ADDRESSING THE HIGH SCHOOL DROPOUT CRISIS AT-RISK STUDENTS AND EDUCATION2020 ONLINE CREDIT RECOVERY

ADDRESSING THE HIGH SCHOOL DROPOUT CRISIS:
AT-RISK STUDENTS AND EDUCATION2020 ONLINE CREDIT RECOVERY
by
Kamala Dexter
_______________________________________________________________________
A Dissertation Presented to the
FACULTY OF THE USC ROSSIER SCHOOL OF EDUCATION
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree of
DOCTOR OF EDUCATION
May 2011
Copyright 2011 Kamala Dexter

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Table of Contents
List of Tables iv
List of Figures v
Abstract vi
Chapter One: Introduction 1
Background of the Problem 4
Statement of the Problem 5
Purpose of the Study 6
Theoretical Orientation 7
Significance of the Problem 10
Methodology 10
Limitations of the Study 11
Key Term Definitions 13
Chapter One Summary 15
Organization of the Study 15
Chapter Two: Literature Review 17
The Dropout Crisis 18
Dropout Rates 19
Dropout Consequences 23
Dropout Causes and Characteristics 26
K-12 Online Learning 27
Education 2020 34
Summary 38
Chapter Three: Methods 39
Research Questions and Hypotheses 40
Population/ Sample 40
Site 41
Data Collection and Instrumentation 42
Data Analysis 43
Education2020 44
Chapter Two Summary 45
Chapter Four: Data Analysis 47
Demographics 48
Data Cleaning 51
Principle Components Factor Analysis 52
Findings 54
Chapter Four Summary 63

iii
Chapter Five: Conclusions, Implications, and Recommendations 65
Conclusions 65
Implications and Recommendations 68
References 72
Appendices
Appendix A: Institutional Review Board Approval 78
Appendix B: Memorandum of Understanding 80
Appendix C: Education2020 Student Survey 82

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List of Tables
Table 1: Average Course Credit Accrual of Spring 2002 10th-Graders, 21
by Academic Year, Subject, and High School Status: 2004
Table 2: Demographic Characteristics of e2020 Satisfaction Survey Respondents 50
Table 3: Means, Standard Deviation, and Pearson Correlations for Select Variables 56
Table 4:E2020 Students’ Concurrent Face-to-Face Class Failure Rate 58
Table 5: Predicting Student Success (# of credits completed) by Courses Failing 59
Table 6: Student Demographics by School Site 61
Table 7: Summary of Hierarchical Linear Regression of Student Characteristics 62
on e2020 Satisfaction (N = 153)

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List of Figures
Figure 1: Social-Cognitive Application 8
Figure 2: Event Dropout Rates of 15- Through 24-Year-Olds Who Dropped Out 20
of Grades 10–12, by Family Income: October 1972 Through October 2004
Figure 3: Event Dropout Rates During 2008 by Ethnicity and Gender 20
Figure 4: Collapsed Satisfaction Scale 52

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Abstract
The purpose of this study was to examine at-risk students’ satisfaction with an
online credit recovery program, education2020, and its potential for providing equal
opportunities to get students back on track toward graduation. This study also
determined the degree to which at-risk student characteristics such as socioeconomic
status, gender, age, ethnicity, and academic behaviors affected experiences in
education2020 as measured by student satisfaction and pace of credit recovery.
This study consisted of 220 high school students enrolled in the education2020
online credit recovery program at a small, urban high school district. Students came from
three comprehensive high schools and one continuation school. The sample included
students who were identified for education2020 due to applicable credit deficiency. A
district-initiated survey gathered all demographic information and satisfaction levels.
De-identified credit recovery data from one quarter revealed credit recovery patterns of
one group of 67 seniors from the original 220-student sample. The survey data was
analyzed using a hierarchical linear regression analysis to determine whether any student
characteristics predicted satisfaction with education2020. Credit recovery data was
examined using anova and Chi-square analyses to determine the relationship between
pace of recovery and specific variables such as school site, academic behaviors, age, and
ethnicity.
The results of this study indicated that no demographic characteristic predicted
satisfaction with education2020; the only significant variance was related to school site.
There was no significant relationship between demographic characteristics and pace of

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credit recovery, but there was a positive correlation between academic behaviors such as
prior credits earned and pass rate in concurrent face-to-face classes.
Because no demographic characteristic prevented or ensured success in e2020,
these findings demonstrate equal opportunity for at-risk students to find satisfaction and
to recover credits. However, learning environment factors and academic behaviors did, in
fact, demonstrate a relationship to satisfaction and pace of credit recovery. This
information suggests that further research is needed to examine the specific components
of the online learning environment and of programs for at-risk students, as well as their
success in concurrent face-to-face classes and ability to recover credits in an online credit
recovery program.

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Chapter One
Introduction
California, home of the largest number of high school students in the United
States, sees approximately one dropout for every graduate (California Dropout Research
Project, 2009). The dropout epidemic across the United States is concentrated
disproportionately among racial and ethnic minorities of low socioeconomic status, who
are invariably enrolled in schools with high dropout rates (Shore & Shore, 2009).
America continues to face this challenging social issue, but is also equipped with
potential solutions resulting from technological growth. These promising solutions
involve presenting potential dropouts with innovative, technology-driven opportunities
that hold great promise for addressing the dropout rate in the United States (Blueprint for
Reform, 2009; Duncan, 2010; Gates Foundation, 2009; Watson & Gemin, 2008). It is
imperative that educators, policymakers, and all stakeholders take immediate steps to
offer students at risk of dropping out of school with the technological intervention of
online credit recovery. Credit deficiency is the primary reason for students’ decisions to
dropout of high school (Bridgeland, DiIulio, & Burke Morison, 2006; Institute of
Education Sciences [IES], 2009). High school students are expected to earn credits for
each class completed and, traditionally, when they do not pass a class have been required
to retake the same face-to-face class in the same amount of time. Online credit recovery
interventions enable students to retake courses that they previously failed and for which
they therefore did not earn credit (Watson & Gemin, 2008). This opportunity provides
students at risk of dropping out of high school due to severe credit deficiency a great

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opportunity, as they are able to work at their own pace, gain confidence, and potentially
recover more credits than are possible in the traditional classroom (Watson & Gemin,
2008).
High schools across the nation are still struggling to meet the graduation
expectations of The No Child Left Behind (NCLB) Act of 2001. According to NCLB
(2001) students have to finish one grade per year after beginning 9th
-grade in order to
satisfy school graduation requirements. Each state has leeway to modify this definition
slightly, but federal graduation guidelines mandate that a student must finish high school
in four years in order to be considerate a graduate. When traditional methods do not
provide opportunities for at-risk students to pass classes and earn credits toward
graduation, educators must implement innovative solutions consistent with student needs.
President Obama’s Blueprint for Reform (2009) supports NCLB graduation requirements,
but emphasizes the need for innovative solutions to facilitate these demands. Online
credit recovery is one innovative and modern way to address the high school dropout
crisis (A Blueprint for Reform, 2009; Patrick & Powell, 2; Watson & Gemin, 2008;
Watson, Gemin, Ryan, & Wicks, 2009).
Online learning for K-12 students is growing in the United States, and is
presenting students in danger of dropping out of high school with new opportunities to
succeed. Examples range from virtual schools, which are entirely online institutions of
learning, and single-class supplements to traditional instruction (Patrick & Powell, 2009;
Roblyer, Davis, Mills, Marshall, & Pape, 2008; Watson & Gemin, 2008). The vast
majority of research involving online learning in the K-12 sector examines virtual schools

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and distance learning courses geared toward nonurban students with technological
knowledge (Means et al. 2009; Patrick & Powell, 2009). Many online programs are
beginning to address at-risk students suffering from credit deficiencies by providing
alternatives to traditional classroom-based learning in which this population of students
has not experienced success (Watson & Gemin, 2008). Credit deficiency is one of the
many qualities associated with the term “at-risk,” which also includes socioeconomic
status, ethnicity, gender, attendance rates, and academic background. This study will
examine patterns of credit recovery among at-risk students across four high schools as
well as how certain characteristics of students classified as “at-risk” interact with specific
qualities of the e2020 online credit recovery program. “At-risk” will be defined
according to California Education Code (2009), which explains that students classified as
such must have at least three of the following characteristics: (a) irregular attendance, (b)
past record of underachievement, (c) past record of low motivation or school disinterest,
(d) socioeconomic disadvantage, (e) a score of below or far below basic on the math or
English state standardized test, and/or (f) a grade point average of 2.2 or below. This
study will examine the viability of online credit recovery for a sample of students all
previously classified as at-risk by the aforementioned criteria and who have not
experienced success with other interventions.
Online credit recovery programs have the potential to increase the engagement
and achievement of at-risk students through technological interaction, personalization,
and feedback (Biesinger & Crippen, 2008; e2020, 2009; Watson & Gemin, 2008). If
online credit recovery programs are successful in providing socioeconomically and

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academically disadvantaged at-risk students with opportunities to recover credits and thus
to avoid dropping out, online credit recovery opportunities must continue to spread across
public schools with low graduation rates. This study explores the potential of one online
credit recovery intervention in providing at-risk students with the ability to achieve
academic satisfaction and success.
Background of the Problem
If immediate interventions do not successfully address the current dropout rate,
the consequences will continue to cripple our economy and our society (Bridgeland et al.,
2006). The economic, social, and health consequences of dropping out of high school are
too severe to ignore. Not only do high school dropouts have a much lower chance of
being employed, but also when they are employed, they often have the lowest paying jobs.
The average joblessness rate for young high school dropouts ages 16-24 during the 2006-
2007 year was 54% (Stillwell, 2009). In addition to economic consequences for the
dropouts themselves, the American economy and society as a whole pays a great price.
During the 2006-2007 year, American taxpayers paid for incarcerating high school
dropouts at a rate 63 times higher than those of four-year high school graduates (Center
for Labor Market Studies, 2009). If the current dropout rates do not dramatically
decrease, researchers estimate the lifetime economic losses of dropouts to be $2.1 billion
in California alone (California Dropout Research Project, 2009). The dropout problem in
America is also a serious health issue. People in the United States with less education
have more health issues than their educated peers and education seems to be the strongest
correlate to health. This relationship derives from lack of access to health care, which

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increases with lower levels of education (Freudenberg & Ruglis, 2007). Additionally,
Liem et al. (2001) found that severe mental health consequences relate to dropping out of
high school. Dropouts demonstrated higher levels of depression and anxiety, and more
mental health issues than peers who had graduated from high school. Dropping out of
high school negatively affects the dropouts and society overall in terms of personal health,
access to jobs, and economic success. Students at risk of dropping out desperately need
and deserve interventions to help them avoid the negative consequences of dropping out.
Students at risk of dropping out of high school experience intense academic challenges
and low levels of engagement and interest (Bridgeland et al., 2006). Students in the
greatest need of support in the form of additional explanation, resources, or teacher
assistance often suffer in the traditional classroom due to excessive numbers of students
and an inability to interact successfully with learning materials. Online learning has the
ability to provide the exact personalization and individualized instruction suited for at-
risk students (Watson & Gemin, 2008).
Statement of the Problem
Although research involving K-12 online learning has explored various purposes
and populations, there is still a dearth of research involving online learning with the
specific purpose of credit recovery for at-risk students (Stillwell, 2009; IES, 2009).
Online learning is growing rapidly in the K-12 sector (Bonk, 2009; Patrick & Powell,
2009; Watson et al. 2009) but has been most prevalent in higher learning, where the bulk
of research has been conducted (Means et al., 2009). Previously, Cavanaugh et al. (2005)
published a meta-analysis focused on K-12 distance learning and found that virtual

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classrooms produced comparable or better levels of student achievement than similar
traditional classrooms. These results, however, were exclusive to virtual classrooms and
were not exclusively concerned with at-risk students. Patrick and Powell (2009) revealed
similar results in examining the effectiveness of online learning. Online learning was
found to have as good as or better outcomes, but these results were also not exclusively
concerned with at-risk students.
Extensive research has explored the status of virtual schools and the growth of
online learning in the K-12 population (Means et al., 2007; Watson & Gemin, 2009;
Watson et al., 2008) but little exploration has concerned online learning with students at
risk of dropping out of high school. The need for research has been established, but the
evidence is lacking. Online learning holds great potential for all students (Bonk, 2009;
Watson et al., 2009) but research is needed to establish how the online format interacts
specifically with at-risk students. This study will address that gap.
Purpose of the Study
This study will analyze patterns of credit recovery in one online credit recovery
program (education2020 [e2020]), designated for credit-deficient high school students
across four high schools in one district. Additionally, the relationship between at-risk
student characteristics and specific components of the e2020 online credit recovery
program will be examined to determine whether any of these factors predict success
and/or satisfaction with the program. Possible connections between at-risk student
characteristics and online credit recovery will be explored in order to determine whether
online learning is a viable option for this specific population of students and how at-risk

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students respond to e2020. Students from four high schools in one school district were
designated “at-risk.” This study will examine credit recovery by total credits earned in
one 10-week period, as well as patterns of credit recovery as related to academic
behaviors including previous credit history and grades in concurrent face-to-face classes.
Additionally, at-risk students’ satisfaction with e2020 will be analyzed to determine
whether any relationship exists between at-risk students’ demographic characteristics and
their overall satisfaction with the e2020 online credit recovery program.
This study will explore the following research questions:
1. What are at-risk students’ patterns of credit recovery in the online credit recovery
program, e2020? Do certain at-risk student characteristics predict satisfaction with
education2020?
Theoretical Orientation
This study will examine student experiences in the online credit recovery program
known as education2020. This relationship among student, learning environment, and
behavioral outcome can be explained through a social-cognitive lens (Bandura, 1978.
Figure 1, below, demonstrates how Social Cognitive Theory (SCT) can be applied to at-
risk students’ experiences in online credit recovery.

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Figure 1. Social Cognitive application
Student involvement in the online credit recovery program will be explained
through engagement theory, which specifically addresses the use of technology to guide
instruction and enhance student involvement (Kearsley & Shneiderman, 1998; Toms &
O’Brien, 2008). Social Cognitive Theory used in conjunction with Engagement Theory
can best explain how the technological, media-rich learning environment interacts with
students’ personal characteristics to produce specific outcomes, such as high satisfaction
levels and the ability to succeed in a technological learning environment (Bandura, 2001;
Kearsley & Shneiderman, 1998). Kearsley and Shneiderman (1998) have explained how
technology can engage students and facilitate learning, whereas Toms and O’Brien
(2008) have described this process of engagement as it relates specifically to
technological interaction. Engagement Theory presents a framework for viewing and
understanding technology-infused education. It draws from many other educational
frameworks such as Constructivist Theory and Situated-Learning Theory because it is
At‐Risk Students
(Socioeconomically disadvantaged, ethnic
minorities, credit de7icient)
Student Experiences
(Patterns of credit
recovery, satisfaction)
Online Credit
Recovery
(Techonogical interaction,
self‐paced, personalized,
immediate feedback)

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centered on the learner’s personal experience, interaction with material and collaboration
with others, as well as on project-based learning (Kearsley & Shneiderman, 1998).
Although Engagement Theory includes more peer interaction and has a project-oriented
focus, for the sake of application to online credit-recovery, the specific area of focus and
application will emphasize student interactions with technological learning. Central to
Engagement Theory is the belief that engagement and learning are inseparable and that
technology is an invaluable tool in stimulating engagement and learning due to its ability
to inspire the user’s attention and interest (Toms & O’Brien, 2008). Technology can
engage the at-risk learner, who will interact with the online environment and theoretically
have a more positive, attention-grabbing, and interested learning experience. The
environment is directly focused on helping the student regain credits to facilitate
graduation through technological interaction with a personalized online credit recovery
program. The person is the at-risk learner, who is in need of credits because he/she has
failed enough high school courses to be considered “at risk” of dropping out. For the
purpose of this study, the behavior is expressed through students’ average pace of credit
recovery and satisfaction with the online credit recovery program.
Bandura’s (2001) Social Cognitive Theory of mass communication focuses on the
interaction between the individual and the networked environment with its specific ability
to provide authentic incentives through media and technology, as well as to provide
personal guidance. Social Cognitive Theory (Bandura, 1978, 1986, 2001) used in
conjunction with Kearsley and Shneiderman’s (1998) Engagement Theory creates a
comprehensive framework for understanding how the at-risk learner can benefit from

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online learning. The technological environment will facilitate student engagement and
therefore produce successful learning outcomes as measured through credit recovery and
course satisfaction. Additionally, integral to the personalized nature of online learning is
the need for self-regulation. Caprara et al. (2008) and Bandura (2001) integrate self-
regulation with self-efficacy in emphasizing the agentic perspective of Social Cognitive
Theory and one’s “self-regulatory efficacy” in preventing students from dropping out of
high school. Understanding online learning for at-risk students will utilize this integrated
framework, which synthesizes Engagement and Social Cognitive Theory as each relates
to technological learning, as well as to the individual and his/her interaction with the
online credit recovery program, e2020.
Significance of the Problem
President Barack Obama, Secretary of Education Arne Duncan, and Bill Gates all
advocate online learning as a progressive means of assisting struggling students and of
facilitating learning with 21st
century tools (Bill and Melinda Gates Foundation, 2010;
CDE, 2009; U.S. Department of Education, 2010). In the 21st
century, a high school
diploma is the door to a college degree and to more opportunities for employment. If
online learning can benefit at-risk students and efficiently address the dropout crisis in
America, immediate implementation of online dropout prevention interventions must take
place.
Methodology
This study will describe patterns of credit recovery among students designated as
at-risk for dropping out of high school due to credit deficiency. Descriptive and

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correlational analyses will reveal patterns of at-risk students’ credit recovery in one
academic 10-week quarter and how specific academic behaviors interact with students’
patterns of credit recovery. A correlational design will also be used to examine the
relationship between at-risk student characteristics and level of satisfaction in
education2020. This examination of how at-risk student demographic characteristics
interact with the e2020 online credit recovery program will reveal the degree to which all
students positively experience the program as measured by their satisfaction with certain
program components. Bridgeland et al. (2006) have found that high school dropouts
ranked boredom and being too behind in credits toward graduation highest on the list of
reasons for dropping out. If students at risk for dropping out are satisfied with e2020 and
feel positively about their ability to recover credits, they are more likely to remain in the
course and recover credits toward graduation.
Limitations of the Study
Although the two research questions are distinct, a possible or implied connection
may still exist between students’ ability to recover credits successfully and their level of
satisfaction with the online credit recovery program. Unfortunately, certain limitations
restricted the possibility of connecting the two to determine whether student satisfaction
with specific components of the education2020 online credit recovery program predicted
credit recovery success. This limitation existed because of changes in student sample
across the district, the time in which the data was gathered, access to e2020 data, and the
need to de-identify student data. De-identification of student data made it more difficult,
but not impossible, to connect student satisfaction to credits recovered. Changes in

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student enrollment and differing times in which data was gathered made it impossible to
connect students involved in the satisfaction survey to those examined in the credit
recovery data. Although the majority of students who responded to the e2020 satisfaction
survey were from the same sample of credit recovery data, enough change occurred in
enrollment and survey responses to prevent the possibility of connecting the two.
Additional limitations involved the sample of students. The study involved at-risk
students from one Los Angeles-area high school district and, although variability existed
among the four high schools studied, all students came from a similar area and shared
similar at-risk characteristics, such as SES, ethnicity, and academic backgrounds. All
students were preselected by each school site administration based upon specific at-risk
criteria including total credits and attendance. Students were identified based upon need
and predicted completion potential, as determined by prior attendance rates and total
number of credits. Students enrolled in education2020 were required to satisfy certain
requirements as a means of maximizing potential for academic success. This specific
group of at-risk students was possibly more likely to succeed than other more heavily
truant and credit-deficient peers. Generalizing to other at-risk students may pose a risk if
the credit, attendance, and grade differential is much greater than this established
threshold. This selectivity may limit generalizability. If certain students were selected
based upon their projected ability to succeed in the program, these selection parameters
may influence the potential for success in education2020. Additionally, only 11th
and
12th
-grade students were able to enroll in the education2020 online credit recovery
program because of the need to recover credits most urgently in order to graduate on time.

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This exigency may have also influenced how the students perceived the program and
were motivated to succeed.
With regard to examining the average pace of credit recovery, data gathered was
available from all school sites but more detailed data used to examine the relationship
between academic behaviors and credit recovery patterns were only available from one
site. This factor may affect the generalizability of results due to variability in student
performance among sites. A larger, more representative sample would provide more
insight into the relationship between academic behaviors and pace of credit recovery.
Definition of Key Terms
Academic Achievement: This term is defined as completing the expected level of
credits per semester to be on track for graduation (30 credits per semester).
At-Risk Students: "At-risk" students means students enrolled in high school who are at
risk of dropping out of school, as indicated by at least three of the following criteria:
a. Past record of irregular attendance.
b. Past record of underachievement, in which the student is at least one year behind the
respective grade level.
c. Past record of low motivation or a disinterest in the regular school program.
d. Disadvantaged economically. (CDE, 2009)
e. A score of below or far below basic on the math or English state standardized test,
f. A grade point average of 2.2 or below
Credit Deficient: Any student who is at least one year behind (60 credits) in
academic progress toward graduation will be placed in this category.

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Credit Recovery Program: This term includes any educational program with the
central intent of assisting students in finishing classes and gaining credits toward
graduation (Watson & Gemin, 2008).
Dropout: This term refers to a student who left high school between the
beginning of one year and the following year without earning a diploma or taking an
equivalency test degree (NCIS, 2009).
Economically Disadvantaged: Any student who receives or is eligible for free or
reduced lunch falls under this category.
Education2020: This online program was established in 1998 and is offered online for
the sole purpose of providing secondary students with additional educational
opportunities (education2020, 2008).
Face-to-Face Learning: This term is defined as a teacher-student in-person
interaction and may be used interchangeably with “traditional” classroom.
Online Learning: This term will be used interchangeably with “e-learning” and
“virtual learning,” and includes a variety of platforms and instructional structure, all of
which are delivered via the Internet.
On-Track Pace of Credit Recovery: In order to earn 220 credits for graduation, students
must earn 55 credits per year, or 27.5 per semester to be considered on track.
Satisfaction: This term represents a level of positive response to experiences in the online
credit recovery program as measured by a Likert scale ranging from satisfaction to
dissatisfaction.

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Traditional Classroom: This definition includes a room of 20-40 students, teacher(s), and
classic learning materials such as a pen, pencil, and book, and may be used
interchangeably with “face-to-face” classroom.
Chapter One Summary
The current economic situation in the United States has been difficult for all
Americans, but those who have dropped out of high school face even greater challenges
(Labor Center for Market Studies, 2009). American public high schools must provide at-
risk students additional opportunities to recover the credits necessary for graduation.
Online options may positively affect at-risk students and enable them to gain confidence
to do what they were not able to in the traditional classroom (Watson & Gemin, 2008).
Understanding the relationship between at-risk students and the online format will
provide insight into the great potential for preventing students from dropping out of high
school.
Organization of the Study
Chapter One introduced the study and established the background to the problem,
statement of the problem, study’s purpose, research questions, study’s significance,
methodology, and study’s limitations, and has defined key terms.
The remaining four chapters of this study will explore the problem, purpose,
significance, related literature, and methods used in this study. Chapter Two will review
the relevant literature in four separate sections. The first section will provide an
overview of the dropout problem, analysis of causes and consequences, and a description
of common characteristics of high school dropouts. The second section of Chapter Two

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will explore the background and description of online learning in the United States, as
well as its potential for addressing the high school dropout problem through online credit
recovery. This second section will also examine several examples of online credit
recovery programs, which illustrate how online learning might benefit students at risk for
dropping out. The final section of Chapter Two will review education2020, the online
credit recovery program being examined in the study. Chapter Three will outline the
research model including sampling, control variables, independent variable, description
of the dependent variable, and then the proposed data collection and analysis techniques.
Chapter Four will present the results of the study and details of the data analysis. Finally,
Chapter Five will present data and analysis as well as implications and recommendations
for future research.

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Chapter Two
Literature Review
The following literature review will examine (a) the high school dropout epidemic
in America, (b) the growth of K-12 online learning, (c) online credit recovery, and (d) the
online credit recovery program education2020. Dropout information will help explain
high school dropout characteristics and the effects of this epidemic in the United States.
Research related to the consequences of dropping out of high school will contextualize
the importance of addressing this social issue through educational interventions such as
online credit recovery (Watson & Gemin, 2008). Examining research surrounding
dropout causes and characteristics will create a high school dropout portrait, which will
explain who the at-risk student is in order to examine how his or her characteristics may
interact with online learning. Online learning is a general term used to describe a
plethora of educational platforms accessed via the Internet (Watson, 2008; Watson et al.,
2009). Reviewing the literature related to online learning and specifically to online credit
recovery for at-risk students will detail how students have experienced online learning
and the degree to which they have found academic success. Finally, education2020
research (e2020, 2009) will provide information about the online credit recovery program,
which at-risk students used in this study. Discussion of several aspects of the program,
including its instructional foundation and format, will outline specific ideas about what
the at-risk students in the study experienced (e2020, 2009). Reviewing the literature
related to the high school dropout crisis, online learning, and education2020 will establish
a foundation of understanding the variables of the study, which include the characteristics

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of students at risk for dropout and academic success in the education2020 online credit
recovery program.
The Dropout Crisis
The American dropout epidemic can be viewed in relation to several student
characteristics. Disparities are found across ethnicity, socioeconomic status, and gender
lines (Cataldi, Laird, KewalRamani, & Chapman, 2009; CDE, 2010; Center for Labor
Market Studies 2009; Laird, Kienzl, DeBell, & Chapman, 2009). A multitude of
demographic and academic qualities are associated with dropping out of high school,
such as gender, ethnicity, socioeconomic status (SES), academic background, and other
psychosocial issues (Battin-Pearson, Newcomb, Abbot, Hill, Catalano, & Hawkins, 2000;
Bridgeland et al., 2006). In the United States, and specifically in California and Los
Angeles, dropout rates are alarming, as are their extreme consequences such as
unemployment, homelessness, and incarceration (CDRP, 2008). Socially, economically,
and health-wise, high school dropouts experience great challenges in this country, and
even more so in certain populations, communities, and school types than others. High
school dropouts are disproportionately from a minority background and have lower
socioeconomic status (Bridgeland et al., 2006; Freudenberg & Ruglis, 2007; Labor
Center for Market Studies, 2009; Liem et al., 2001). Although calculating and
understanding the dropout rates in America can happen many ways, for the sake of this
study, dropout rates will be understood in terms of both the national event dropout rate,
which measures the rate at which students in America left school without a successful
outcome as represented in a high school diploma, and the national status dropout rate,

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which includes students who may or may not have been enrolled in U.S. schools but do
not attend school and do not have a high school diploma (National Center for Education
Statistics, 2009).
Dropout Rates
Dropouts can be categorized by several common characteristics related to
demographics, academic background, and psychosocial issues. The highest dropout rates
occur among African Americans or Latinos living in low socioeconomic areas and who
have academic difficulties and low levels of school engagement (Battin-Pearson et al.,
2000; IES, 2009; Labor Center for Market Studies, 2009). According to the Labor Center
for Market Studies (2009), during a national study in 2007, two out of every five Latino
males and one in every four African American males aged 20-24 had dropped out of high
school.
California Department of Education (CDE) data (2009) also reveal troubling
patterns in dropout rates by race/ethnicity and socioeconomic status (SES). Latino and
African American students of lower socioeconomic status drop out of high school at
higher rates than White and Asian students of similar SES backgrounds (CDE, 2009).
Rates across the United States reveal the same unequal patterns. (See Figures 2-3)

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Figure 2. Event dropout rates of 15- through 24-year-olds who dropped out of grades 10–
12, by family income: October 1972 through October 2004
Figure 3. Event dropout rates during 2008 by ethnicity and gender
Large numbers of Latino, African American, and American Indian/Alaska Native
students are not attending school, engaging in the material, or achieving academically and
are thus dropping out at higher rates than their White and Asian peers (CDE, 2009). As

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Bridgeland, DiIulio, and Burke Morrison (2006) found, dropping out of high school does
not result from a specific incident, but rather from a long process of disengagement.
Students slowly become disengaged and disconnected, fall behind academically, and are
consequently unable to pass classes (Battin-Pearson et al., 2000). As a result, they fall
behind in credits and are then unable to catch up. Hampden-Thompson, Warkentien, and
Daniel (2009) found a pattern of developing credit gaps across grade-level dropouts.
Students who were deficient in credits each year continued to fall behind. The gap was
compounded until it finally led to the choice to drop out of school. (See Table 1).
Table 1
Average Course Credit Accrual of Spring 2002 10th-Graders, by Academic
Year, Subject, and High School Status: 2004
Academic Year (AY) Subject (AY 2000–02)
Status in 2004 2000–01 2001–02 Total English Mathematics Science
Dropouts 5.1 4.6 9.7 1.7 1.3 1.2
On-time graduates1
6.6 6.7 13.3 2.1 2.0 1.8

22
The recent growth of online learning used specifically for credit recovery is a new
approach to addressing the dropout epidemic. Online credit recovery may increase the
total number of credits recovered by at-risk students due to the possibility of recovering
credits more quickly, which can ultimately decrease the number of students dropping out
of high school in the United States. Watson and Gemin (2008) examined several online
learning programs, ranging from virtual schools to supplemental educational
opportunities in various school districts across the country, and found high levels of
success among certain online learning formats.
According to the National Center for Education Statistics (NCES) (2009), over
one-third of Latinos ages 16-24 born outside the United States had not completed high
school with a diploma or earned a Graduation Equivalency Diploma (GED) in 2007-2008
and were not enrolled school. The gender disparity also indicated that more males than
females drop out of high school (NCES, 2009). The changing cultural atmosphere in
America—and more dramatically in certain areas due to immigration, social issues, and
economic changes—has affected where people live, which schools they attend, and how
they live. According to Neild and Balfanz (2006), communities with the highest levels of
academic failure lack an ability to address at-risk students’ needs. These communities
are in urban areas and are composed of disproportionately large numbers of low-income
ethnic minorities. The schools within these communities also have the highest
concentration of dropouts (Neild & Balfanz, 2006). High schools in urban
neighborhoods enroll students with the greatest number of multiple risk factors for
dropping out of school, such as low SES, ethnic minority background, and previous

23
academic challenges (Kao & Thompson, 2003). The American high school dropout rate
is unacceptable for many reasons, but above all it is a moral issue. Poor, disadvantaged
Black and Latino teenagers in America are not graduating at rates comparable to their
higher SES peers, and immediate steps must be taken to reverse this destructive and
unethical trend. The No Child Left Behind (NCLB) Act of 2001 mandated increasing
academic achievement in several areas, including graduation rates. Certain ethnic groups
were targeted in terms of schools having to make group-specific gains based upon
numerically evidenced low achievement levels. Unfortunately, many schools continue to
fall short of NCLB expectations and are not meeting graduation requirements (CDE,
2009; CDRP, 2009; IES, 2009). California Department of Education (CDE) data reveal
further troubling figures reflective of the high concentration of dropouts in certain areas
and schools. The 100 schools in the state with the highest dropout rates account for only
4% of all high schools in California, but in the year 2005-2006, these 100 schools had
41% of the state’s high school dropouts (CDE, 2009).
Dropout Consequences
The decision to dropout of high school and end one’s education before earning a
diploma invariably contributes to debilitating social, economic, and health conditions in
this country; high school dropouts have higher rates of unemployment, homelessness, and
mental and physical health problems (Bridgeland et al., 2006; Freudenberg & Ruglis,
2007; Labor Center for Market Studies, 2009; Liem et al., 2001). The dropout epidemic
in America has long been understood as a social and economic issue (Labor Center for
Market Studies, 2009). The Centers for Disease and Control Prevention (2007) also

24
presented the American high school dropout epidemic as a public health issue.
Americans with less education are more likely to have health problems than their more
educated peers. This reality presents issues for the suffering individual, as well as social
issues due to the high numbers of this population without health insurance (Freudenberg
& Ruglis, 2009).
Eliminating educational disparities is necessary to having a positive effect on
current health disparities in America. According to Freudenberg and Ruglis (2007), the
American dropout rate should be understood as an urgent health issue because individuals
without high school diplomas are much more likely to develop severe mental and
physical health problems, suffer throughout life dependent upon drugs, and/or die at an
earlier age. More education often brings more economic success and, consequentially,
more access to health care. Having a high school diploma may be the first step in
preventing unnecessary economic and health challenges.
Relatedly, having a high school diploma is associated with higher rates of
employment and lower rates of incarceration. Stillwell (2009) found extreme differences
between high school dropouts’ and graduates’ joblessness and incarceration rates.
Research indicated a 54% joblessness rate for high school dropouts in America (Stillwell,
2009). If an individual in 2008 had not graduated high school, he/she was more likely to
be unemployed than to have a job. Unemployment contributed to the high levels of
dropouts classified as living in poverty. High school dropouts were four times as likely
to be living below the poverty line than their counterparts with college degrees (Stillwell,
2009; Sum, Khatiwada, McLaughlin, & Palma, 2009). Teen pregnancy rates also

25
increase among high school dropouts. Of all the single mothers in America during the
year 2006-2007, 22.6% was high school dropouts (National Labor Market Statistics,
2009). Female high school dropouts were also found by the National Labor Market
Statistics (2009) to be six times more likely than college graduates to have had children
when they were in their teens. Incarceration rates in 2006-2007 also indicated gender,
ethnic, and academic disparities; it was more likely for an ethnic minority male dropout
to be in jail than his high school graduate counterparts (Sum et al., 2009). Chronic
incarceration was much more likely to occur among high school dropouts than college
graduates and the highest rate of incarceration rates for dropouts is among young Black
men (Center for Labor Market Studies, 2009).
In California, the state with the greatest number of high school dropouts in the
nation, the rates are alarming. A study by the California Dropout Research Project
(2008) revealed very disturbing high school dropout numbers. In the year 2006-2007,
12,367 students in Los Angeles dropped out of middle or high school, and during the
same year, 13,174 students graduated. The same study found extreme economic
differences in how high school dropouts and college graduates lived life and affected
society. Americans without high school diplomas suffered challenges in job
opportunities and earning power (Center for Labor Market Studies, 2009; Sum et al.,
2009).
Dropout Causes and Characteristics
Most high school dropouts fall into specific categories or typologies. Many
theories have sought to explain the process behind dropping out of high school (Battin-

26
Pearson et al., 2000). Some theories utilize various typologies that categorically describe
the reasons for not finishing high school (Janosz, LeBlanc, Boulerice, & Tremblay, 2000).
The categories are generally based on school behavior, classroom engagement, and
academic achievement. For the sake of this study, these characteristics will be
categorized into demographic information and academic background.
According to a longitudinal study completed in 2002 by the National Center for
Education Statistics (NCES), the top two reasons why students dropped out of high
school were that they did not like school and/or they were failing school. As previously
illustrated, the majority of these students were ethnic minorities from lower
socioeconomic communities attending schools with high dropout rates (Cataldi et al.,
2009; CDE, 2010; Center for Labor Market Studies 2009; Laird et al., 2009). According
to a study of high school dropouts, Bridgeland et al. (2006) found that students dropped
out due to several risk factors, but the top factors were not being interested in classes and
failing in school. Students also reported a lack of motivation and inspiration. From the
students’ perspectives, this disengagement and academic failure directly contributed to
dropping out of school (Bridgeland et al., 2006). The need to address these challenges is
immediate and can potentially happen with the technological resources afforded to us in
the 21st
century. Online learning for at-risk high school students is a modern, adaptable,
and efficient means of providing opportunities to learn, recover credits, and graduate
from high school. Students in the 21st
century are saturated with technology, yet schools
often present an approach to learning that does not include the technology to which
students, and specifically high school students, are accustomed. Online learning may

27
provide this technologically rich learning experience. If at-risk students are more
engaged, inspired, and satisfied with their experiences with online learning, there is great
potential for stemming the dropout crisis in the United States.
K-12 Online Learning
The Obama administration has put forth its Blueprint for Reform (2009), an
outline for educational improvement in America. This report places strong emphasis on
choice and opportunity, and one example includes online learning programs in low-
performing schools. This national mandate establishes the importance of providing
students with more opportunities to succeed, especially students with the greatest need
due to at-risk characteristics and/or a history of academic failure. The Blueprint for
Reform (2009) also declares its commitment to providing additional support and giving
priority to schools successfully utilizing technology to address student needs ( ABlueprint
for Reform, 2009).
Background. Watson and Gemin (2008) recognized the rapid growth of online
learning over the last decade and especially since 2006, at which time 36 states had
already established state-run online learning programs. With this great growth came the
need to assess the success of online learning in order to identify what components are
most beneficial and replicable. Christensen and Horn (2008) support students learning at
their own pace and in the digital format that is becoming more and more pervasive and
preferable to younger learners. Bonk (2009) has emphasized the importance and
potential of online learning in K-12 education through his detailed exploration of the
many possibilities of online learning. Many examples, such as the Indiana University

28
High School (IUHS), provide students with opportunities to learn and succeed
academically outside of the constraints of the traditional classroom. IUHS began in the
late 1990s and now has almost 4,000 students taking classes online. According to Bonk
(2009), students’ varied backgrounds and experiences contributed to their decision to
enroll in online courses, and the flexibility and individualization enables them to succeed.
Students at IUHS range from incarcerated teens to those hoping to graduate early; many
have been accepted to prestigious universities across the country (Bonk, 2009).
Online format. Online learning can be understood in terms of teaching, learning,
and the environment in which each occurs. Teaching occurs many ways in the online
format. Information is disseminated in many ways; one is through a virtual teacher,
which is an actual teacher represented virtually in video format. The teacher’s
responsibilities can be broken down into four categories: course development,
communication, guidance, assessment, and professional development (Watson & Gemin,
2008). Online learning is often a blend of synchronous learning, occurring at one time,
and asynchronous communication, which can be done at different times. Synchronous
tools include examples such as webcams, text chatting, and Internet communication.
Asynchronous tools often utilized in online learning are e-mail and threaded discussions.
Content is delivered in a variety of formats, which are usually dictated by the course itself.
English may have text with audio, lectures, and graphic organizers, whereas science
classes will commonly have lab simulations and video links (Watson, 2008).
Wood (2004) traced the development of virtual schools from 1997 when the
Florida Virtual School (FLVS) was established. Although many different online formats

29
exist—from specialized courses to entirely online schools—successful components of
online learning have been identified as being immediate in that the learner can get
information at any time, personalized in that the learner can modify learning to fit needs,
and interactive due to technological feedback and the role of learner in constructing
knowledge (Hannum & McCombs, 2008; Weiner, 2003). The virtual experience can
essentially serve as a personal tutor for students. Among the several characteristics of
online learning programs is individualization, which best facilitates learning.
Bonk (2009) examined the growth of FLVS from its inception, which consisted of
several dozen students in 2007, at which time over 52,000 students grades 6-12 were
enrolled. The success of FLVS has been attributed to several factors. According to
Bonk’s (2009) analysis of several facets of the program, FLVS has been successful due to
its “rigor, depth, innovation, and quality” (p.109). Students are responsible for their own
academic growth and decisions, and inadvertently increase their literacy skills as a result
of the reading and writing that occurs in the online environment. Students are used to
reading and writing in e-mails, texts, social networking, and web-browsing, so literacy
naturally increases when literacy skills and practice are embedded within the online
format (Bonk, 2009). Research on virtual school has supported students’ ability to
succeed in this virtual world of learning.
Patrick and Powell (2009) examined the effectiveness of K-12 online learning in a
meta-analysis of various virtual school-based data. Their study focused on comparisons
of virtual students’ experiences with those in traditional, face-to face classrooms, and
found that the virtual students outperformed or equaled traditional classroom students due

30
to the personalized and enhanced instruction of the virtual format (Patrick & Powell,
2009).
Means et al. (2009) analyzed the effects of many online learning programs and
found support for online learning as a valuable alternative to face-to-face education.
Their study also identified gaps in evidence of online learning producing better outcomes
than traditional classroom results. Means et al. (2009) and Watson (2008) found the K-
12 area needing further research.
Hannum and McCombs (2008) emphasized the importance of learner-centered
qualities in online instruction. For students to succeed in online learning, learning must
occur for a specific purpose, at the learner’s own pace, and with sufficient feedback and
support (Hannum & McCombs, 2008). Weiner (2006) echoed this principle in an analysis
of adolescents’ experiences in a virtual learning program. Adolescents, as do most K-12
students, benefit from consistent feedback and supplementary activities when necessary.
Without additional assistance from a teacher, the benefits of online learning are more
difficult to achieve (Weiner, 2006).
Online learning for at-risk students. The rapid growth in K-12 online learning
has stimulated development in many specialized areas, which include additional
opportunities to gain credits toward high school graduation. The bulk of online learning
studies have examined the effects of online learning for various populations, and there is
a great need for studies focused entirely on students at risk for dropping out. It is
necessary to examine how online learning can benefit specific populations such as
students at risk of dropping out of high school.

31
Thomas (2008) has established the importance of learner-centered principles for all
students, but most specifically for low-performing students. Enabling students to
construct their own knowledge, embrace individual learning styles, and engage in
material with intrinsic motivation are essential facets of a learner-centered instructional
program. Online learning has the ability to be specialized, self-paced, and naturally
engaging for students of the 21st
century due to its familiar and accessible nature.
Thomas (2008) found that students responded positively to information presented in
online courses, and to the online format overall. Over 70% of 2,000 high school and
middle school students reported positive responses to online courses (Thomas, 2008).
Watson and Gemin (2008) have examined how technology and online access can
reinvent at-risk students’ learning experiences. Their study was part of a series that
explored successful educational practices such as flexibility, individualization, and
diagnostic testing. The researchers outlined several ways that online learning can benefit
high school students in danger of dropping out. The most direct means of preventing
students from dropping out is to provide opportunities to regain credits toward graduation.
Online credit recovery gives students who had previously failed the courses necessary for
graduation the ability to regain credits in significantly less time. Because they had
already earned “seat-time” students are able to work at their own pace and recover credits
at a much faster pace than in the traditional classroom. According to Watson and Gemin
(2008), the most successful credit recovery programs are those most learner-centered. If
at-risk students with severe credit deficiencies are able to personalize their learning

32
through class choice, pace, and practice, they succeed by recovering credits and
progressing toward graduation.
Online credit recovery examples. Watson et al. (2009) identified credit
recovery programs as the greatest area of growth in K-12 online learning in the United
States. This specialized, purpose-directed instruction provides students with personalized
opportunities to earn the credits necessary to graduate. Among credit recovery programs,
district online supplemental courses, virtual schools, and the myriad options for K-12
courses, several characteristics facilitate learning. Watson et al. (2009) outlined 10
categories of online learning characteristics, which included comprehensiveness, reach,
type, location, delivery, operational control, type of instruction, grade level, teacher-
student interaction, and student-student interaction. These categories will be used to
describe and understand education2020 in comparison to other K-12 online learning
programs. Watson (2008) described successful online learning programs in the following
way:
They are teacher-led, with extensive interaction between teachers and students,
and often between students. Because the teachers are so closely involved,
students find that it is not easy to cheat in an online course. Given that online
courses are so interactive, and full-time programs provide opportunities for
students to interact in person, online students are not isolated, but instead can
focus on learning and socializing at different times. (p. 4)
Many misconceptions exist about online learning, such as the impression that it is nothing
more than reading information of a computer. According to Watson (2008), this
interpretation is inaccurate. Online learning is intended to be highly interactive and
learner-centered through the delivery of material, availability of content, communication,
and individualized learning and assessment.

33
Many examples from districts such as Los Angeles Unified School District
(LAUSD) in California and Volusia County Schools in Florida have demonstrated how
online learning can provide students with the opportunities to succeed where they have
often failed in traditional face-to-face classrooms (Watson & Gemin, 2008). LAUSD
credit recovery courses have demonstrated success through their blended format and
differentiated instruction. The blended format enables students to learn online while
accessing face-to-face support. Each course is individualized through diagnostic tests,
which determine the student’s skill level and then begins instruction from that determined
place, instead of requiring students to repeat material (Watson & Gemin, 2008). LAUSD
uses a blended approach to providing students with teacher interaction and support. This
approach is also present in the Volusia County School where teachers create
individualized student plans and monitor progress (Watson & Gemin, 2008). The self-
paced and flexible nature of these online credit-recovery courses enable students to be
responsible for their own learning. With the online format, the student is able to take
ownership of learning in that he/she can progress at a personal pace and receive constant
feedback. This format, however, is only possible with specific educational and
pedagogical structures and does not occur solely because of the technological format
(Aronson & Timms, 2004; Hannum & McCombs, 2008; Watson, 2008). Specific
instructional strategies and systems must be in place to ensure the greatest levels of
academic success and the most positive student experiences. Archambault et al. (2010)
found that certain online program qualities assist at-risk students and foster greater levels
of success. One central component is the use of teacher support and guidance, as well as

34
specific instructional strategies that enable the at-risk learner to learn at a personalized
pace and to access extra assistance when necessary. The following section will analyze
one specific online credit recovery program in depth to provide an example in action.
Education2020 is an online learning program dedicated to utilizing specific strategies to
encourage student learning; it is used by schools around the country to provide students at
risk for dropping out an opportunity to recovery credits online.
Education2020
Education2020 (e2020) is founded on Universal Design for Learning (UDL),
Quality Standards for Online Courses in accordance with the Southern Regional
Education Board (SREB), state and national standards, measurable objectives, Bloom’s
taxonomy, and a combination of behavioral and cognitive learning theories. There are
three central principles, within which there are several guidelines (e2020, 2010).
Principle 1 involves representation and mandates the use of various means, including
alternate information displays, various forms of content, text with sound, symbols, and
visual aids. Principle 2 recognizes the need for a variety of activities that engage and
support student learning. Web Reader software, lab simulations, eResources, and
eWriting all provide students with instructional options so they can learn by choice.
The goal of e2020 is to create pedagogically sound content for delivery over an
online medium in order to address variability in student learning styles, thereby
producing a dynamic (i.e., adaptable) 21st
century educational product for students
in grades 6-12. (Foundations of e2020, 2010)
The dynamic and adaptive nature of e2020 increases personalization and choice because
the content and instruction change to meet the needs of the learner. This philosophy is

35
consistent with beliefs held by online credit recovery advocates that format is a promising
means of providing self-paced and personalized ways of learning and getting back on
track toward graduation (Watson & Gemin, 2008)
E2020 foundation. Education2020 (e2020) incorporates many of the
aforementioned strategies to facilitate the highest levels of student success. Education
2020 is founded on the principles of Universal Design for Learning (UDL), which
incorporates three methods of flexible and individualized learning. The first component
of UDL emphasizes multiple representations of information through varying formats and
media. The second allows for multiple means of student actions of understanding and
interaction with material. The third kind of UDL flexibility focuses on engaging and
motivating students through multiple pathways (Rose & Meyer, 2002). Education2020
utilizes these principles in its platform features and course structure. The e2020 platform
enables school district, school site, and individual course instructors to customize content
delivery and assessment, and students are able to bypass material they have already
mastered through diagnostic testing, personalized passing thresholds, and a variety of
assessment options. The e2020 course structure incorporates Universal Design for
Learning (UDL) principles through several additional methods. Students are presented
with information and able to interact through direct instruction, lab simulations, and a
variety of activities including projects, journaling, design proposals, and content reading.
These technologically driven, multiple modalities facilitate varying levels of motivation
and engagement.

36
Additionally, e2020 students are able to personalize learning through study plans
with prescriptive materials used to supplement curriculum when mastery has not been
achieved. Also, several forms of random assessment help determine students’ progress
toward mastery. Students receive consistent feedback as they interact with materials and
lessons. If e2020 students are unable to understand material or feedback, they receive
opportunities to gain additional support through teacher-tutorials and supplemental
media-rich sites where they can practice and apply course concepts and lessons
(Foundations of e2020, 2010).
E2020 case studies. Education2020 (e2020) has demonstrated success in several
schools across the country. According to the National Dropout Prevention
Center/Network (2008), many reasons explain why e2020 has done well in its effort to
provide learning opportunities for students at risk for dropping out. Education2020 has
been effective due to presenting experienced teachers in the virtual world, as well as to
using interactive technology and fostering problem-solving skills. Several case studies
have demonstrated this success.
In 2006-2007, over 300 students in an Albuquerque, New Mexico school district
were enrolled in the e2020 program. Instead of taking core classes such as English, math,
and science in the traditional classroom, the e2020 virtual classroom was available inside
and outside of school, so students could work at their own pace. At the end of the 2006-
2007 school year, all students (those in e2020 courses and in traditional classrooms) took
the New Mexico High School Standards Assessment (NMHSSA). In comparing e2020

37
student scores to those of the state average, e2020 students achieved higher levels of
success across all disciplines.
An additional case study examined an e2020 credit recovery program in a
Michigan school district consisting of six middle schools, four high schools, and two
alternative programs. Data reveal successful rates of completion and credit recovery,
which assisted students in getting back on track toward graduation or in graduating on
time. From 2002 to 2007, e2020 students accomplished a 90% course completion rate.
In the summer of 2004, e2020 was used in a large, urban school district in west
Texas for 184 students attempting to take courses needed for graduation. Ten percent
was classified as special needs students. The rate of course completion for general
students was 97% and the rate for special needs students was 78%, which was nearly
double the average rate in previous traditional summer classes. Due to the summer
success, the district implemented the e2020 program in the fall, spring, and summer.
Over 80% of e2020 students were successful in gaining credits toward graduation.
A fourth case study examined student achievement in a virtual summer school
program in Hawaii that provided additional credits or credit recovery for 600 high school
students. Education2020 partnered with several high schools to create specialized
courses aligned with Hawaii state standards and intended to provide opportunities for
gaining credits toward high school graduation. This program achieved great levels of
success, with a 98.5% completion rate (Foundations of Education2020, 2010).

38
Summary
The high school dropout crisis can be addressed with online credit recovery
programs, which directly deal with students’ need to accrue credits at a pace consistent
with high school graduation. Education2020, one example, presents students with the
potential for credit recovery. Students at risk of dropping out of high school may or may
not find success in education2020 due the relationship between the online credit recovery
format and at-risk student characteristics.

39
Chapter Three
Methods
In order to describe how at-risk students interact with the education2020 (e2020)
online credit recovery program, one must examine specific student demographic
characteristics and their impact on students’ experiences in e2020. The vast majority of
K-12 online research has looked at the results of students in virtual schools and programs
not specialized for at-risk students (Patrick & Powell, 2009). Isolating this subgroup is
vital because the specific characteristics of at-risk learners may interact differently with
the online format. In order to address the high school dropout crisis in the United States,
it is necessary to determine the potential success of certain intervention programs,
including the growing area of online credit recovery. Although online learning may be
appropriate for students with a history of academic success, at-risk students enrolled in
online programs must also be examined to determine how the unique characteristics of
this population interact with the e2020 online credit recovery program.
This study examined patterns of academic success as indicated by credit accrual
rates. Data gathered describes patterns of credits earned by at-risk students enrolled in
the online credit recovery program, education2020, as well as the relationship between at-
risk student characteristics and their satisfaction in the e2020 online credit recovery
program. Student characteristics included demographic classifications such as
race/ethnicity, gender, grade level, age, and socioeconomic status (SES). Student
satisfaction was measured by responses to survey questions involving experiences in
e2020. This study was both descriptive and correlational in nature; students’ credit

40
recovery was described and the relationship between at-risk student characteristics and
their satisfaction in online credit recovery program, education2020, was examined to
determine whether specific characteristics predicted satisfaction with the program.
Research Questions and Hypotheses
This study will explore the following research questions:
1. What are the patterns of credit recovery by at-risk students in education2020?
2. Do certain at-risk student characteristics predict satisfaction with education2020?
Population and Sample
A large number of students are in danger of dropping out of high school in the
United States each year (Cataldi et al., 2009; IES, 2009). These students are defined as
“at-risk” due to a number of factors related to psychosocial issues, academic performance,
and myriad qualities related to school experiences and performance. This at-risk
population unfortunately consists of a disproportionate number of African American,
Latino, and low socioeconomic status teenagers (Labor Center for Market Studies, 2009).
This study examined the population of students considered at risk for dropping out of
high school in the United States; the sample was representative of this population due to
demographic characteristics and credit deficiency rates.
The sample was drawn from The California Union High School District
(CUHSD). All students came from the education2020 online credit recovery program
used by the district, which consisted of three comprehensive high schools and one
continuation school. The district identified students as possible candidates for the credit
recovery program due to their extreme credit deficiencies. Students were, on average, at

41
least one grade (60 credits) behind. As a condition for participating in the program,
students were required to have at least 90 credits at the beginning of the online program.
All students identified by CUHSD were juniors, seniors, or second-year seniors. The
sample included students from each of the four schools, which resulted in approximately
200 students.
Site
The California Union High School district is composed of four schools. Each
school is similar in ethnic and SES make-up, but Academic Performance Index (API)
scores vary. H1 High School (HHS) had an API in 2010 of f 635, L1 High School (LHS)
had an API of 576, and W1 High School’s (WHS) API was 729 (California Department
of Education, 2009). The fourth school (O1) was not required to partake in standardized
testing.
Across the California Union High School District, the average dropout rate for
2008-2009 was 35.9% (CDE, 2010), which means that between three and four of every
10 students in the CUHSD dropped out of high school. Considering that the entire
district is comprised predominately of low SES, ethnic minority students, CUHSD
dropout rates are representative of dropout trends across the United States (Center for
Labor Market Studies, 2009). To redress these alarming statistics, CUHSD acquired the
education2020 credit recovery program.
Education2020 was adopted by CUHSD for all four schools, with variation by site
in terms of implementation. Two of the four schools followed a block schedule, which
meant that six classes met every other day for two hours daily. One of the schools was

42
on a traditional schedule of six classes meeting daily for one hour each. The fourth
school was a continuation school that followed an independent schedule in which
students were able to work at their own pace for four hours daily. The e2020 classes
corresponded with the schedule, but students were enrolled in a variety of ways.
Depending on the level of credit deficiency, and how many courses CUHSD determined
were needed to get the student back on track toward graduation, a certain number of
e2020 courses were assigned daily. Some students were enrolled in a one-hour or one-
block period, and spent the remainder of the day in traditional face-to-face classes,
whereas other students were assigned to a full day of e2020 courses.
This study followed the interaction between students and e2020 based on the
pace of credit recovery within one quarter, student satisfaction levels, and the relationship
between student characteristics and e2020 satisfaction. Satisfaction levels were gauged
for all students with no separation of daily number of classes; all students enrolled in one
or six courses of e2020 were given the opportunity to take the survey. The online credit
recovery pace took into account a number of periods per day in order to analyze the pace
of credit recovery per period in one quarter.
Data Collection and Instrumentation
This study occured following the completion of one e2020 quarter, which is 10
school weeks. The online program was offered in semester periods and all data was
collected in during the Spring 2011 semester—the second semester CUHSD had utilized
e2020. Data was collected through a deidentified survey given by the district and
included demographic data such as race/ethnicity, gender, age, and SES. Deidentified

43
teachers’ progress grades provided access to academic results in education2020 including
pace of credit recovery. Student responses to e2020 and experiences were collected from
a district-implemented survey, which was useful for gathering information anonymously
from multiple participants (Cresswell, 2003). The survey revealed information about
students’ experiences within the program, as compared to experiences in traditional
classes. The survey was not tested for reliability or validity. It was administered to
gather information about students’ experiences and satisfaction with the program. (See
Appendix C). Data for this study was collected from these two preexisting sources;
CUHSD student surveys and teacher-documented credit recovery progress.
Data Analysis
Data synthesis and analysis included descriptive and inferential statistics to reveal
student satisfaction and credit recovery experiences in e2020. Descriptive statistics
explained any patterns in credit recovery pace as they related to student academic
background and to face-to face experiences concurrent with enrollment in education2020.
Anova and Chi-square analyses examined whether demographic characteristics and
academic behaviors revealed a connection between these characteristics and the pace of
credit recovery. This analysis helped illustrate not only whether students at-risk for
dropping out of high school were able to recover credits at a faster rate than those in
traditional face-to-face classes, but also which behaviors had a relationship to credit
recovery pace. For instance, 12th
-grade students failing face-to-face government may be
less likely to recover credits at a rate considered successful by the California Union High
School District (CUHSD). According to the CUHSD policy on expected pace of credit

44
recovery, a student must earn credits at double the rate of the traditional face-to-face
classroom pace. In a standard, one-hour class per semester, a student is expected to pass
the course and earn five credits in one semester. The CUHSD education2020 student is
expected to pass one class in a quarter, and two in a semester per one standard period.
This study provided information about at-risk students’ patterns of credit recovery in
education2020.
A hierarchical linear regression analysis examined student survey responses,
providing information about the potential relationship between student characteristics and
overall satisfaction with e2020. A regression analysis was chosen in an effort to
determine whether all categories of at-risk students were equally satisfied with e2020 or
if certain categorical characteristics predicted satisfaction with the program. Examples of
student characteristics included gender, ethnicity, grade level, and school site.
Education2020
CUHSD acquired education2020 to address the large numbers of students with
credit deficiencies, circumstances that often result in dropping out (IES, 2009). The
education2020 online credit recovery program was chosen as an intervention targeting
students deemed at risk for dropping out or not graduating on time. CUHSD provided
students with two blocks of three periods to complete classes. Students could enroll in
one or both blocks, depending on need. E2020 classes cover the spectrum of classes
required for high school graduation, from science and social studies to math and a broad
range of electives.

45
The California Union High School District allocated rooms equipped with
individual computers for students and one teacher desk. Students were assigned an
individual log-in at the site computer and each had personal headphones. They worked at
their own pace on a variety of courses required for high school graduation. All
coursework was taken online, with digital teachers and digital content and assessment.
Students read materials online, listened to lectures, and completed activities and exams.
Students in e2020 began each course with a diagnostic exam that placed them in the
appropriate level within the course. Students were able to demonstrate competency so
material would not be repeated. This feature very well may have facilitated a faster rate
of credit recovery for some students.
Chapter Two Summary
The education2020 program in the California Union High School District
presented students with the opportunity to recover credits at a faster pace than they could
in the traditional classroom. Examining at-risk student characteristics, such as academic
behaviors and patterns of credit recovery, in e2020 provided insight into the potential of
the online credit recovery program to help the population of high school students
identified as at risk for dropping out. Additionally, student characteristics may or may
not have had a relationship to satisfaction with the education2020 online credit recovery
program and the program may or may not have been received positively by students. If
student characteristics, including SES, ethnicity, age, grade level, educational
classification, and academic setting revealed no relationship to their ability to interact
positively with an online credit recovery program, and students had a predominately

46
positive experiences with the program, then online credit recovery programs such as
education2020 would hold great potential for the addressing the high school dropout
crisis.
This quantitative study will answer the following research questions:
Is there any relationship between student characteristics and rate of credit recovery in
education2020?
A correlational analysis of quarter progress data and student demographic and
academic characteristics revealed any relationship between variables. Anova and Chi-
square analyses examined whether demographic characteristics such as age and ethnicity
and academic behaviors—such as prior credits earned and pass rates in concurrent face-
to-face classes—correlated with pace of credit recovery in education2020.
Do certain at-risk student characteristics predict satisfaction with education2020?
Student characteristics examined included race/ethnicity, gender, age, grade level,
educational status, and socioeconomic status. A hierarchical linear regression analysis
determined how each at-risk student’s demographic information interacted with overall
satisfaction with e2020.

47
Chapter Four
Data Analysis
The purpose of this study was to examine patterns of credit recovery by students
at risk for dropping out of high school due to severe credit deficiencies as well as to
determine any relationship between specific student characteristics and overall pace of
credit recovery Additionally, this study sought to ascertain the degree to which at-risk
student characteristics predicted satisfaction with the online credit recovery program,
education 2020. Results of this quantitative correlational study are enumerated below.
All credit recovery data was gathered from the California Union High School District’s
(CUHSD) records tracing courses completed in education2020. No grades will be
discussed; academic results will be measured solely in terms of credits earned.
Correlational data used to examine the relationship between student
characteristics and student satisfaction were gathered and measured by a 23-question
survey. (See Appendix C). Demographic-based questions were closed-ended and
comprised nine of the total 23 questions. The subsequent 13 questions utilized a seven-
point Likert scale related to students’ experiences in e2020 and to their overall level of
satisfaction with categories, including interaction (three questions), course content (four
questions), self-efficacy (four questions), and technological personalization (two
questions). The last question was an optional, open-ended qualitative question that was
skipped by all students. The second set of data gathered consisted of quarterly credit
accrual data from a group of seniors at one school site during quarter three of Spring
2011, An analysis of mean credit recovery provided insight into at-risk students’

48
expedited ability to earn credits toward graduation. Further analyses considered patterns
of credit accrual as they related to students’ academic characteristics, such as history of
credits earned and concurrent passage rates in face-to-face classes. Through correlational
analysis, this study provided information about at-risk student demographics and their
relationship to satisfaction in the e2020 online credit recovery program, as well as about
pace of credit recovery to regain an opportunity to graduate.
Demographics
The majority of survey respondents were seniors, due their high enrollment rates
in education2020. Other students, labeled senior+, had missed graduation the previous
year due to a limited number of classes and thus were eligible for education2020. Some
juniors were also able to recover credit in order to get back on track for their senior year.
Of those indicating a race of multiethnic, the majority (87.0%) indicated that they were
Hispanic in addition to one or more other races (Native American 40.0%, Asian 10.0%,
African American/Black 45.0%, PI 15.0%, White 10.0%). The great majority (83.9%) of
students indicated that they were eligible for free or reduced lunch, which reflected the
disadvantaged socioeconomic status of students enrolled in the education2020 online
credit recovery program; this statistic was ethnically and socioeconomically consistent
with the overall dropout rates in the United States (CDE, 2010). Although students
enrolled in education2020 were more evenly distributed across the district by number of
students enrolled at each site, survey responses were uneven, for reasons unknown. Of
218 respondents, over 55% was from one of the four schools, thus there was unequal
survey response. The great majority came from L1. The students in this sample came

49
predominately from the general education population, which may be due to the district’s
student enrollment selection. The great majority (96.3%) of respondents identified
themselves as nonspecial education, which could be largely due to the district’s selection
of students for the program. These demographics are presented in Table 2, below.

50
Table 2
Demographic Characteristics of e2020 Satisfaction Survey Respondents
Demographics Descriptive Statistics a
n
220-218
Sex
Male 123 (56.2%)
Female 96 (43.8%)
Race
American Indian or Alaska Native 0 (0.0%)
Asian 2 (0.9%)
Black or African American 28 (12.7%)
Hispanic 152 (69.1%)
Native Hawaiian or other Pacific
Islander
3 (1.4%)
White 11 (5.0%)
Multiethnic 23 (10.5%)
Other 1 (0.5%)
Grade Level
Junior 69 (31.5%)
Senior 140 (63.9%)
Senior+ 10 (4.6%)
Age
16 52 (23.7%)
17 127 (58.0%)
18 37 (16.9%)
19+ 3 (1.4%)
Educational Classification
General Education 210 (96.3%)
Special Education 8 (3.7%)
Free/Reduced Price Lunch
Yes 183 (83.9%)
No 35 (16.1%)
Number of e2020 Courses Enrolled In

51
1 109 (50.2%)
2 37 (17.1%)
3+ 71 (32.7%)
School
O1 13 (5.9%)
L1 121 (55.3%)
W1 51 (23.3%)
H1 34 (15.5%)
Data Cleaning
Before survey results were analyzed, some areas were in need of data cleaning.
After that point, the survey responses were analyzed using principle components factor
analysis and hierarchical linear regression modeling. Data cleaning revealed several
examples of ambiguous responses that required attention, such as selection of more than
one age category. Responses for the items in questions were not included in the data
analysis. In each instance the subject’s other responses were examined for invariant or
nonsensical response patterns, but in the absence of these indicators of careless or
disingenuous answers, all subjects in the sample were kept. Ultimately no individuals
were removed from the sample, so the final dataset contained at least partial information
from 220 e2020 students.
In addition, upon review of the survey administered the text anchors used on the
13 items assessing satisfaction with e2020 courses were discovered to be in error.
Specifically, the anchors “satisfied” and “somewhat satisfied” were reversed. In order to
control for any confusion resulting from the text anchors, the scales were collapsed from
seven-point scales to five-point scales. (See Figure 4, below)

52
Figure 4. Collapsed satisfaction scale
Further examination of the 13 e2020 items revealed an additional problem. For
several students, multiple answers were provided. As a result, upon creating a scale based
on these 13 questions, only 159 respondents (72.3%) had data suitable for scale creation.
Ultimately a scale comprised of all 13 items was made by synthesizing the
individual e2020 satisfaction items, which created a single variable with possible scores
ranging from 13-65. Application of Cronbach’s alpha showed a high degree of internal
consistency (α = .91), with single-item deletions making unnoticeable changes to the
alpha value. Though the alpha value observed was high, this result may have been partly
due to alpha’s tendency to increase based on the number of items used in a scale’s
creation.
Principle Components Factor Analysis
A principle components factor analysis (PCA) was conducted on the 13 e2020
satisfaction items considered in this study, with plans to implement orthogonal rotation
(varimax). The Kaiser-Meyer-Olkin measure confirmed that the sample utilized was a
dataset appropriate for PCA (KMO = .96 [“superb,” according to Hutcheson & Sofroniou,
as cited in Field, 2009]). Bartlett’s test of sphericity (x2
(78) = 2657.37, p < 0.001)
determined that the correlations between the 13 e2020 items were of enough significance

53
to allow for examination with PCA. Prior to rotation, eigenvalues of the unrotated matrix
were examined, yielding only a single factor with an eigenvalue greater than 1. This
single factor, in turn, explained a total of 77.25% of the observed variance. The single
factor solution was confirmed through examination of a scree plot, with inflexion
showing only a 1- or 2-factor solution proving appropriate. Given that factors with
eigenvalues of less than 1 are often no better than individual questions (Valente, 2002)
and that the factor loadings for the individual items ranged from .78-.92, the decision was
made to retain a single factor comprised of all 13 e2020 questions. All 13 questions
answered in the survey contributed to the single factor of satisfaction with education2020.
Satisfaction has been examined in relation to the various facets of online learning,
including social presence, types of interaction, instructional structure, and environment
(Bollinger & Martindale, 2004; Hughes, McLeod, Brown, Maeda, & Choi, 2007; Jung,
Choi, Lim, & Leem, 2002; Richardson & Swan, 2003). Although satisfaction has been
measured in various ways, for the sake of this specific study, satisfaction was understood
as one general concept related to students’ experiences in the online credit recovery and
to their subjective ratings of program components. The Likert scale in the 13-question
survey progressed from dissatisfied to satisfied, measuring students’ overall level of
satisfaction with areas ranging from interaction to course content.
Taking this overall concept of satisfaction into consideration, all 13 items were
retained as suggested initially by the PCA. In order to discuss the results, this 13-item
scale was synthesized and discussed as a global measure of e2020 satisfaction. To
establish student level of satisfaction, pre-existing district survey questions examined

54
various components of e2020 and students were required to rate their experiences with
each. Together, these questions contributed to an overall level of satisfaction with the
e2020 online credit recovery program.
Findings
The goal of this study was to explore patterns of credit recovery in one online
credit recovery program adopted by one small high school district. Additionally, a
correlational approach examined possible connections between students and experiences
with education2020. In order to best ensure success in online credit recovery, it is
important to determine whether certain students experience the program more positively
than others. Examination of student satisfaction in an online learning program is integral
to understanding the potential for success; students who express higher levels of
satisfaction are more likely to perceive higher levels of learning, complete online
programs, and achieve at higher rates (Herbert, 2006; Hughes et al., 2007; Jung et al.,
2002). The following findings reveal consistent levels of satisfaction among at-risk
students in education2020, with specific variability residing only in the area of learning
environment. All at-risk student characteristics were equally likely to predict satisfaction
with e2020, but satisfaction levels varied by school site.
Research Question #1
Is there any relationship between student characteristics and rate of credit recovery in
education2020?
The data used to answer this question came from one school site at the quarter
grading period in Spring semester 2011. This school had the most readily available and

55
accessible course completion data. Student characteristic data came from seniors at one
school site.
At this point in the traditional classroom credit recovery pace, students would be
expected to have completed half of their five-credit classes. Cataldi et al. (2002)
portrayed the importance of credit accrual pacing in preventing students from dropping
out of high school and many studies of online learning illustrate the value of providing
students an opportunity to work at their own pace (Aronson & Timms, 2003;NACOL,
2010; Watson & Gemin, 2008). In e2020, some students were able earn credits at a faster
rate than they had accomplished in the traditional classroom, but variability occurred in
the credit accrual rate. The data collected came from block-period classes, which were
three periods; thus, students who completed three e2020 courses in one quarter completed
courses in half the time of the traditional classroom, which placed students on track with
one course per period, per semester.
At the end of the quarter, many students had completed several classes, and some
had hardly finished one. In order to examine the overall pace of recovery and compare it
to the traditional classroom pace of recovery, credit recovery means were used as
comparison. Utilizing data from n = 67 seniors at one site, students recovered, on
average, a total of 2.25 (SD = 1.86) credits per three-hour block in one quarter.
In the traditional classroom, students are expected to complete 1.5 credits per
three-hour block, or half a class per hour based on the on-track pace of credits toward
graduation as 55 credits per year. In one quarter of education2020, students were, on
average, able to complete .75 more credits than in the traditional classroom, but in order

56
to actually recover lost credits and simultaneously earn credits for the current academic
schedule, they were expected by the California Union High School District (CUHSD) to
recover at least one course per quarter. Out of 67 seniors at one site, great variability
occurred in the number of courses completed. This finding reflects that site differences
may be related to personal learning factors, and not to demographics or learning
environment. An exploration of one potential connection is represented below. The 67
seniors whose patterns of credit recovery were examined had each been enrolled in high
school for the three previous years, but had varying levels of credit deficiency.
Additionally, all students examined were concurrently enrolled in three face-to-face
classes. These two academic areas, related to past and present credit accrual, were
analyzed for potential relationship to credit accrual in education2020. (See Table 3,
below).
Table 3
The number of credits completed was moderately, positively associated with the number
of e2020 courses completed. Similarly, the number of classes one was failing was
moderately, negatively associated with the number of credits completed. The more face-
to-face classes one failed while enrolled in education2020, the fewer courses he/she

57
would have completed. Students who had more credits also had higher rates of success
with credit recovery in e2020. In addition, students who were failing face-to face-classes
were less successful in recovering credits. Not surprisingly, students with a greater
number of credits were likely to earn more and to find success in face-to-face classes as
well as in e2020. The more interesting finding here was the last of the significant
findings. There was a modest, negative association between the number of e2020 courses
completed and the number of concurrent face-to-face classes failed. Senior students with
severe credit deficiencies, who were enrolled in three periods of e2020 and who were
earning credits at the highest rates were concurrently failing their face-to-face courses at
the lowest rates. This phenomenon could be explained by many personal factors, such as
self-regulation and resolve, but there is also the potential for transfer of skills or academic
self-efficacy from success in e2020. The implications and suggestions for future research
will be further discussed in Chapter Five.
Upon additional examination of credit accrual patterns among L1 seniors enrolled
in education2020, an interesting frequency of failure in face-to-face classes emerged.
This finding could be explained by the frequency in enrollment, or by the literacy
demands of the courses, English and government, but the most frequently failed face-to-
face classes among this group of students were the two with the greatest literacy demands
and the two required for graduation. One in three senior L1 students concurrently
enrolled in education2020 and face-to-face classes was failing English and/or
government; of the 67 e2020 students examined, 49 students were failing English and/or
government. This finding is interesting due to the great literacy demands of both courses,

58
and the similar literacy requirements in education2020. As previously mentioned,
students with the highest credit accrual rates also had the lowest failure rates in face-to-
face classes. Table 4, below, illustrates the patterns of failure among L1 seniors enrolled
in education2020.
Table 4
E2020 Students’ Concurrent Face-to-Face Class Failure Rate
Course Number
Failing
English 25 (37.3%)
Government 24 (35.8%)
Geometry 14 (20.0%)
PE 3 (4.5%)
Weight Training 3 (4.5%)
Algebra 2 3 (4.5%)
Biology 2 (3.0%)
Spanish 2 2 (3.0%)
Chemistry 1 (1.5%)
Physical Science 1 (1.5%)
Additional analyses of student face-to-face failure rates illustrated interesting patterns,
which may suggest a possible relationship between face-to-face failure rates and
education2020 credit accrual success. The strongest correlation existed between failure
in geometry and P.E. classes and lack of education2020 credits accrued. (See Table 5).
Coming to any serious conclusions is difficult due to the low numbers of students
examined, but this finding is worth exploring further with a larger sample.

59
Table 5
Predicting Student Success (# of credits completed) by Courses Failing
Variable B SE B β
Step 2 (final model)1
Geometry -36.50 8.41 -.47***
PE -42.58 16.53 -.05*
Note. R2
= .22 for Step 1 (p < 0.001); ΔR2
for Step 2 = .07 (p < 0.05)
* p ≤ 0.05. *** p ≤ 0.001
This analysis shows us that although they were the most often failed courses in this
sample of seniors, face-to-face English and government courses were not the strongest
predictors of student failure (as measured by fewer total credits completed). Based on this
model, the best estimation of the total number of credits seniors would have completed
was 36 credits lower for students who were failing face-to-face sections of geometry
versus those who weren’t. Similarly, students who were failing PE would have completed
42 fewer credits than those not failing PE in their senior year. Though these relationships
were most likely not causal, this information could be useful for identifying the highest
risk students in the future and for providing support in specified areas outside e2020, to
ensure graduation support from as many sources as possible. Even if students recovered
credits successfully in e2020 online courses, they were still required to pass face-to-face
classes simultaneously in order to achieve the ultimate goal: avoiding dropping out and
potentially graduating from high school on time.

60
Research Question #2:
Do certain at-risk student characteristics predict satisfaction with specific components of
e2020?
Hierarchical linear regression modeling was conducted to determine whether
student characteristics predicted satisfaction with the e2020 curriculum. The variables
were entered into a stepwise building model in four blocks. The first block of the model
consisted of gender, age, educational status, and free lunch. Due to the categorical nature
of the remaining predictors, each was dummy-coded and force-entered into the model in
separate blocks. As a result, blocks 2-4 consisted of race, school location, and grade level,
respectively. This hierarchical linear regression analysis demonstrated that the student
characteristics examined didn’t seem to predict their satisfaction with the e2020
curriculum. The first stepwise block was eliminated, so the demographic variables
included were ethnicity, school site, and grade level. Each characteristic had an equal
opportunity for satisfaction, and no characteristic predicted a significant level of
variability in level of satisfaction. If a relationship between any of the variables had been
discovered, it would have been important to understand why specific characteristics
predicted greater satisfaction in e2020. This result, however, did not occur; results
demonstrated that variability in satisfaction was reflected only by school site. Although
specific student characteristics did not predict satisfaction with e2020, the difference in
satisfaction by school site supported the need to delve deeper into the differences in
students and school learning environment by site. Table 6, below, represents student
demographic differences by site.

ADDRESSING THE HIGH SCHOOL DROPOUT CRISIS AT-RISK STUDENTS AND EDUCATION2020 ONLINE CREDIT RECOVERY

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