This dissertation examines evidence of systematic digital inequities in Texas K-12 schools. The study analyzed data from over 6,000 schools representing 90% of Texas K-12 public schools. A quantitative analysis found several school characteristics were correlated with lower technology readiness scores, including higher percentages of economically disadvantaged, Black, and Hispanic students. The top predictors of lower scores were the percentages of economically disadvantaged students and Black and Hispanic students enrolled. The study recommends policies to address digital equity and opportunities for future research.

1. Systematic Digital
Inequities: Evidence from
the STaR Chart
Examining the Digital Divide in Texas K12 Schools
Renata Geurtz
Dissertation Defense Presentation
October 30, 2015
“Injusticeanywhere
isathreattojustice
everywhere.”Dr.
Martin Luther King, Jr

2. Research Question
What is the relationship between school and
student characteristics and the campus
composite technology readiness score as
reported on the STaR chart?

3. Critical Race Theory
• Research framework
• Provides a structure to understand and explore the
intersection of race, class, and gender
• Six tenets
• Centrality of race and racism
• Challenge to dominant ideology
• Historical analysis of contemporary issues
• Centrality of experiential knowledge
• Interdisciplinary perspective
• Commitment to social justice

4. Overview of Research Method
Participants: 6,091 K-12 public schools in Texas,
represents 90% of K-12 Texas schools.
Data was aggregated from over 225,000 teacher self-
assessments
Data Sources:
1. School Technology and Readiness Chart (Texas Education Agency); n = 6870
2. National Center for Educational Statistics (US DoE);
3. Financial Allocation Study for Texas (FAST) (Texas Comptroller of Public Accounts).
Hypotheses:
11 hypotheses exploring the relationship between the
dependent and independent variables

5. Dependent Variable
STaR Chart data for each campus was averaged to
calculate a composite score of technology readiness
Focus areas on the STaR chart include
Teaching and learning
Educator preparation & professional development
Leadership, administration, & instructional support
Infrastructure for technology
Composite score becomes a multi-topical measure of
technology readiness
School Technology and Readiness Chart (Texas Education Agency); n = 6870

6. Independent Variable
11 variables to explore the relationship between the
dependent and independent variables
1. TEA accountability rating,
2. locale,
3. school type,
4. per student expenditures
5. Title 1 status (of campus),
6. % of economically disadvantaged students,
7. % of at-risk students,
8. % of students participating in free/reduced lunch,
9. % of English language learners,
10. % of White students, and
11. % of Black and Hispanic students.

7. Quantitative Methodology
Correlation and ANOVA tests of statistics to determine whether there is
a relationship between
• Composite STaR score, indicative of technology readiness practices
(dependent variable), and
• Eleven school and student characteristics (independent variable).
Create a parsimonious model by using step-wise modeling to identify
those student and school characteristics which are statistically significant
in predicting STaR composite technology readiness scores.
SPSS was the statistical tool.
The data is for the 2012/13 academic school year, which was the
most current year of released STaR chart data.

8. ANOVA Models
Hypothesis
Omega-
squared R-squared
Hypothesis 1a: Schools with higher accountability ratings
will have a higher composite technology readiness scores
.021 .022
Hypothesis 1b: Schools located in suburban locales will have
higher composite technology readiness scores
.019 .020
Hypothesis 1c: High schools will have higher composite
technology readiness scores
.007 0.007
Hypothesis 1e: Schools with Title 1 status will have lower
composite technology readiness scores
.023 0.024
Finding is that there is statistical evidence to suggest that each of the
four factors explained variation in technology readiness scores.

9. Pearson Correlation Coefficient
In six of the seven factors, the r-value was less than .05, indicating that
there exists a statistically significant correlation.
Hypothesis Pearson’s r Relationship direction Implication
Hypothesis 1d: Schools with higher per student expenditures will have higher
composite technology readiness scores
0.001 no correlation
Per pupil spending does not correlate to STaR
composite scores
Hypothesis 2a: Schools educating higher percentages of economically
disadvantaged students will have lower composite technology readiness scores
-0.234 weak, negative correlation
Larger percentages of economically disadvantaged
students correlates with lower STaR composite
scores
Hypothesis 2b: Schools educating higher percentages of at-risk students will
have lower composite technology readiness scores
- 0.157 weak, negative correlation
Larger percentages of at-risk students correlates
with lower STaR composite scores
Hypothesis 2c: Schools educating higher percentages of students eligible for
free and reduced lunch will have lower composite technology readiness
scores
- 0.167 weak, negative correlation
Larger percentages of students participating the FRL
correlates with lower STaR composite scores
Hypothesis 2d: Schools educating more English language learner students
will have lower composite technology readiness scores
- 0.105 weak, negative correlation
Larger percentages of LEP students correlates with
lower STaR composite scores
Hypothesis 2e: Schools educating more White students will have higher
composite technology readiness scores
0.196 weak, positive correlation
Larger percentages of White students correlates
with higher STaR composite scores
Hypothesis 2f: Schools educating more African-American and Hispanic
students will have lower composite technology readiness scores
- .213 weak, negative correlation
Larger percentage of African-American and Hispanic
students correlates with lower STaR composite
scores
Field(2013)suggestsguidesforeffectsizes: r=.1(smalleffector1% oftotalvariance),r= .3(mediumeffector9% oftotalvariance), r=.5
(largeeffector25% oftotalvariance)

10. Parsimonious Model
Accounting for 6.8% of the variance in composite technology
readiness scores (R2 = .068) are seven factors.
1. % of economically disadvantaged students (81% of the 6.8%
effect in the model),
2. % of Black and Hispanic students (67% of the 6.8 effect in the
model),
3. % of White students (56.8% of the 6.8 effect in the model),
4. % of students participating in free/reduced lunch (41% of the
6.8 effect in the model),
5. % of at-risk students (36.5% of the 6.8 effect in the model),
6. % of English language (16% of the 6.8 effect in the model),
7. school type (8.5% of the 6.8 effect in the model).
eliminated threevariables (TEAaccountability rating, school locale, and Title 1status).

11. Discussion
There are differences in technology readiness scores between K-12 schools in
Texas and those differences are primarily based on:
• Socio-economic status measured thru several factors
• Title 1 status for the campus
• % of economically disadvantaged students
• % of students eligible for free and reduced lunch
• Student ethnicity
• % of White students (r = .196)
• % of Black and Hispanic students (r = -.231)
• School locale
• Suburbs
• Greatest mean difference was between urban and suburban schools
• No statistical difference between rural and suburban schools
• Accountability ratings
• 90% of schools met the accountability requirements
• 8.5% who need improvement have lower STaR scores
• School type
• High schools had the highest STaR scores
• Elementary schools are a missed opportunity
• Per student expenditure
• No correlation found, contrary to other findings

12. Recommendation for Policy Makers
• Update the high school graduation requirements to include a
one-year technology application course.
• Review and revise the STaR chart so that it is a better measure
of technology integration practices.
• Create a national measure of technology integration in schools
and student digital literacy.
• ISTE should expand focus on digital equity.

13. Future Research
• Large-scale surveys of school leaders, teachers, and students to
monitor digital literacy, technology integration practices and
infrastructure optimization. What are the trends and outlook of digital
integration in K-12 schools?
• Analyze and explore the digital differences between schools that score
high and low on the STaR chart. How are digital differences
manifested on the educational experience?
• Investigate the long-term implications for students who attend schools
at the high and low end of the STaR Chart. What are the effects on
college and career readiness?

14. Limitations
• Data quality since 3 data sets are from other organizations.
• Currency of STaR chart. Developed in 2001 and the questions
have not been revised since.
• STaR chart is a self-assessment by teachers and may not be
indicative of actual campus condition.
• Data is limited to Texas and is not representative of other
states.

15. Questions

16. Thank you.
It has been a great honor to be your student and to learn
beside my fellow classmates.

17. Digital Equity
Develop digital participation which improves societal
economic and educational divides which already
exist.
Educational participation is about the right to an
education, about the right to know, to learn and to
be empowered through education.
In a digital world, it is also about how one learns and
the learning resources one can access.

18. The Digital Divide
The term refers to the division between those how have access to
digital devices and those who do not.
• Top-level divide (TLDD): access to devices
• Second-level divide (SLDD): range of use as well as the
levels of intensity and types of use
The digital divide has been substantiated with numerous NTIA
reports.
• 1995 – Falling Through the Net: A Survey of the "Have
Nots" in Rural and Urban America : although more
households are connected, certain households are gaining
access to new technologies far more quickly, while others
are falling further behind.
• 2011 – Exploring the Digital Nation: Computer and Internet
Use at Home: a digital divide persists among certain groups

19. Literature Review Findings
• Less than one-third of studies were conducted in the K-12 school
context.
• The dominant research method was quantitative.
• The number of research studies about the digital divide has
remained constant despite the proliferation of technology in schools
and society.
• Less than 1/3 of studies relied on publically available data, nearly half
relied on researcher created instruments. Sample sizes are often
small, results can not be generalized.
• The vast majority of studies focused on differences between
individuals rather than differences between organizations, namely
schools.
• Corroborated the existence of the digital divide at both the Top-
Level Digital Divide and more profoundly at the Second-Level Digital
Divide at all levels of social engagement including individuals,
classrooms, schools, states, and nations.
The gaps in our understanding

20. The Goal
Examine the Digital Divide in Texas K-12 SchoolsSubstantive
Question
Statistical
Question
Statistical
Conclusion
Substantive
Conclusion
What is the relationship between school and student
characteristics and the campus composite technology
readiness score as reported on the STaR chart?
Chapter 4
Chapter 5