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SES and Technology 1

LOW SES AND TECHNOLOGY IN THE HOME

The Effect of Low Socioeconomic Status Students with and without Technology in the Home on Timeliness and Thoroughness of Technology Based Assignments

Ashley ElkinsMarshall University Graduate College

EDF 621 August 6, 2009


Abstract

The purpose of this research is to determine if the presence or lack of technology in the home for low socioeconomic status students has an effect on the amount of time it takes for such students to complete technology based assignments and how thorough the assignments are upon completion. The research hypothesis is as follows: “Timeliness and thoroughness of technology based assignments will be greater for low socioeconomic status students with the presence of technology in the home than for low socioeconomic status students without technology in the home.” The subjects are four hundred low socioeconomic status middle and high school students from Wayne County, West Virginia. The subjects who participate in the study will complete computer based assignments in a contained environment. Data will be collected through the use of an approved rubric and will be analyzed with the use of an interdependent-samples t test.

Chapter One: Nature and Scope of the ProblemThe Effect of Low Socioeconomic Status Students with and without Technology in the Home on

Timeliness and Thoroughness of Technology Based Assignments

Technology has become increasingly predominant in the education of students from

elementary school through post-secondary education since the mid 1990s. More than ever,

students are expected to learn the ins and outs of software programs and to be able to use

these programs efficiently in their daily work. For some students, this is an unchallenging task.

However, many students are handicapped by the lack of interaction with technology they may

receive on a daily basis due to low incomes of families. For parents struggling to meet the basic

needs of their families, providing their children with access to computers and gaming consoles is

not a high priority. Therefore, these students do not have the same opportunities as children

with technology in the home to familiarize themselves with software programs.

In many cases, the introduction of technology in the classroom has been found to

increase the effectiveness of learning in students. Project based learning that integrates the


use of technology captivates and motivates students. As a result, educators are required to

incorporate technology into their classrooms to assist the 21st century learners. Although

technology has proved to be a positive aspect in many classrooms, are all students truly

benefiting from its presence? Are students from low socioeconomic backgrounds without the

presence of technology in the home at a disadvantage in technology based classrooms?

Students without access to technology may be at a disadvantage because the lack of experience

using computers and software programs hinders their ability to produce quality work in a

sufficient amount of time. When children are not familiar with software programs, simple tasks

will take a much longer time to complete than for a student familiar with such programs and

will rob technology deprived students of quality time which could be used completing

assignments.

The purpose of the study is to determine if the presence or lack of technology in the

home for low socioeconomic status students has an effect on the amount of time it takes for

such students to complete technology based assignments and how thorough the assignments

are upon completion. This problem will be examined by the following research question: “Do

low socioeconomic students who are raised in a technology rich environment perform better in

technology based classrooms than low socioeconomic students who are raised in homes

without access to technology?”

The null hypothesis is as follows: “There will be no difference in timeliness and

thoroughness of technology based assignments completed by low socioeconomic status

students with or without technology in the home.” The research hypothesis is as follows:


“Timeliness and thoroughness of technology based assignments will be greater for low

socioeconomic status students with the presence of technology in the home than for low

socioeconomic status students without technology in the home.”

The dependent variable for this research proposal is timeliness and thoroughness of

technology based assignments for low socioeconomic status students with or without

technology in the home. The first part of the dependent variable, timeliness, refers to the

amount of time it takes each student to complete technology based assignments. Being the

first student finished does not necessarily mean he or she performed better than those who

spent more time on the assignment; however, the research study will measure time as a matter

of efficiency. An efficient time is the time it should take students to adequately complete an

assignment. This will be determined through direct observation of students who are familiar

with technology completing assignments; also, an expert in the field of technology education

will be consulted. Thoroughness is a measurement of how complete the student’s assignment

is. This works with timeliness because a student may do a wonderful job of completing the

assignment, but the time it took him or her to produce the finish product hindered the

efficiency of his or her work.

The independent variables for this research proposal are low socioeconomic status

students with or without technology in the home. Socioeconomic status is based on parental

income, occupation, education, social status. The students qualifying as low socioeconomic

status would be those students who receive free or reduced breakfast and lunch.


The proposed research is important because the demand of technology based education

will continue to rise. If there is a link between the success of students who have technology in

the home compared to those who do not, further research will need to be conducted to learn

how to get students who are being left behind due to conditions beyond their control up to par.

In order for students to succeed beyond high school, they must be educated with tools and

methods they will use in a post secondary institution or in the work force.

In respect to ethical considerations, all student names will be kept confidential. When

findings are presented, names will be coded and randomized so that no logical order can be

found to identify students. There are no dangerous aspects of this research which could harm a

student in any way, shape or form. All student participants will be required to gain parental

permission to participate in the study. Also, home surveys must be conducted to determine the

level of technology available in each home; this information will also remain confidential and

information presented in findings will also be coded.


Chapter Two: Review of the Literature

This review of the literature is intended to offer a view into research which has

previously been conducted on technology based education from pre-school through adulthood.

This review begins with the study of the use of technology to promote academic achievement

and behavior management with low socioeconomic African-American students and concludes

with a study examining the difference in computer usage by students of low socioeconomic

status compared to students of middle or high socioeconomic status in the home and at school

The purpose of the study by Laffey, Espinosa, Moore and Lodree (2003) was to

determine if interactive computer technology (ITC) had an impact on student achievement in

math and behavior for low socioeconomic African-American students who displayed behavior

problems. The Midwest school selected for this study was composed of 400 students. The

school’s standardized test scores ranked in the bottom 5% of the state’s academic achievement

test. None of the school’s students met the state standards in science, mathematics, social

studies or language arts. The students selected for study came from prekindergarten,

kindergarten and first grade classrooms. To determine behavior level, the Social Skills Rating

System (SSRS) was used; students scoring 70 or higher were identified as being at-risk for future

behavior problems while scores of below 50 indicated a student was not at-risk for future

behavior problems. Four study groups were selected for the study. Two groups, one at-risk

and one not at-risk for behavior problems, received ICT treatment while the remaining two

groups, also composed of one group at-risk and one not at-risk for future behavior problems,


received no ICT treatment and was used as a comparison group. Specific numbers for each

group were not indicated.

Data was collected for this study in two different methods: standardized testing and

direct observation. All students were given a pre-test and a post-test individually. The test for

prekindergarten and kindergarten was made up of tasks that required students to count, to

recognize numbers, to write numbers, to recognize shapes and to make shape patterns. The

test for first grade was made up of tasks that required students to recognize two-digit numbers,

add and subtract numbers, tell time, count money, identify simple fractions and draw shapes.

Observation during the ITC treatment sessions was conducted by a research assistant. The

assistant was trained to accurately document student behavior on a scale from one to five. The

research assistant worked with each student in the treatment groups two times each week in

20-25 minute sessions for eight weeks in which students would work with programs such as

Jumpstart, Mighty Math and Millie’s Math House. The research assistant did not help students

solve mathematical problems; the only instruction in mathematics that each student in both

the treatment and comparison groups received was from the classroom instructor.

Post-test scores revealed that both at-risk and not at-risk students in the treatment

group scored higher than the students in the comparison groups. The not at-risk students, with

a gain score of 1.04, in the treatment group did score higher than at-risk students, with a gain

score of 0.14, in the treatment group; however, the at-risk students in the treatment group

scored higher on the post-test than not at-risk students, with a gain score of -0.16, in the

comparison group. Also, while in the ITC treatment room, student behavior significantly


improved. Students were observed as being attentive, enthusiastic and engaged. However,

upon returning to their regular classrooms student misbehavior continued. Therefore, a t test

showed no significant difference in change of behavior before or after the study. The figures for

the t test are not given in the article.

The purpose of the study by Jantz, Anderson and Gould (2002) was to determine if

computer based assessments could be successfully used in the education of nutrition for low

income Hispanic mothers. The study was completed in Colorado; however, the article is not

specific in which city or cities the research was completed. Participants were located through

county health departments, Special Supplemental Nutrition Programs for Women, Infants and

Children clinics and English as a Second Language classes. The study consisted of two groups: a

control group and an intervention group. Thirty-four voluntary participants were included in the

control group while thirty-six voluntary participants were included in the intervention group.

Although the study was targeted at Hispanic mothers, any low income individual over the age of

18 with a child was invited to participate. In total, there were 67 female participants and 3 male

participants. All but three participants made less than $30,000 per year. Forty-four of the

participants made less than $10,000 per year or were unemployed.

Two types of data were collected in this study: formative and summative. The formative

data was collected during the developmental phase and was used to make corrections or

adjustments in the program. The individuals participating in the collection of formative data

were upper level nutrition students and members of the La Cocina Saludable program, an

interactive multimedia (IMM) program directed at low income Hispanic mothers. The


summative data was collected through a pre-test and a post-test of the intervention and control

groups. The intervention group was exposed to an IMM program titled “Make a Great Start”

which incorporated the use of scripts, storyboards, graphics, translations and audio. No literacy

skills were needed to complete this assessment. The control group was also exposed to an IMM

program; however, the content covered was based on budgeting and had no information about

nutrition. Both groups were exposed to an IMM program so that the differences in levels of

computer skills between the two groups would not interfere with results.

The data was analyzed through the use of a t test to determine if the use of IMM

programs successfully improved the knowledge of nutrition in the participants. The control

group had an average pretest score of 51.5% while the intervention group had an average

pretest score of 51.1%. As expected, the intervention group showed a greater amount of

improvement than the control group. The control group’s average post-test score was 47.4%

(p=0.07) while the intervention groups average post-test score was 83.3% (p=0.000).

Approximately 94.4% of the intervention participants improved their score in knowledge of

nutrition. Since the increase was so dramatic, the McNemar’s chi-square test was used to

reanalyze the knowledge based questions. The score for each question for the intervention

group significantly increased from pretest to post-test; however, the figures are not given in the

article.

The purpose of the study by Li and Edmonds (2005) was to determine if at-risk adult

learners would benefit from technology integration in mathematics. The study was conducted

in an adult base education high school in Western Canada. Students participating in the


program had participated in other education programs, but most were unsuccessful. The study

consisted of three classes. Class 1 was composed of twenty-two students who had an average

age of 25 years old. Students were from different ethnic and cultural backgrounds.

Approximately 37% of the students did not speak English as a first language. These students

were also from a low socioeconomic background. No information on the makeup of Class 2 was

given; it was indicated that Class 2 used the same model and Class 1, but the only data that was

collected was from an exit survey. Class 3 was composed of sixteen students; no information

on the makeup of the students other than race is given. Most students were Caucasian (45%)

with other races being Native (27%) and Other (28%). The instructor for the treatment class

had completed graduate level education in technology.

Three types of data were collected in this research study: student testing, student open

ended surveys and teacher observations and lesson planning. All students participating in the

study completed a pre-test and a final examination for the course. Students who participated

in the treatment group completed both paper and online quizzes and exams. Also, students in

the treatment group were required to spend at least one hour out of the weekly five hours of in

class time working with mathematics programs on the computer over a sixteen week period.

The instructor had also created a website for students to access outside of the classroom.

Students in the control classroom were only given paper examinations. The entry and exit

surveys were given to students in the treatment classroom to first gather information on

previous computer skills and then to gather information on what they learned during their

experience. Teacher observations were used to adjust lesson planning when needed.


The data was analyzed through the use of a t test to determine if there was a significant

difference between the class which incorporated technology into the curriculum and the class

that did not. Through the use of the entry survey, it was found that 83% of the treatment

students had the use of a computer at home and 67% used the Internet on a regular basis.

However, only 25% of these students felt comfortable using programs such as Microsoft Excel

or Microsoft PowerPoint. The t test (t = 0.305, p = 0.76) revealed that there was no significant

difference between the knowledge of students in either class before entering the program with

the treatment group scoring an average of 68.8% on the pre-test and the control group scoring

an average of 67.6%. It was also found that there was no significant difference (t = 1.9, p =

0.06) in the final grades between the two groups; the treatment group had an average of 68.3%

and the control group had an average of 30.3% with a standard deviation of 29.3%. However,

the treatment group scored significantly better on exams in whole number, fractions and

decimals; the scores for these tests for treatment and control groups are as follows

respectively: 83% and 54.8% ( t= 6.77, p = 0.000), 73.7% and 56.1% (t = 2.09, p = 0.047), and

79.1% and 56.6% (t = 2.45, p= 0.021). The exit surveys also revealed that students in the

treatment class felt that the integration of technology was helpful; some also indicated that

they would like to use computers more in the future, particularly with education.

The purpose of the study by Primavera, Widerlight and DiGiacomo (2001) was to see if

integrating technology into the preschool curriculum with the use of a mentor trained in

technology would increase student achievement in math, language arts and fine arts as well as

increase student understanding of how a computer works. The study was conducted by

Fairfield University in partnership with Action for Bridgeport Community Development (ABCD)


of Bridgeport, Connecticut. To begin with, there were 295 students from urban Head Start or

Childcare programs. The ages of students ranged from almost 3 years to 4 ½ years old. Ninety-

six percent of students in the study came from families with a yearly income of less than

$30,000. Only 33% of students had access to a computer at home. Students were randomly

assigned to two classroom conditions: Traditional Access (7 classrooms) or Mentor Medicated

Instruction (10 classrooms). A total of 126 students were in the traditional classroom while 169

students were part of the treatment classrooms. However, 94 students were dropped from the

study due to leaving the program or excessive absences. The total students involved in the

study were as follows: 89 students in Traditional Access and 123 students in Mentor Mediated

Instruction. The Traditional Access classroom resembled a traditional preschool classroom

where a teacher would oversee student usage of computers; however, the Mentor Mediated

Instruction class had a technology trained undergraduate student work with students 15-30

minutes during a total of sixteen training sessions.

Three sets of data were collected to determine student success: a school readiness

skills assessment, the Computer Knowledge Scale, and the Children’s Computer User

Assessment Scale. The Jumpstart Pre-K software program was used to measure the students’

school readiness, or in other words, how prepared a student was to enter kindergarten. This

test measures language arts, fine arts, and mathematics. The Computer Knowledge Scale (CKS)

measured how well students knew the parts of a computer and their function. Instructors

would point at an object and ask students if they knew what the part was and what it did on

the computer. The Children’s Computer User Assessment Scale (CCUAS) was a ‘homemade’

scale developed by the researchers to determine the students’ confidence level, anxiety level,


understanding of how the computer works, ability to use the computer, and overall

improvement of working with the computer. This scale was only used with the treatment

group.

The data for this study was analyzed using chi squares and t-tests. It was found through

a pre-test of the school readiness exam that there was no significant difference of student

knowledge or performance upon entering the program. The school readiness exam post-test

data indicated that children in the treatment group scored significantly higher than those in

students in the control group (x2 = 27.42, df = 2, p < 0.001). For example, 31% of students in the

treatment group had a score of mastery on the post-test while only 1% of students in the

traditional classes had a score of mastery. Also, 26% of the treatment scored beginner while

55% of the traditional students scored beginner. The Computer Knowledge Scale was analyzed

through ANOVA, and it was found that students in the treatment classrooms had a larger

vocabulary (F(1,200) = 65.24, p < 0.001) for parts of the computer and understood their

functions much more than students in the traditional classroom. The final test, CCUAS, found

that students in the treatment classrooms did not experience much anxiety when using

computer before or after the program. However, it did find that student confidence (t(117) =

6.42, p < 0.001) and understanding of computer usage (t(117) = 10.88, p < 0.001) significantly

rose.

The purpose of the study by Page (2002) was to determine if technology based

classrooms promoted higher achievement than traditional classrooms. The study also

evaluated student self-esteem and interaction between the two types of classrooms. The study


was conducted in ten Louisiana elementary classrooms from five different schools. The levels

studied were third and fifth grade classrooms. Of the ten classrooms, five were technology

based and five were traditional. The schools selected consisted of mostly low income families

with all students being considered low socioeconomic status students. Students were selected

to participate in the study at random by the school principals. All students participating in the

study were in a regular education, self contained classroom. All teachers, including those in

control classrooms, were highly qualified and trained in technology.

Three types of data were collected in this study. Achievement was determined through

two types of standardized tests: the Iowa Tests of Basic Skills used by nine of the schools and

the California Achievement Test used by only one school. Tests were given in April 1998 and in

March 1999. Self-esteem was measured through the Coopersmith Self-Esteem Inventories

given in October and November 1998 and posttested in April and May 1999. Finally, classroom

interaction was measured through direct observation of the researcher once at the beginning

of the school year in 1998 and again at the end of the school year in 1999.

The ITBS and CAT standardized tests were analyzed using an analysis of covariance

(ANCOVA). It was found that there was no significant difference in reading levels between the

two groups. However, students who participated in the technology enriched classroom had a

significantly higher score in mathematics than students who participated in the traditional

classroom. ITBS Mathematics totals for the technology enriched classrooms had an adjusted

posttest score of 196.40 while the traditional classrooms had an adjusted posttest score of

191.54; CAT Math Concepts and Applications scores for the technology enriched classrooms


had an adjusted posttest score of 26.95 while the traditional classrooms had an adjusted

posttest score of 13.17. The results of the survey examining self-esteem did not indicate any

difference between the two types of classrooms. Also, there was no significant difference

found on the level of interaction between the two types of classroom; however, it was found

that the technology based classroom had more student initiated interaction while the

traditional classroom had more teacher initiated interaction.

The purpose of the study by Thomas (2008) was to determine if there was a difference

in the frequency of computer usage by students of low socioeconomic status compared to

students of middle or high socioeconomic status in the home and at school. The study was

completed in the Mississippi Delta, and included the participation of 1,119 students. Of these,

571 students came from low socioeconomic schools which was determined by the schools’ Title

I status. The remaining 548 students came from schools that did not qualify for Title I status

and was therefore considered to be middle to high socioeconomic schools. The students of low

socioeconomic status were most often found in rural areas whereas the students of middle or

high socioeconomic status lived in towns or cities.

Data was collected in this study through the use of a survey. Students were asked if

they had a computer at home, if they had Internet access at home, if they accessed the

computers and Internet at school, and how they felt about their ability to use different forms of

technology. Both sets of students were presented with eleven categories for students to

indicate if they could do the skill without help, do the skill with help, or not able to complete

the skill at all. These categories include the following: play games, surf the net, e-mail, watch


DVDs, play music CDs, type a letter/report, prepare a spreadsheet, create a database, publish

pictures, create pictures and create PowerPoint slideshows.

The data collected was analyzed using a chi-square. It was found that 94.3% of non-Title

I students (NTS) had access to a computer at home while 76.4% of Title I students (TS) had

access to a computer at home (Chi-square analysis Value = 69.81, df = 1, Asymp. Significance =

0.000). It was also indicated that 86.7% of NTS had access to the Internet at home while only

65.5% of TS had access at home (Chi-square analysis Value = 79.756, df = 2, Asymp. Significance

= 0.000). When asked if students used computers at school, 99.8% of NTS and 95.8% of TS said

that they did use the computers provided by the schools (Chi-square analysis Value = 20.696, df

= 1, Asymp. Significance = 0.000). However, 77.0% of NTS and only 62.2% of TS indicated that

they used the Internet for research or for fun while at school (Chi-square analysis Value =

28.224, df = 2, Asymp. Significance = 0.000). Of the eleven categories for students to indicate

whether they could complete the skill by themselves, with help or not at all, Title-I students felt

that they could complete the skill without help better than their non-Title I peers in only three

categories: watch DVDs, play music CDs and publishing pictures. All three of these are very low

level skills.


Chapter Three: Methodology

Design:

The study proposed quantitative research with a two group design. Subjects will be

chosen through a stratified sampling based on qualifying characteristics. The dependent

variable for this research proposal is timeliness and thoroughness of technology based

assignments for low socioeconomic status students with or without technology in the home.

Subjects:

The target population for this research proposal is all low socioeconomic status students

in the nine Wayne County Middle and High Schools which totals 1,923 students (West Virginia

Department of Education, 2008). The sample for this study will be selected through a stratified

sampling, meaning students are chosen at random to represent a specific number of students

from each group. These groups are low SES students with technology in the home and low SES

students without technology in the home. Before the selection begins, a survey will be

completed by all students in which each student will indicate what types of technology they

have in their home. A total of 400 students will be selected to participate in the study and will

be split into groups as follows: Low SES middle school students without technology in the

home: 100 students, low SES middle school students with technology in the home: 100

students, low SES high school students without technology in the home: 100 students, and low

SES high school students with technology in the home: 100 students.


Instruments:

The data collected will be the scores from student assignments which are based on

thoroughness and timeliness of work. The instrument used to determine these scores is the

Student Achievement in Technology Based Education Scale, a homemade rubric created by the

researchers. An example of this rubric is given below. The major categories for this rubric

include Microsoft Word, Microsoft PowerPoint and Microsoft Excel. Each of these major

categories have subcategories specific to each program. Examples include formatting text for

Microsoft Word, adding page transitions for Microsoft PowerPoint, and creating graphs using

Microsoft Excel. Each major category will also include a subcategory for timing. All

assignments given should be able to be completed efficiently in 30 – 45 minutes. If students go

over this time limit, their score for timeliness decreases.

To ensure validity, the Student Achievement in Technology Based Education Scale will

be evaluated and approved by a panel of experts in computer based education in middle and

secondary schools. Upon approval, the rubric will serve as the measuring tool in a pilot run of

the research. The pilot will test a small group of students. This test run will allow researchers

to identify possible flaws in the rubric which will be adjusted and reapproved by the expert

panel before actual research begins. Also, reliability will be ensured through multiple

administrations of this study.


Microsoft PowerPoint

Criteria 1 3 5Formatting Student does not

include proper formatting of text or alignment of graphics. Many errors are shown throughout the presentation.

Student includes mostly proper formatting of text and alignment of graphics. Only a few errors are shown throughout.

Student includes proper formatting of text. Graphics are included where necessary and are properly aligned.

Transitions/ Animations

Student does not include any transitions and/or animations in the presentation.

Student includes some transitions and/or animations throughout the presentation.

Student includes transitions and/or animations throughout the presentation.

Sound Student includes no sounds in the presentation.

Student includes one sound in the presentation.

Student includes two or more sounds in the presentation.

Logical Flow Presentation information is not consistent and does not follow a logical flow.

Presentational information is mostly consistent and follows a logical flow.

Presentation information is consistent and follows a logical flow.

Hyperlinks Student includes zero (0) working hyperlinks in the presentation.

Student includes a minimum of one (1) working hyperlink in the presentation.

Student includes a minimum of two (2) working hyperlinks in the presentation.

References No references are included.

References are included but are not properly cited.

All references are proper cited.

Grammar There are four (4) or more grammatical or typographical errors found in the presentation.

A maximum of three (3) grammatical or typographical errors are present.

No grammatical errors are present.

Time Student completes assignment in more than twenty (>20) minutes of the recommended time for assignment completion.

Student completes assignment within twenty (20) minutes of the recommended time for assignment completion.

Student completes assignment within ten (10) minutes of the recommended time for assignment completion.


Procedures:

I. Before research can begin, permission must be granted from the university.

Proposals will be submitted to appropriate committees.

II. Upon approval, permission from county and school officials in Wayne County will be

obtained.

III. Surveys inquiring about family income, parental occupations, parental education and

technology in the home will be sent to Wayne County middle and high schools.

IV. The surveys will be separated into groups first based on low socioeconomic status

and then into groups of which low SES students have technology in the home and

which do not.

V. From these groups, a random selection of 400 students will be chosen to participate

in the research.

VI. Parental permission will be obtained for each student.

VII. Research will occur during the regular school year. Students will be pulled out of

class to complete computer based assignments in a confined room while being

observed by the researchers. The time it takes for students to complete the

assignments will be noted by the instructor.

VIII. Students will save all work on provided flash drives which the researchers will later

use to analyze data and produce results.

IX. Data will be analyzed.


X. When publishing findings, student confidentiality will be kept through coding of

names and reporting results in a random fashion so that no obvious procedure can

be noted.

Data Analysis:

Null Hypothesis: “There will be no significant difference in timeliness and thoroughness of

technology based assignments for low socioeconomic status students with or without

technology in the home.”

Data will be analyzed using inferential statistics, specifically using the interdependent-

samples t test. This will allow researchers to determine if there is or is not a significant

difference in timeliness and thoroughness of technology based assignments for low

socioeconomic status students with or without technology in the home. Also, the test of

significance for the research will have a p level of 0.05, indicating that only five out of every 100

times the same research is conducted will yield different results.


Resources

Jantz, C., Anderson Ph.D., J, & Gould Ph.D., S. M. (September/Octoboer 2002). Using computer

based assessments to evaluate interactive multimedia nutrition education among low

income predominantly hispanic participants. Journal of Nutrition Education and

Behavior, 34(5), 252-260.

Laffey, James M., Espinosa , Linda, Moore, Joi, & Lodree, Anika (Summer 2003). Supporting

learning and behavior of at-risk young children: Computers in urban education. Journal

of Research on Technology in Education, 35(4), 423-440.

Li, Qing, & Edmonds, K. A. (Winter 2005). Mathematics and at-risk adult learners: Would

technology help?. Journal of Research on Technology in Education, 38(2), 143-166.

Page, Michael S. (Summer 2002).Technology-enriched classrooms: Effects on students

of lowsocioeconomic status. Journal of Research on Technology in Education, 34, 389-

409.

Primavera Ph.D., Judy, Wiederlight, Peter P., & DiGiacomo, Timothy M. (August 2001).

Technology access for low-income preschoolers: Bridging the digital divide. Paper

presented at the annual meeting of the American Psychological Association, San

Francisco, CA. Retrieved July 18, 2009 from http://www.knowledgeadventure.com/

school/teacher/pdf/childtechnology_WhitePaper.pdf


Thomas, Dianne (Summer 2008).The digital divide: What schools in low socioeconomic areas

must teach. Delta Kappa Gamma Bulletin, 74(4), 12-17.

West Virginia Department of Education, (2008). Schools by composition all grades all subgroup-

sorted by county, school, school year: 2008-2009. Retrieved June 29, 2009, from West

Virginia Department of Education Web site:

http://wveis.k12.wv.us/nclb/pub/enroll/e06Makeup.cfm?sy=09

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