Systematic digital inequities : evidence from the School Technology and Readiness chart

The primary purpose of this study was to identify and quantify the relationship between school and student characteristics and the campus technology readiness score as reported on the School Technology and Readiness (STaR) 2013 report issued by the Texas Education Agency. The secondary purpose was to identify those student and school characteristics that are statistically significant in predicting STaR composite scores as an indicator of technology integration. This study contributes to research on the digital equity and inequity by exploring the differences between K-12 schools in Texas. The unit of research was the schools themselves, thus changing the research focus from individuals and households to institutionalized, public, educational campuses. Secondly, the study used quantitative measures of technology readiness submitted by approximately 224,243 (StarChart, 2015) Texas teachers and aggregated to 6,091 schools. To address the research questions, quantitative methods were applied. Research questions and hypotheses were developed and tested to investigate whether a significant relationship existed between the dependent variable, the campus technology readiness score, and school characteristics and student characteristics. There were five independent variables for school characteristics and six independent variables for student characteristics. A parsimonious model was developed that identified the factors already evaluated independently, which were statistically significant in explaining the variation in the STaR composite score of technology readiness. Data analysis of 6,091 schools indicated that technology integration in Texas schools was statistically unequal based on student and school characteristics. Of the 11 factors tested, 10 were statistically significant, indicating that the differences were due to the evaluated factors rather than chance. Of the factors tested with ANOVA methodology, schools with Title 1 status had the highest R-squared (.024). Of the factors tested with Pearson product-moment correlation, schools educating higher percentages of economically disadvantaged students had the most influential Pearson r (-0.234). Using step-wise modeling, seven factors were included in the parsimonious model. The factors that contributed most to variation in technology readiness were percentage of economically disadvantaged students and the percentage of African American and Hispanic students. This research presents statistical evidence that technology integration practices vary between K-12 campuses in Texas and that there are systemic digital inequities. The research is a call to action to address digital inequity in Texas schools.Curriculum and Instructio