Introduction
This paper presents research conducted as part of a
five-year Innovative Technology Experiences for Students
and Teachers (ITEST) project, funded by the U.S. National
Science Foundation (NSF). The project’s goal is to focus
pre-teen interest in activities that foster learning about
energy consumption in students’ homes and communities,
and to incubate interests and knowledge about STEM
majors and careers. Data mining techniques (Witten,
Eibe, & Hall, 2011; Gibson & Clarke-Midura, 2013) were
employed to help select a parsimonious list of predictors
to be used in multiple linear regression analysis for each
gender, and then multidimensional scaling techniques
(Dunn-Rankin, Knezek, Wallace, & Zhang, 2004) were applied
to produce visualizations of major results (Knezek &
Christensen, 2014). The targeted outcome of this process
was isolation of variables worthy of more detailed study