dissertation defense oct 30
TRANSCRIPT
Systematic Digital Inequities: Evidence from the STaR Chart
Examining the Digital Divide in Texas K12 Schools
Renata GeurtzDissertation Defense Presentation
October 30, 2015
“Injustice anywhere is a threat to justice everywhere.” Dr. Martin Luther King, Jr
Research QuestionWhat is the relationship between school
and student characteristics and the campus composite technology readiness score as reported on the STaR chart?
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
Overview of Research MethodParticipants: 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 =
68702. 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
Dependent VariableSTaR Chart data for each campus was averaged
to calculate a composite score of technology readiness
Focus areas on the STaR chart includeTeaching and learningEducator preparation & professional developmentLeadership, administration, & instructional supportInfrastructure for technology
Composite score becomes a multi-topical measure of technology readiness
School Technology and Readiness Chart (Texas Education Agency); n = 6870
Independent Variable11 variables to explore the relationship between the dependent and independent variables1. 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, and11. % of Black and Hispanic students.
Quantitative MethodologyCorrelation 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.
ANOVA Models
HypothesisOmega-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.
Pearson Correlation CoefficientIn 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 correlationPer 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 correlationLarger 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 correlationLarger 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 correlationLarger 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) suggests guides for effect sizes: r = .1 (small effect or 1% of total variance), r = .3 (medium effect or 9% of total variance), r =.5 (large effect or 25% of total variance)
Parsimonious ModelAccounting 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 three variables (TEA accountability rating, school locale, and Title 1 status).
DiscussionThere 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
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.
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?
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.
Questions
Thank you.It has been a great honor to be your student and to learn beside my fellow classmates.
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.
The Digital DivideThe 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 useThe 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
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
The Goal
Examine the Digital Divide in Texas K-12 Schools
Substantive
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