Examining the Digital Divide in Texas K12

Schools

Renata GeurtzProposal Defense Presentation

April 25, 2014

“Injustice anywhere is a threat to justice everywhere.” Dr. Martin Luther King, Jr

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 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

Literature Review Guiding QuestionsExamine research about the digital divide, and to identify the gap in research about digital divide in K-12 schools.

The guiding questions for the literature review are:

• Question 1: What is the nature of research about the digital divide?

• Question 2: What are the research evidences of digital divide in K-12 schools?

• Question 3: What are the research foci that researchers been investigating on the topic of digital divide in K-12 schools?

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

Research Question and Data SourcesWhat is the relationship between school characteristics,

student characteristics and the campus composite technology readiness score as reported on the STaR chart?

Data sources

• School Technology and Readiness Chart (Texas Education Agency); n = 6870

• Academic Excellence Indicator System (Texas Education Agency);

• National Center for Educational Statistics (US DoE);

• Financial Allocation Study for Texas (FAST) (Texas Comptroller of Public Accounts).

MethodCorrelation and ANOVA tests of statistics to determine if there is a relationship between

• composite STaR score, indicative of technology readiness practices, and

• school and student characteristics.

Create a parsimonious model by using a backwards step-wise analysis to identify those student and school characteristics which are statistically significant in predicting STaR composite technology readiness scores.

SPSS will be the statistical tool.

The data is for the 2013/14 academic school year which is the most current year of released STaR chart data.

Dependent VariableSchool Technology and Readiness Chart (STaR chart)

• All Texas educators are legally required to complete a 24-question self-assessment of their technology integration practices

• Six questions for each technology area• Teaching and Learning

• Educator Preparation & Professional Development

• Leadership, Administration, and Instructional Support

• Infrastructure for technology

• Answers are on a 4-point scale• 1 = level 1, early tech

• 2 = level 2, developing tech

• 3 = level 3, advanced tech

• 4 = level 4, target tech

• Scores are averaged to develop a Composite STaR score Interval Scale variables

Independent VariablesSchool Characteristics Variables

• Accountability Rating

• Locale Code

• School Level

• Title 1 Eligible School

• Per Student Expenditures

Student Characteristics Variables

• % At-risk Students

• % Economically Disadvantaged

• % Free/Reduced Lunch Eligible

• % Limited English Proficiency

• % White Students

• % Asian Students

• % Hispanic Students

• % Black, non-Hispanic Students

• % American Indian/Alaska Native Students

Ratio Scale variable = Correlation test of statisticNominal Scale variable = ANOVA test of statistics

Tests of StatisticsANOVA

• Accountability Rating

• Locale Code

• School Level

• Title 1 Eligible School

Significance will be determined at the .05 alpha level and the p-value will be observed to assess the strength of the relationship. The R-squared value will measure the strength of the model.

Correlation

• Per student expenditures

• % Economically Disadvantaged

• % At-risk Students

• % Free/Reduced Lunch Eligible

• % Limited English Proficiency

• % White Students

• % Asian Students

• % Hispanic Students

• % Black, non-Hispanic Students

• % American Indian/Alaska Native Students

Significance will be determined at the .05 alpha level and the Pearson r will be observed to assess the strength of the relationship. Tukey’s post hoc analysis will be conducted on the significant factors.

Nominal Scale variable = ANOVA test of statistics; Ratio Scale variable = Correlation test of statistics;

Parsimonious Model

• A parsimonious model will be developed through backward elimination.

• All variables will be tested and those that are not significant will be removed until only the significant variable (s) in the presence of each other remain and no improvement in the significance can be observed.

• Develop a model to identify those student and school characteristics which are statistically significant in predicting STaR composite technology readiness scores.

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 characteristics, student characteristics and the campus composite technology readiness score as reported on the STaR chart?

Chapter 4

Chapter 5