Document Type
Article
Abstract
The primary purpose of this study was to use visual analytics to examine the context in which U.S. schools can excel concerning science literacy. The school context was assessed using 49 various student-, teacher-, and principal-reported variables related to school resources, characteristics of the teaching force, and student dispositions regarding science learning at a school level. Using multidimensional scaling preference analysis as a visual analytical method of data mining, we examined the data of 238 U.S. schools collected from the Program for International Student Assessment in 2015. The visual analytic results suggested that the context in which schools can achieve better science literacy includes high- quality science teachers and favorable student dispositions toward science learning. The analysis of variance further supported the results. The results were discussed concerning the implications for school leaders for setting program priorities.
Publication Date
1-1-2022
ISSN
26663740
Publication Title
International Journal of Educational Research Open
Volume
3
DOI
10.1016/j.ijedro.2022.100191
Recommended Citation
Ding, Cody, "Examining the context of better science literacy outcomes among U.S. schools using visual analytics: A machine learning approach" (2022). Education Sciences and Professional Programs Faculty Works. 44.
DOI: https://doi.org/10.1016/j.ijedro.2022.100191
Available at:
https://irl.umsl.edu/espp/44