Dissertation defense oct 30
- 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
Editor's Notes
- #2: Personal story Before I was a graduate student, I taught business at the community college. Before community college, I was a financial analyst for a multi-national corporation. In the business world, when the computers don’t work, we go home – there isn’t a way to be productive without our technical tools. When my daughter started Kinder, at one of the most selective schools in the city, I was surprised at the lack of technology. There were computers, but in all honesty, if they went down, I think only the cafeteria wouldn’t be able to work. For students and teachers, schooling went on. Then my family moved to Austin and I had the opportunity to apply to this program, in my application I asked the question, what is the role of computers in education? Through coursework and research here at the University exposed me to the epitome of technology integration practices. At the same time, my teaching experiences at a title 1 high school provided a counter-narrative to the potential of technology. What I observed was a great divide, along traditional lines of marginalization, that a few schools are participants in the digital culture while many are struggling to make do and keep up (assimilate).
- #7: These variables were selected based on literature review and the CRT framework
- #9: The Omega-squared and R-squared explained the percent of variance in the model. Although the effect size measures were small, they were statistically significant and represented the percentage of variation which can be attributed to these four factors.
- #10: There is no correlation between per student expenditures The factor with the greatest correlation is the percentage of economically disadvantaged students Interestingly with race the effect of African-American and Hispanic students has a greater effect than the percentage of White students
- #11: We recognize that schools are complex ecosystems where variables influence each other. The goal of the parsimonious model is to identify those factors which work together to influence technology readiness. Using step-wise modeling, 10 factors were tested, three were eliminated, to develop the model that had the greatest effect. The model represents 6.8% of variation in technology readiness within which each factor makes a contribution.
- #12: Each of these three factors was statistically significant and predicted lower levels of technology integration. Of the factors tested with ANOVA, Title 1 status was the most significant, explaining 2.4% of variation in technology readiness scores. The mean technology readiness score for non-Title 1 schools was 2.7 while it was 2.54 for Title 1 schools, a 5% difference. The other two factors that provide economic data, percentage of economically disadvantaged students and percentage of students eligible for free and reduced lunch, have a weak, negative correlation. As the percentage of these vulnerable student populations increases, the technology readiness scores decrease. As with Title 1 status, the correlation between economically disadvantaged students had the greatest magnitude of the factors tested at r = -0.234. These findings confirm the findings by other researchers who have identified differences in technology integration practices based on socio-economic status Economic status of students is a major contributor to digital inequity in schools as well as to general educational achievement. Short-term or familial poverty negatively impact many of our students. As a nation, we believe that education can position students for life and career success and help them overcome poverty. Poverty is a roadblock for students and is not easily overcome. According to recent survey of Children Living in Poverty, the National Center for Education Statistics (2013) found that 21% of American school children live in poverty, an increase over the past two decades. Furthermore, the Center reports that living in poverty is connected with lower than average academic performance that begins in elementary school and extends to high school. Students living in poverty also graduate high school at lower than average rates. Student ethnicity: the Pearson correlation has a stronger effect with the percentage of Black and Hispanic students than with percentage of White students. Meaning, the technology readiness score decreases at a faster rate in schools with greater percentages of Black and Hispanic students than the score increases in schools with greater percentages of White students.
- #15: Self-assessments may over-estimate or under-estimate educational practices and may not represent the reality of technology integration. Technology integratoin is a difficult ocnstruct to define and using the STaR chart data may not be fully representative of technology integration
- #18: Just as technology has brought about new opportunities for social engagment, it has brought about new inequalities and perhaps sharpens social divisions. Clinton explicitly said that it (the divide) “will not disappear of its own accord. History teaches us that even as new technologies create growth and new opportunity, they can heighten economic inequalities and sharpen social divisions. Digital equity is the social-justice goal of ensuring that everyone in our society has equal access to digital tools. Even more importantly, digital equity is about technical literacy. In our digital society, digital equity has profound, long-lasting implications. The goal shared by all leaders is toward educational equity, it’s the right thing to do. In addition, it’s the law of the land. Education is a complex enterprise with many stakeholders. Bennett and LeCompte identified 4 functions of education: intellectual, social, economic, and political. about the function of education and said Adding the goal of digital to the is a goal shared by all leaders
- #19: Despite our very principled goals of equity, we acknowledge that there are differences in educational opportunities as well as digital participation. Mid-1990’s, the NTIA investigated the distribution patterns of digital devices in US households and found alarming divisions. At the time, President Clinton and Vice President Gore began to talk about the digital divide specifically in schools to promote the administration’s efforts to digitize our schools and thus move the US towards digital equity. Researchers who were investigating technology in school started calling for a more nuanced understanding of educational computing. They hoped to get a deeper understanding of where the inequalities were. In 2002, Esther Hargattai coined the term: second-level divide which moves the conversation from a binary classification of users vs non users to a multi-demontional conversation about the range of use. Thus, today, the digital divide is described as a top level divide and a second level divide.
- #20: A review of 79 articles attempted to document the breadth and depth of the research on the digital divide. less than one-third of studies were conducted in the K-12 school context. This is significant since numerous national level initiatives, such as E-rate (Telecommunications Act of 1996, 1996) and National Education Technology Plan (“National Education Technology Plan 2010,” n.d.), call for digital technologies in the education system to transform learning and prepare students for the 21st century. Additionally, the English Language Arts Standards in the Common Core, adopted by 46 states, include media and technology literacy, defined as critical analysis and production of media (“Common Core State Standards Initiative | Key Points In English Language Arts,” n.d.). The State of Texas is not a Common Core state but has comprehensive technology standards for all grade levels, beginning in Kindergarten through 12th grade (“19 TAC Chapter 126,” n.d.). the vast majority of studies focused on differences between individuals rather than differences between organizations, namely schools. Individuals, as units of analysis, were either students (e.g. Barron et al., 2010; Kim & Bagaka, 2005; Moore et al., 2002) or teachers (e.g. J.-Q. Chen & Price, 2006; Reinhart et al., 2011; Valadez & Duran, 2007). Few studies relied on schools as units of analysis where interventions can minimize the disparities at both the Top-Level Digital Divide (TLDD) and the Second-Level Digital Divide (SLDD) through access, infrastructure, social support, and skills development. A school’s digital culture, or lack of, shape a student’s conception of digital participation (Vie, 2008; Mark Warschauer et al., 2004; White & Selwyn, 2012). Several authors challenged the popular notion that computers and the Internet are the “great equalizers” in schools. Their research positions technology as another form of embedded inequity, commonly referred to as the Matthew effect, that is, those who have more resources and knowledge to begin with, benefit most from technology (Merton, 1968). 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. These findings indicate a need for more research as digital devices become more ubiquitous, especially with a focus on the role of schools in developing those who may be left behind, left out, or “have not” in our dynamic digital ecology. The preceding literature review identifies a gap in the understanding of the digital divide in the K-12 environment. The proposed study seeks to delve deeper into the digital divide which exists between K-12 schools in Texas.