What Does Learning Look Like?: Data Visualization of Art Teaching and Learning

Abstract

Drawing upon the data visualization work of Lev Manovich and Manuel Lima, in this chapter the author discusses ways for envisioning and representing the complex teaching and learning that is associated with the visual arts. Experiences and examples are shared that use new and old technologies to create and make connections among critically reflective collections of student learning artifacts such as research, journals, preliminary sketches, work in other classes, and realms of experience outside of school. Instead of relying on one final art product, the author explores embedded data mining and visualization as a viable approach to gauging student learning. Following the lead education notables Elliot Eisner (2002, 2004), John Dewey (1934), and Howard Gardner (1985), this research positions the visual arts as a common thread throughout disciplines. Such inherent and fundamental visual arts practices as portfolios, project-based instruction, and exhibition continue to expand instruction and learning in such classes as English, math, science, and history. The implications include the possibility that art education will lead the way to implementing authentic embedded assessment processes across education disciplines and grade levels.