Title

Bringing The Arts As Data To Visualize How Knowledge Works

Abstract

Professional audiences, scholars, and researchers bring varied experiences and expertise to the acquisition of new understandings and to problem solving in visual art and literary contexts. The same breadth of experience and learning capability was found for students at eighth grade, sampled from the national population of students in the United States who were queried in the National Assessment of Educational Progress (NAEP) about formal knowledge, technical skills, and abstract reasoning in visual art and in language arts. This chapter explores statistical data relating to the presence of art specialists in the sampled eighth grade classrooms. In particular, schools with specialists in place varied in density across the country as is demonstrated through geographic mapping. Secondary analysis of NAEP restricted data showed that students in schools with art specialists performed significantly better than students in schools with other types of teachers, or no teacher. The authors surmise that art specialists conveyed something fundamental to NAEP 2008 Response scores. An aspirational model of assessment assumes broad audience clarity through knowledge visualization technology, via thematic mapping. The authors explore through analog Deleuze and Guattari's double articulation of signs in natural and programming languages and demonstrate through knowledge representation the means by which complex primary and secondary statistical data can be understood in a discipline and articulated across disciplines. This chapter considers NAEP data that might substantiate a general model of aspirational learning and associates patterns in perception discussed by researchers and philosophers.

Publication Date

2-28-2015

Publication Title

Handbook of Research on Maximizing Cognitive Learning through Knowledge Visualization

Number of Pages

515-534

Document Type

Article; Book Chapter

Personal Identifier

scopus

DOI Link

https://doi.org/10.4018/978-1-4666-8142-2.ch019

Socpus ID

84946136453 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84946136453

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