an essential way to represent and interpret information (Figueiras, 2013).…”

“…The basic idea of data visualization, namely, making sense of graphical representations of data, has been well studied across statistics, mathematics, and science education in multiple ways using various names and constructs (Cooper & Shore, 2010;Pfannkuch, 2006;Maltse et al, 2015;Mirel et al, 2016;Roth & Bowen, 2001). One prominent and pertinent approach from the perspective of statistical literacy 2 emphasizes graph comprehension (Curcio, 1987;Friel et al, 2001).…”

Improving Debt Literacy by 2/3 Through Four Simple Infographics Requires Numeracy and Not Focusing on Negatives of Debt

“…Therefore, we designed our study to go one step further and test the processes underlying effective education in the field of debt education. In our study, we decided to focus on visual educational techniques because they are likely to be particularly efficient (Mirel et al, 2016 ; de Haan et al, 2018 ); they enable easy access, do not require a major time commitment, and meet the civilizational and cultural changes reflected in image-based communication and the shift of human activity to the online sphere. Moreover, the media and Internet are among the top sources for learning about financial management, especially credit practices (Hilgert et al, 2003 ).…”