Title

Predicting Student Performance Via Naep Secondary Art Analysis Using Partial Least Squares Sem

Keywords

Arts education policy; NAEP; Predictive analysis; Secondary data analysis; Smart PLS; Student performance

Abstract

This article describes a secondary analysis of the National Assessment of Educational Progress 2008 eighth-grade visual arts data (N D 3,912). These assessments occur under government mandate on a periodic schedule and data on the arts were collected in 1997 and again in 2008. The purpose of this study was to predict students’ visual art response performance using students’ home environment, personal characteristics, in-school curriculum, and art-related not-for-school (extracurricular) activities. Formative measurement models and structural paths were modeled in structural equation modeling (SEM) using Smart PLS. The initial SEM model included four latent constructs and one endogenous variable measuring students’ performance. Both direct and indirect effects between latent constructs were modeled and assessed. Altogether, the four latent constructs explained 21.3% of the variance students’ responding performance, out of which home environment construct had the strongest impact. School-related artistic activities in school do predict students’ performance significantly but in lesser strength. Students’ personal attributes and their art-related not-for-school activities predict students’ performance to a substantially lesser degree. Implications of these findings will be discussed in terms of the data findings and larger issues of what these data represent as a means of following curriculum articulation with standards and the impact of art specialists in schools.

Publication Date

11-28-2018

Publication Title

Arts Education Policy Review

Volume

119

Issue

4

Number of Pages

231-242

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1080/10632913.2017.1327382

Socpus ID

85035748207 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85035748207

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