1. Self-Regulated Learning and Success in an Online Developmental Math Community
College Course
Dissertation Manuscript
Submitted to Northcentral University
Graduate Faculty of the School of Education
in Partial Fulfillment of the
Requirements for the Degree of
DOCTOR OF PHILOSOPHY
by
Nancy Chatteinier
Prescott Valley, Arizona
October 2016

2. Abstract
This causal-comparative (ex-post facto) quasi-experimental study investigated
two questions regarding institutional support provide to community college
developmental math courses. The first attempted to ascertain whether institutional
support for SRL behaviors and skills increases student retention in community college
online and F2F developmental math courses. The second examined if the effects of
institutional support of self-regulated learning behaviors and skills on retention rates
differ between online and F2F courses. The population for this study consisted of
students in developmental math courses offered a California Community College in Fall
of 2014 and Fall of 2015. Fall of 2015 is when the California Community College first
introduced instructional support of SLR in the form of WebAssign. The statistical result
of logistic regression showed no significant increase in student retention status pertaining
to instructional support. The model explained 12.2% of the variance in retention status (p
> .01). Recommend a replication of the current study be conducted, but after the program
has existed over an extended period of time. Also, it is recommended that additional
criterion variables such as student and teacher attributes be included. Also
recommendations for future research include performing a quantitative longitudinal study
to investigate the progression of developmental math students in the program to
determine whether they continue to that first credit-bearing math course and a qualitative
study examining those learners who were not retained.

3. Acknowledgements
I acknowledge with great respect and admiration my dissertation committee chair,
Dr. Gregory Caicco, and my committee members, Dr. David Thomas and Dr. Rollen
Fowler. Dr. Caicco has been a constant and guiding star through this journey. Thank you
for your time, efforts, advice, guidance, input, warnings, suggestions, recommendations,
and instruction. This was a meaningful collaboration for me, and I could not have done
this without Dr. Caicco. Thank you.
I would like to also acknowledge Dr. Adam J. Fowler, J. Blake Knight, and Dr.
Steve Watts for their excellent advice and editing help.
Finally, I would like to thank my family, my children Kelly, Suzy and Tim, my
son-in-laws, the Mikes, and my grandson Damen for all their support and love throughout
this journey.

4. Table of Contents
Chapter 1: Introduction....................................................................................................... 1
Background ................................................................................................................... 2
Statement of the Problem.............................................................................................. 5
Purpose of the Study..................................................................................................... 7
Theoretical Framework................................................................................................. 9
Research Questions..................................................................................................... 19
Significance of the Study............................................................................................ 22
Definition of Key Terms............................................................................................. 23
Summary..................................................................................................................... 24
Chapter 2: Literature Review............................................................................................ 26
Documentation............................................................................................................ 26
Self-Regulated Learning ............................................................................................. 28
Models of Self-Regulated Learning............................................................................ 31
The Self-Efficacy Effect on SRL................................................................................ 33
The Adult Learner and College Readiness ................................................................. 36
Community College Role in Developmental Education............................................. 39
Assessment Procedures to Determine College-Level Readiness................................ 47
Developmental Math: Help or Hinderance? ............................................................... 50
Student Challenges in Online Courses........................................................................ 57
The Importance of STEM Occupations ...................................................................... 60
Competition for STEM Graduates .............................................................................. 62
STEM Initiatives......................................................................................................... 63
Summary..................................................................................................................... 66
Chapter 3: Research Method............................................................................................. 68
Research Methods and Design.................................................................................... 69
Population ................................................................................................................... 74
Materials/Instruments.................................................................................................. 75
Operational Definition of Variables............................................................................ 77
Data Collection, Processing, and Analysis ................................................................. 78
Assumptions.............................................................................................................. 790
Limitations .................................................................................................................. 80
Delimitations............................................................................................................... 81
Ethical Assurances ...................................................................................................... 82
Summary..................................................................................................................... 82
Chapter 4: Findings........................................................................................................... 85
Results......................................................................................................................... 86
Evaluation of Findings................................................................................................ 90
Summary..................................................................................................................... 93
Chapter 5: Implications, Recommendations, and Conclusions ........................................ 95

5. Implications................................................................................................................. 97
Recommendations..................................................................................................... 103
Conclusions............................................................................................................... 104
References....................................................................................................................... 106
Appendix A: G*Power Analysis........................................Error! Bookmark not defined.

6. List of Tables
Table 1 Descriptive analysis: Study variables............................................................ 87
Table 2 Omnibus Tests of Model Coefficients ............................................................ 88
Table 3 Model Summary ............................................................................................. 88
Table 4 Classification Table ....................................................................................... 89
Table 5 Logistic Regression Predicting Likeihood of Rentention based on Delivery
Method and Instructional Support .............................................................................. 90

7. 1
Chapter 1: Introduction
A troubling gap between the knowledge and skills of the United States’ current
and projected workforce and the demands of jobs is expected to grow most rapidly during
the next decade (Abel & Deitz, 2012; Carnevale, Smith & Strohl, 2010). President
Obama has called on community colleges to increase science, technology, engineering
and math (STEM) graduates (Obama, 2009). By 2018, 63% of the projected 46.8 million
job openings will require workers with at least some college denoting a change in
American society specifically postsecondary education has become the threshold
prerequisite for access to middle-class (Carnevale et al., 2010; Tüzemen & Willis, 2013).
Student interest in online classes is increasing (Allen & Seaman, 2014; Caruth &
Caruth, 2013). In 2002, 9.6% of all students attending postsecondary institutions in the
United States were enrolled in at least one online course. This figure rose to 33.5% in
2012, suggesting a significant increase in overall online college course enrollment
nationally (Allen & Seaman, 2014). However, concerns exist regarding high rates of
attrition among those taking online courses, particularly students taking online courses at
community colleges (Clay, Rowland, & Packard, 2009; Leeds, Campbell, Baker, Ali, &
Brawley, 2013; Lei & Gupta, 2010). Approximately 15 to 20% of all U.S. undergraduates
are more likely to dropout of online courses compared to face-to-face (F2F) courses
(Pittenger & Doering, 2010; Xu & Jaggars, 2013). Moreover, nearly eight million
individuals – 42% of all U.S. undergraduates – are enrolled in community colleges, and
these students are much more likely to fail to complete their studies than their peers at
traditional institutes of higher education (IHEs) (Knapp, Kelly-Reid, & Ginder, 2012).
Addressing the high rates of attrition among those taking online courses is necessary if

8. 2
community colleges are to reach the President’s goal to produce an additional five
million program completers by 2020, an approximate 50% increase over current levels
(Obama, 2009). Otherwise, the U. S. may lose jobs to other countries and will be at a
disadvantage in the global economy (Obama, 2009; U. S. Department of Education,
2011).
To further explore options for alleviating these high rates of attrition, researchers
have begun to examine the self-regulated learning (SRL) skills and behaviors of students
participating in online courses in particular (Artino, & Jones, 2012; Flowers, 2011; Lee &
Tsai, 2011; You & Kang, 2014). Self-regulated learning is a self-directive process that
involves learners proactively identifying their personal strengths and weaknesses and
monitoring their learning processes. And then honing their methods of learning and
staying motivated by engaging in strategies leading to academic success based on self-
observations (Andrade & Bunker, 2011; Lopez, Nandagopal, Shavelson, Szu, & Penn,
2013). The context-specific nature of academic motivation and its relationship to
perceived importance and usefulness regarding course material and student interests has
been noted (Osborne & Jones, 2011). Also, that task value (the incentive to engage in
academic work perceived as valuable, important, useful, and interesting) may be a key
predictor of engaging in SRL behavior (e.g., Schunk & Zimmerman, 2012).
Background
Legislators, corporate entities, economists and scientists are concerned the
number of STEM graduates in the United States will not meet the needs of the
technology and innovation requirement of tomorrow (Carnevale, Jayasundera & Gulish,
2015; Craig, Thomas, Hou, & Mathur, 2011; Langdon et al., 2011; Obama, 2012; Yi &

9. 3
Larson, 2015). Developmental math courses offered at community colleges prepare
students to take introductory college level mathematics—a prerequisite for most degrees
(Ashby, Sadera, & McNary, 2011; Bailey, Jeong, & Cho, 2010; Xu & Jaggars, 2011).
However, of those students who enroll in a developmental math sequence online, only
20% will complete their first college-level math course compared to 32% who completed
their developmental math sequence in a F2F class (Jaggars, Edgecombe, & Stacey, 2013;
Xu & Jaggars, 2013; Xu & Jaggars, 2011). Educators and policymakers continually
debate the effectiveness of current developmental education offered at community
colleges (Brothen & Wambach, 2012).
Limited studies have been performed to ascertain if developmental education
contributes to the academic success of learners (Bahr 2012; Boudreaux, 2016; Bremer et
al. 2013; Melguizo et al., 2016; Pruett, & Absher, 2015; Weiss et al., 2015). Continuing
from the 1800’s, the debate on the effectiveness of developmental education at the
postsecondary level has persisted (Brothen & Wambach, 2012). Researchers have been
unable to show definitively that developmental courses at the secondary level provide
learners with enriched skills (Bremer et al., 2013; Crisp & Delgado, 2014; Melguizo,
Bos, & Prather, 2011; Scott-Clayton & Rodriguez, 2015). Some areas of the debate are
concerned with where and when to teach postsecondary developmental education
(Wilson, 2012). Several states such as Colorado, Florida, North Dakota, New York,
Louisiana, Tennessee, Missouri, and Minnesota have guidelines preventing four-year
institutions from offering developmental education courses (Jacobs, 2012). However, at
the two-year college level, the states of Nebraska, Virginia, Oregon, and Indiana do not

10. 4
require unprepared students to enroll in developmental education courses but merely
advise students to enroll in developmental education courses (Wilson, 2012).
Additionally, from 2012 to 2014 the amount of students taking at least one course
online increased by 403,420 while overall student enrollment decreased by 421,631
during the same time period (Allen & Seaman, 2015). Some researchers observed a
significant negative effect of taking an initial introductory math class online (Xu &
Jaggars, 2011) conversely, other research found delivery method was significantly
different with online (85% passing), blended (69% passing), and face-to-face (63%
passing) (Ashby et al., 2011). The importance of instructor facilitation in online classes is
a frequent topic in the literature (Baxter, 2012; Bradford, 2011; Hart, 2012; Lee & Choi,
2011; McMahon, 2013; Moore & Kearsley, 2011). Because many online classes are an
asynchronous format, learners reported difficulty when online class designs were not
organized to promote self-directed learning (Kuo, Walker, Belland & Schroder, 2013).
The degree of learner self-management and control of learning are thought to be
significant factors that contribute to readiness for an online class (Yang & Park 2012).
The online student’s self-regulation or self-directed learning skills might influence
readiness for and successful completion of an online class (Eom, 2011; Holt & Brockett,
2012; Michinov et al., 2011). Learner control, described as the degree to which a student
can control outcomes and events, can contribute to the student’s ability to self-regulate
his/her learning (Lee & Choi, 2013). Research is varied as to reasons for learner difficulty
in an online environment.
The online community college learner’s role differs from other roles accustomed
to by learners because of its fluidity and the needed higher degree of self-awareness and

11. 5
reflection (Karp & Bork, 2014). Self-regulation strategies and skills may be essential for
success in the online learning setting (Allen & Seaman, 2014). Those who have a high
degree of social or emotional intelligence may be able to adjust effectually to a college
situation (Reilly et al., 2012; Sparkman et al., 2012). Researchers have found in the
online environment the use of SRL techniques along with high expectancy and self-
efficacy levels often indicated academic success (Cheng et al., 2012).
However, sources remain conflicted as to whether the lack of institutional support
for self-regulated learning within an online delivery method is a key reason for the lower
success rate (Bol & Garner, 2011; Cho & Heron, 2015; Heller & Marchant, 2015;
Hudesman et al., 2014; Mahlberg, 2015; Skinner, Saylors, Boone, Rye, Berry, &
Kennedy, 2015). Some studies show promising results for SRL interventions (Heller &
Marchant, 2015; Hudesman et al., 2014) other studies show no positive effect (Cho &
Heron, 2015; Mahlberg, 2015; Skinner et al., 2015) while another study found results
differed dependent on student characteristics (Bol & Garner, 2011). In order to
successfully address the retention problem in online developmental math courses further
research is necessary to clarify these conflicting results. Failure to address the retention
problem in online developmental math courses will have a negative impact on
communities (Baum et al., 2013 Langdon et al., 2011). Also, the middle class will
continue their decline (Abel & Deitz, 2012; Tüzemen & Willis, 2013; National Science
Board, 2012).
Statement of the Problem
Developmental math courses offered at community colleges prepare students to
take introductory college level mathematics—a prerequisite for most degrees (Ashby,

12. 6
Sadera, & McNary, 2011; Bailey, Jeong, & Cho, 2010; Xu & Jaggars, 2011). Estimates
indicate 60% percent of community college students are referred to a developmental math
course (Bailey et al., 2010) with 7% of those enrollments taking place in online courses
(Xu, & Jaggars, 2014). However, of those students who enroll in a developmental math
sequence online, only 20% will complete their first college-level math course compared
to 32% who completed their developmental math sequence in a F2F class (Jaggars,
Edgecombe, & Stacey, 2013; Xu & Jaggars, 2013; Xu & Jaggars, 2011). The problem is
sources are conflicting as to whether the lack of support for self-regulated learning within
an online delivery method is a key reason for the discrepancy in online and F2F success
rate (Bol & Garner, 2011; Cho & Heron, 2015; Heller & Marchant, 2015; Hudesman et
al., 2014; Mahlberg, 2015; Skinner et al., 2015).
If college retention in STEM courses remains unaddressed, it is estimated by 2018
the United States postsecondary system will fall short by three million in filling the labor
market’s demand for college graduates (Carnevale et al., 2010). Fueling the further
decline of both the United States long-term competitiveness (Landers, 2010; Langdon,
McKittrick, Beede, Khan & Doms, 2011; National Science Board, 2010; President’s
Council of Advisors on Science and Technology, 2012) and the middle class (Abel &
Deitz, 2012; Tüzemen & Willis, 2013; National Science Board, 2012). Additionally,
individuals who fail to complete their postsecondary education are more likely to be
unemployed (Kena et al., 2014; Snyder, & Dillow , 2013), make less money (Baum, Ma,
& Payea, 2013; Johnson, 2015; Wheary & Orozco, 2010), be on public assistance
(Johnson, 2015; Irving & Loveless, 2015), be less healthy (Baum et al., 2013; Cutler &
Lleras-Muney, 2010), or be incarcerated (Mann, Spjeldnes & Yamatani, 2013; Pew

13. 7
Charitable Trusts, 2010; Shutay, Plebanski & McCafferty, 2010), consequently having a
negative effect on the economy (Baum et al., 2013 Langdon et al., 2011).
According to Self-Determination Theory (SDT) (Deci & Ryan, 1985),
developmental math students may be impacted by factors that support or hinder their
interest and motivation. The causes of students dropping out seems to be numerous and
complex (Nichols, 2010); however, student entry characteristics including students’
previous academic experiences and performance, learning skills, self-motivation, self-
efficacy may be among the most distinctive factors (Cho & Heron, 2015; Lee & Choi,
2011). Additionally, researchers have found not improving student’s emotional
experience could lead to diminished motivation, cognitive process and achievement (Kim
& Hodges, 2012; Winberg, Hellgren, & Palm, 2014). Future research needs to examine
success rates in community college-level developmental courses (Ashby et al., 2011). In
particular, future research needs to explore the relationship between online community
college courses and factors such as self-regulation (Hu & Driscoll, 2013), pedagogical
factors, institutional structures, and support structures (Xu & Jaggers, 2011).
Purpose of the Study
The purpose of this causal-comparative (ex-post facto) quasi-experimental
quantitative study was to investigate whether institutional support for SRL behaviors and
skills (i.e., independent variable [IV]) increases student retention (i.e. the dependent
variable [DV]) in community college online and F2F (i.e., independent variable [IV])
developmental math courses. For this study, institutional support is defined as the use of
the program WebAssign Basic College Mathematics: An Applied Approach (WebAssign,
2016) and student retention is defined as receiving grade of A, B, C, D, F, P, NP, I*, IPP,

14. 8
INP, or FW. Based on a G*Power analysis with a two tailed alpha set at 0.05, and power
set at 0.95, a minimum of 721 students was needed to be convenience sampled from
developmental math courses offered through Rio Hondo community college- in
California. There were two groups studied: (a) online courses with instructional support
of SRL behaviors and skills; (b) online courses without institutional support of SRL
behaviors and skills. Secondary archival data for Fall 2014 and Fall 2015 obtained from
the California Community Colleges’ Chancellor Office’s Management Information
Systems (COMIS), also known as Data Mart results, was compared between online
courses with (Fall 2015) and without (Fall 2014) institutional support. A Poisson logistic
regression analysis was planned to be utilized to reveal whether the institutional support
of SRL behaviors and skills has a statistically significant effect on course retention.
However, to insure data assumptions were met to execute a valid Poisson regression
Kolmogorov-Smivnov Test was conducted. The p value generated was 0.00 indicating a
statistically significant result and does not follow a Poisson distribution (Laerd, 2015).
Consequently, a logistic regression was substituted as the statistical test for this study.
Through this study it was hoped that education professionals’ concerned about high rates
of attrition for community college students taking online courses will be able to assist
better and inform this unique subset with a more thorough understanding of practices of
self-learning (Lee & Tsai, 2011; Schunk & Zimmerman, 2012). This study contributes to
the growing literature on the development of online course delivery structures and
informs community college faculties’ pedagogical practices (and, potentially, the
processes and designs of online course developers).

15. 9
Theoretical Framework
The theory of self-determination (SDT), which is a macrotheory of a number of
motivational sub-theories (Deci & Ryan, 1985), serves as the theoretical framework for
this study. The purpose of this causal-comparative (ex-post facto) quasi-experimental
quantitative study is to investigate whether Self-Determination Theory (SDT) predicts
that the independent factor, institutional support for Self- Regulated Learning (SRL)
behaviors and skills, when compared in conjunction with the independent variable,
delivery method for developmental math community college courses contributes
positively to retention rate. According to Self-Determination Theory (SDT) (Deci &
Ryan, 1985), developmental math students may be impacted by factors that support or
hinder their interest and motivation. This section will examine the evolution of
motivation theories, SDT, and SDT uses in education as it applies to the proposed study.
Beginning with Aristotle, the idea of the value of having not only goals, but
correct goals has been described as flowing from the higher principles of greatest well-
being, concern for self-interest in the framework of other as well as self, and the
importance of reaching for one's greatest and highest potential in order to experience the
true meaning of life (Waterman, 1993; Maslow, 1970; Ryan, Sheldon, Kasser & Deci,
1996). Motivational theories offer the necessary structure to help understand and evaluate
an individual’s behavior. Since the work of Sigmund Freud, researchers have been
persistent in pursuing ways to understand what moves individuals to act in certain ways.
Of importance is Malsow’s (1943) hierarchy of needs which has significance to all
disciplines. With physiological needs at its base, this hierarchy develops incrementally
through security needs, social needs, and self-esteem needs, peaks in self-actualization at

16. 10
which point an individual has realized their full potential. This theory demonstrates that
innate individual motivation in its fundamental form is universal; that is, established on
basic human needs motivation has commonality and relevance across all disciplines. Both
Maslow and Freud understood and studied that people possess a duality of nature;
however, Freud's outlook was basically pessimistic, seeing forces for self-destruction
predominant (Freud, 1930), whereas Maslow was an optimist regarding human
capabilities, and concentrated on what he believed was the basic decency and goodness at
the core of humanity (Maslow, 1970). The key problem with Maslow's theory was that it
assumed that all human beings were homogeneous in their behavior, and neglected to
take into account the impact diverse cultures, education and environment have on people.
People do not have analogous ambitions, even those within the identical environment.
Maslow's works chief fault was the lack of empirical data to support his assumptions.
Alderfer (1972) attempted to improve on Maslow’s weakness by supporting his theory
with hard data. He proposed that Maslow’s hierarchy of needs be condensed down to
three which he termed Existence, Relatedness, and Growth and termed the ERG Theory.
Existence needs included all forms of material and physiological desires, and these
contained Maslow’s first two levels and also incorporated money. Relatedness needs
comprised relationship with others, and Growth needs that like Maslow's associated with
the aspiration to be creative and to reach full potential in the current environment.
However, Alderfer rejected the idea of hierarchy and saw them as a continuum, he
believed that two needs could function simultaneously, and considered them
environmental.

17. 11
Building on the work of Maslow and Alderfer, Deci (1971) conducted an
experiment that investigated the effects of external controls and rewards on intrinsic
motivation (IM). The results gave support to the cognitive evaluation theory that
proposed that IM may be affected either through the process of feedback or the process of
change in perceived locus of causality. Responding to the interest in IM, Harter (1978)
began an empirical effort to construct a self-report measure to assess IM in the
elementary school student.
Whereas Harter (1981) proposed that the school culture causes an increasingly
extrinsic orientation and hence a developmental decline in IM, the present study indicates
that such a decline is not necessarily a general trend, but varies with subject area.
Harter’s scale construction studies consisted of over 3,000 pupils located in four states;
Connecticut, New York, Colorado, and California. The instrument Harter created was
found to be a valid and reliable measure sensitive to distinctive variances in both extrinsic
and intrinsic orientation. Additionally, Harter proposed that the school culture produces
an increasingly extrinsic orientation and, therefore, a developmental deterioration in IM.
Building on Harter’s work Gottfried (1985) proposes that such deterioration is not
necessarily a universal trend, but differs with the subject area. He does agree that
educational organizations encourage extrinsic motivation (EM) in conflict to the
preferred IM and recommends that education should encourage intrinsic learning
whenever possible in promoting positive learning behaviors.
More recently, when extrinsic rewards in academic settings were present, students
displayed only minimal effort to acquire the reward (De Castella, Byrne, & Covington,
2013). Similarly, students exhibited a tendency to terminate an activity after eliminating

18. 12
the reward (DeCastella et al., 2013; Lei, 2010). Failing to achieve the reward, high
stress/anxiety, diminished cooperative behavior, and low self-esteem have all related to
adverse effects of utilizing extrinsic rewards in classrooms (Lei, 2010). Regardless of
these drawbacks, extrinsic rewards are still routinely used in educational settings
(Pulfrey, Darnon, & Butera, 2013).
Motivation has been considered as a unitary idea by some theorists differing
primarily in the amount (Bandura, 1996; Baumeister & Vohs, 2007). Additional
motivation, however stimulated, is thought by some researchers to produce more
successful functioning and greater success. Theories that utilize organismic instead of
mechanistic suppositions about people’s nature (Piaget, 1952; Rogers, 1963; Weirner,
1948; White, 1959) regard growth as the process through which people elaborate, refine,
internalize, and assimilate inner depictions of themselves and their environment. Though
this assimilative practice is frequently seen as an inherent endowment or propensity, SDT
stresses that contingent on the extent to which humans experience external endorsements
for basic psychological need satisfaction integration and internalization will operate more
or less effectively (Deci & Ryan, 1985). Specifically, humans are liable to integrate and
internalize internally the control of actions originally regulated and prompted by external
elements. For this reason, SDT may provide a pragmatic solution to the high dropout rate
of online developmental math students. It would follow that if institutions provide
appropriate support, then students would internalize and integrate the control of activities
that could increase their success.
Additionally, SDT is a motivational theory for which over three decades of
empirical research have evidenced support (Chen et al., 2015; Katz, Madjar & Harari,

19. 13
2015; Moran, Diefendorff & Liu, 2012; Rahman, Hudson, Thøgersen-Ntoumani &
Doust, 2015; Ryan & Deci, 2011; Vansteenkiste, Williams, & Resnicow, 2012;
Wormington, Corpus, & Anderson, 2012). Although widely used in many fields, SDT
has been most utilized in school research to enhance the motivation and performance
outcomes of students (Karaarslan, Ertepınar, & Sungur, 2013; Langdon, Webster, Hall, &
Monsma, 2014; Rutten, Boen, Vissers, & Seghers, 2015). Since the 1970’s, researchers
have shifted their focus from studying individual levels of motivation to examining
different types of motivation and found IM to be positively associated with school
achievement (Froiland, 2011; Froiland & Oros, 2014; Froiland, Oros, Smith, & Hirchert,
2012; Taylor et al., 2014; Wormington, Corpus, & Anderson, 2012). For this reason, SDT
is chosen as the theoretical framework of this study. Moreover, according to SDT, the
social context establishes the degree of obstruction or support which these needs are
given (Vansteenkiste et al., 2012; Wang & Peck, 2013). From the viewpoint of SDT,
therefore, it could be hypothesized that the degree a school meets learners’ needs for
autonomy, relatedness and competence may determine whether learners experience
success. As applied to my study, Self-Determination Theory holds that I would expect
my independent variables, instructional support of SRL behaviors and skills and course
delivery method to influence or explain the dependent variable, student retention because
students may be impacted by factors that support or hinder their interest and motivation.
Self-Determination Theory
Self-determination theory was first proposed by Deci and Ryan in 1985. The
fledgling theory stated four major propositions (a) individuals have an intrinsic need to
make choices and to be free to have choice; (b) individuals intrinsically desire to be

20. 14
competent and to master the environment; (c) there are controlling (contingent),
informational (noncontingent), and amotivating (inhibitive) facets of behavior, and these
facets result in different levels of intrinsic motivation; and (d) people generate for
themselves the meaning of events. Building on the work of Harry Harlow (1950) on
intrinsic motivation and Daniel Berlyne (1971) on physiological processes of organismic
need, Deci and Ryan postulate a psychological process of IM. Observed in
circumstances where individuals engage in behavior or perform an action because they
correlate an importance with that behavior even in the lack of an outside reward (1985).
Intrinsic research covers a wide range of motivational sub-theories that try to explain why
individuals ascribe importance to things like donating money, completing puzzles, or
volunteering in the absence of a reward (Deci & Ryan, 1985).
As Deci and Ryan (1991) continued their research, they advanced that individual
behavior is an active organism regulated by three universal Basic Psychological Needs
(BPN): autonomy, competence, and relatedness. Here Deci and Ryan added competence
and relatedness to their evolving theory. Based on White’s (1963) work on independent
ego energies and competence, as well as Harter’s (1978) work which continued White’s
work on competence, Deci and Ryan (1991) added competence to SDT. Ryan and Deci
would further differentiate between different kinds of motivation and classified these as
IM, EM, and Amotivation (AM). These positioned on a continuum according to the
extent to which the motivation is self-determined or internalized within the student (Ryan
& Deci, 2000). At the most determined end is IM with AM placed on the least
determined end while EM is in the center of the continuum. (Ryan & Deci, 2000).

21. 15
The center of the self-determined continuum is occupied by EM according to SDT
(Deci & Ryan, 1985). EM refers to an extensive array of individual behaviors performed
as a means to an end and not for their sake (Deci, 1975). EM is divided into four
subgroups: integrated, introjected, identified, and external regulations. Integrated
regulation is the highest level of self-determination. It occurs when an individual
participates in an endeavor entirely assimilated internally (Deci & Ryan, 1985; Dyrlund
& Wininger, 2006). The next level is identified regulation which is when a person is
participating in an activity since it is in agreement with their identity (Deci & Ryan,
1985; Ryan & Deci, 2003). Third, introjected regulation represents when a person is
engaging in a task to attain high self-esteem or to avoid negative feelings (Deci & Ryan,
1985; Vlachopoulos et al., 2013). The lowest level of self-determination is external
regulation and is contingent on external possibilities, such as, incentives or adverse
reactions (Deci & Ryan, 1985; Müller & Palekčić, 2005).
The reward or incentive does not have to be tangible but usually desired in some
way. An individual decides whether to respond to an extrinsic reward depending on the
recognized social value of the reward (Lee, Reeve, Xue, & Xiong, 2012). If the extrinsic
reward is perceived as a thing with a significant societal worth, such as recognition,
money, or prestige, the mind is more prone to stimulate in regions that support action.
Acknowledging a powerful extrinsic reward before starting an undertaking were more apt
to have goals, plan and have strategies in place to accomplish their tasks (Moos, 2010).
Therefore, it would seem that merely finding the right enticements would motivate any
individual. With the right enticement, potencies of extrinsic incentives are boosted,
including improved learning compliance, increased educational competition, and

22. 16
improved goal setting (Lei, 2010). Customarily, grades have been utilized to reward or
punish students. If students regard grades as a socially important or highly significant,
this incentive has been revealed to have certain merit (Kuh, 2007). However, countless
learners are not grade motivated. Students who received an “A” were more likely to be
motivated by grades (75%) than students who received other grades (48%) (Kuh, 2007).
Students who attain high grades are not necessarily motivated by those grades, but might
be autonomously motivated to start with (Barkoukis, Taylor, Chanal, & Ntoumanis,
2014). High achieving students frequently exhibit the same academic behaviors, such as
taking notes and studying, even on ungraded tasks (Barkoukis et al., 2014) suggesting
that motivation does not come from the prospect of getting a better grade, but for some
internal purpose. Deci, Eghrari, Patrick, and Leone (1994) experimentally established that
offering a meaningful justification for an unexciting behavior, in tandem with supports
for relatedness and autonomy, promoted integration and internalization. Subsequently,
Ryan and Deci (2000) would postulate that instructors could lead learners to internalize
the responsibility and sense of importance for extrinsic goals.
A key element of any discussion of IM is the function of self-efficacy. Self-
efficacy is an individual’s perceived skills for executing a particular action at a specific
level (Bandura, 1996). This element has become a foundation for much IM research in
education (Schunk & Zimmerman, 2012). High self-efficacy revealed clearly linked to
high levels of IM (Vohs & Baumeister, 2011). Moreover, researchers have found that
individual self-efficacy is not a fixed variable, but can be influenced by a variety factors
(Bernacki, Nokes-Malach & Aleven, 2015).

23. 17
Additionally, Deci and Ryan (2008) posits EM and IM can occur simultaneously.
In the workplace, for instance, researchers have found that at diverse times, employees
can be motivated by intrinsic and extrinsic forces even for performing the same task
(Moran et al., 2012). Extrinsic strategies like praise or grades have been found to reduce
IM over time because once the incentive was removed learners tended to finish the task
less often than when the incentive was present (Cameron & Pierce,1994). Also, when
learners were offered a reward for superior performance they opted to use their free time
to study less than learners not offered a reward suggesting that the enduring result of
utilizing rewards based on performance might be that learners when given free time may
be less apt to use it for extra learning (Wehe, Rhodes & Seger, 2015). There were
circumstances where an extrinsic reward was sufficient to motivate a learner to finish a
task, thus promoting their belief in their competence and that consequently led to higher
accounts of IM (Okada, 2010). However, compared to EM, IM seems to have longer-
lasting effects, is more reliable, and increases learner confidence (Lei, 2010). Hence, IM
is the prevailing focus of most motivation research in education (De Castella et al., 2013).
Learners who display high levels of IM perform better in school by an array of measures
(Barkoukis et al., 2014; Brookhart & Durkin, 2003; Conley, 2012; Deci & Ryan, 2008;
Lei, 2010; Pintrich & DeGroot, 1990; Pulfrey et al., 2013; Saeed & Zyngier, 2012). This
research may suggest that increasing IM would be a desired goal for institutions striving
to increase student success. However, change can be difficult for teachers to implement
(Ertmer, Ottenbreit-Leftwich, Sadik, Sendurur, & Sendurur, 2012; Tam, 2015;
Westberry, McNaughton, Billot, & Gaeta, 2015). Interestingly, SDT may also help
facilitate teacher change, recently nurturing satisfaction of the psychological needs for

24. 18
autonomy, competence, and relatedness was found to yield greater openness toward
change among teachers (Aelterman, Vansteenkiste, Van Keer, & Haerens, 2016).
Currently, SDT is comprised of six mini-theories (Deci & Ryan, 2012). To
elucidate a collection of motivationally based occurrences that transpired from laboratory
and field research each theory was developed. Each, consequently, concentrates on one
facet of motivation or personality functioning. Cognitive evaluation theory (CET)
addresses the influences of societal contexts on IM, or how factors like interpersonal
controls, compensations, and ego-involvements effect IM and attentiveness (Deci &
Ryan, 2012). Organismic integration theory (OIT) undertakes the subject of EM in its
various forms, with their determinants, properties, and consequences (Deci & Ryan,
2012). Causality orientations theory (COT) assesses and describes three categories of
causality orientations: the autonomy orientation wherein individuals proceed because of
interest and value for what is happening; the control orientation wherein the attention is
on approval, gains, and rewards; and the amotivated or impersonal orientation
characterized by angst regarding competence (Deci & Ryan, 2012). Basic psychological
needs theory (BPNT) maintains that optimal functioning and psychological well-being is
founded on relatedness, autonomy, and competence (Deci & Ryan, 2012). Consequently,
environments that boost rather than impede these needs should invariably effect well-
being. Goal contents theory (GCT) develops out of the differences amongst extrinsic and
intrinsic objectives and their effect on wellness and motivation (Deci & Ryan, 2012).
Relationships motivation theory (RMT) is concerned with relationships, and advances
that some amount of such interactions is not only pleasing for most individuals but is, in

25. 19
fact, crucial for their well-being and adjustment because the relationships provide
gratification of the need for relatedness (Deci & Ryan, 2012).
Unexciting behavior wants meaningful justification; experiments establish that
relatedness and autonomy together lead to greater integration and internalization (Deci et
al., 1994). These results suggest that by offering meaningful justification, instructors
could move learners toward the most determined end of the motivation continuum
proposed by Deci and Ryan (2000). Also, research finds that learners with the highest
levels of IM—the most determined end of Deci and Ryan’s continuum of motivation—
show higher performance in school, higher than learners with low levels of IM
(Barkoukis et al., 2014; Brookhart & Durkin, 2003; Conley, 2012; Deci & Ryan, 2008;
Lei, 2010; Pintrich & DeGroot, 1990; Pulfrey et al., 2013; Saeed & Zyngier, 2012). An
array of measures bears this out. (Barkoukis et al., 2014; Brookhart & Durkin, 2003;
Conley, 2012; Deci & Ryan, 2008; Lei, 2010; Pintrich & DeGroot, 1990; Pulfrey et al.,
2013; Saeed & Zyngier, 2012). Moreover, that a key ingredient in IM, self-efficacy, has
become a foundation for much IM research in education (Schunk & Zimmerman, 2012).
Additionally, high self-efficacy has been clearly linked to high levels of IM (Vohs &
Baumeister, 2011). Moreover, researchers have found that individual self-efficacy is not a
fixed variable, but can be influenced by a variety factors (Bernacki, Nokes-Malach &
Aleven, 2015). All of the aforementioned research supports the assertion that SDT may
predict that instructional support of SRL behaviors and skills in an online delivery
method for developmental math community college courses contributes to success rate.
ResearchQuestions
The current study sought to investigate online community college developmental

26. 20
math courses outcomes. The two groups studied were online and F2F developmental
math courses and each group was split into courses with and without institutional support
of SRL behaviors and skills. The following quantitative research questions sought to
determine whether institutional support for SRL behaviors and skills (i.e., the
independent variable [ID]) increases student retention (i.e., the dependent variable [DV])
in community college online developmental math courses.
Q1. Does instructional support of self-regulated learning behaviors and skills
increase retention of students enrolled in community college developmental math courses
compared to students enrolled in community college developmental math courses without
instructional support of self-regulated learning behaviors?
Q2. Do the effects of institutional support of self-regulated learning behaviors and
skills on retention rates differ between online and F2F courses?
Hypotheses
H10. There is a statistically significant difference between instructional support
of self-regulated learning behaviors and increased student retention of students enrolled
in community college developmental math courses compared to students enrolled in
community college developmental math courses with instructional support of self-
regulated learning behaviors.
H1a. There is no statistically significant difference between instructional support
of self-regulated learning behaviors and increased student retention of students enrolled
in community college developmental math courses compared to students enrolled in
community college developmental math courses with instructional support of self-
regulated learning behaviors.

27. 21
H20. There is a statistically significant difference in the effects of institutional
support of self-regulated learning behaviors and skills on retention between online and
F2F courses.
H2a. There is no statistically significant difference in the effects of institutional
support of self-regulated learning behaviors and skills on retention between online and
F2F courses.
Nature of the Study
The purpose of this study was to examine the potential relationships between
instructional support of SRL behaviors and skills and retention of students in community
college developmental math courses. This quantitative study was a causal-comparative
(ex-post facto) quasi-experimental design. Quantitative methods are more appropriate to
study characteristics of a group of interest to determine causal relationships and to
provide clarifications of predictions (Kraska, 2010) within populations (Szyjka, 2012). A
cross-sectional design was chosen since the research involved naturally created groups
(e.g., learners enrolled in developmental math courses) at one point in time (Julien, 2008;
Salkind, 2010). Two customary nonexperimental approaches of quantitative research are
causal-comparative (ex-post facto) and survey (Kraska, 2010). Causal-comparative
investigations do not allow the investigator to control for extraneous variables (Kraska,
2010); in this study, it is not feasible to control for extraneous variables that may have
motivated a student’s decision to enroll in a developmental math course or variances in
personal characteristics, instructional strategies, and course content that may have
influenced a student’s success in a developmental math course. Measuring of the impact
of instructional support of SRL behaviors and skills on student retention in community

28. 22
college developmental math courses was accomplished by analyzing data obtained from
the California Community Colleges’ Chancellor Office’s Management Information
Systems (COMIS) also known as Data Mart.
Significance of the Study
Developmental math courses offered at community colleges prepare students to
take introductory college level mathematics—a prerequisite for most degrees (Ashby,
Sadera, & McNary, 2011; Bailey, Jeong, & Cho, 2010; Xu & Jaggars, 2011). Estimates
indicate 60% percent of community college students are referred to a developmental math
course (Bailey et al., 2010) with 7% of those enrollments taking place in online courses
(Xu, & Jaggars, 2014), however, of those students who enroll in a developmental math
sequence online only 20% will complete their first college-level math course compared to
32% who completed their developmental math sequence in a face-to-face class (Jaggars,
Edgecombe, & Stacey, 2013; Xu & Jaggars, 2013; Xu & Jaggars, 2011).
The purpose of this causal-comparative (ex-post facto) quasi-experimental
quantitative study was to investigate whether SDT predicts that institutional support for
SRL behaviors and skills in an online delivery method for developmental math
community college courses may contribute to retention rate. Through this study, it was
hoped that education professionals’ concerned about high rates of attrition for community
college students taking online developmental math courses will be able to assist better
and inform this unique subset with a more thorough understanding of practices of self-
learning (Lee & Tsai, 2011; Schunk & Zimmerman, 2012). This study contributes to the
growing literature on the development of online course delivery structures and informs

29. 23
community college faculties’ pedagogical practices (and, potentially, the processes and
designs of online course developers).
Definition of Key Terms
Developmental math. A sequence of mathematics classes that begins with basic
arithmetic, next to pre-algebra, then elementary algebra, and finally intermediate algebra,
which a learner must pass before registering in a transfer-level college mathematics class
(Stigler, Givvin & Thompson, 2010).
Instructional support. Instructional support is a wide array of educational
strategies including but not limited to after-hours, technology, classroom and school
based strategies (Glossary of Education Reform, 2016).
Online learning environment (OLE). A form of distance learning delivered
entirely over the Internet (Nguyen, 2015).
Self-regulated learning (SRL). Learning as it occurs because of the learner’s
systematic use of motivation, metacognitive, and behavioral strategies: by their
receptiveness to feedback concerning the effectiveness of their learning; and by their self-
perceptions of academic achievement (Dent & Koenka, 2015).
STEM. An acronym for Science, Technology, Engineering, and Mathematics
(California Department of Education, 2016).
F2F courses. A course where 0% of the content delivered is online, and 100% of
the content is delivered orally or in writing (Allen & Seaman, 2014).

30. 24
Summary
The purpose of this study ass to examine the potential relationships between
instructional support of SRL behaviors and skills and retention of students in community
college developmental math courses. The foundational problem for this study was the
college retention rate in STEM courses. If left unchanged it is estimated by 2018 the
United States postsecondary system will fall short by three million in filling the labor
market’s demand for college graduates (Carnevale et al., 2010; U. S. Department of
Labor, 2012). President Obama has called on community colleges to increase science,
technology, engineering and math (STEM) graduates (Obama, 2009).
Developmental math courses offered at community colleges prepare students to
take introductory college level mathematics—a prerequisite for most degrees (Ashby,
Sadera, & McNary, 2011; Bailey, Jeong, & Cho, 2010; Xu & Jaggars, 2011). However,
of those students who enroll in a developmental math sequence online, only 20% will
complete their first college-level math course compared to 32% who completed their
developmental math sequence in a F2F class (Jaggars, Edgecombe, & Stacey, 2013; Xu
& Jaggars, 2013; Xu & Jaggars, 2011).
This quantitative study ws a causal-comparative (ex-post facto) quasi-
experimental design. Quantitative methods are more appropriate to study characteristics
of a group of interest to determine causal relationships and to provide clarifications of
predictions (Kraska, 2010) within populations (Szyjka, 2012). A cross-sectional design
was chosen since the research involved naturally created groups (e.g., learners enrolled in
developmental math courses) at one point in time (Julien, 2008; Salkind, 2010).
Measuring of the impact of instructional support of SRL behaviors and skills on student

31. 25
retention in community college developmental math courses was accomplished by
analyzing data obtained from the California Community Colleges’ Chancellor Office’s
Management Information Systems (COMIS) also known as Data Mart.

32. 26
Chapter 2: Literature Review
The purpose of this causal-comparative (ex-post facto) quasi-experimental study
was to investigate whether SDT predicts that institutional support for SRL behaviors and
skills in an online delivery method for developmental math community college courses
contributes to success rate. The foundational problem for this study was the college
retention rate in STEM courses. If left unchanged it is estimated by 2018 the United
States postsecondary system will fall short by three million in filling the labor market’s
demand for college graduates (Carnevale et al., 2010; U. S. Department of Labor, 2012).
Accordingly, this literature review includes an examination of peer-reviewed, scholarly
journal articles related to SDT, SRL, distance education, self-efficacy, community
colleges, developmental education, online student success, and STEM. The literature
review includes a brief overview of SDT, three models of SRL, self-efficacy as it applies
to SRL, and an analysis of today’s community college role in developmental education.
Also, an analysis of the assessment procedures used to determine placement in
developmental education, an examination of the effect of developmental math, and an
appraisal of the challenges online students encounter. As well as, the importance of
STEM occupations and initiatives related to STEM. The results of the scholarly literature
review provide the foundation for examining the relationship, if any, between
Institutional support of SRL behaviors and skills and a student’s completion of a
developmental math course in a community college setting.
Sample
The participants in this study were collected by using a convenience sampling
method of students enrolled in online developmental math courses at Rio Hondo

33. 27
Community College during the Fall semesters of 2014 and 2015. Rio Hondo Community
College was chosen because it recently added the institutional support of WebAssign in
Fall 2015. Information was obtained from college websites, course descriptions, Data
Mart, and contact with appropriate personnel. Courses that were confirmed to meet these
parameters were included. Based on a G*Power analysis with a two tailed alpha set at
0.05, and power set at 0.95 a minimum sample size of 721is required (Buchner,
Erdfelder, Faul, & Lang, 2012; see Appendix A). There will be four sets of data: 200 F2F
developmental math students without instructional support of SRL behaviors and skills,
200 F2F developmental math students with instructional support of SRL behaviors and
skills, 200 online developmental math students without instructional support of SRL
behavior and skills, 200 online developmental math students with instructional support of
SRL behavior and skills.
Documentation
The search for relevant literature for this study was conducted through
Northcentral University’s Roadrunner Search, utilizing the keywords of Self-
Determination Theory, Self-Regulated Learning, developmental mathematics, STEM,
online learning, and community college along with self-efficacy, motivation, retention,
and dropout within the interval 2011 through 2016. The literature reviewed from these
searches was only peer-reviewed and was acquired from the EBSCOhost, Sage Journals,
ERIC, ProQuest, Gale Academic OneFile databases, and Web of Knowledge/Social
Sciences Citation Index. Additionally, author-name searches were conducted for Deci,
Pintrich, Ryan, Winnie, and Zimmerman. Further, the reference lists of the most current
studies often served as starting points for new searches. Some reference lists contained

34. 28
citations of seminal research on the development of SDT and SRL. The seminal research
studies, which were often contained in entire books or book chapters, were purchased and
read to understand better the foundations for current research on this topic.
Self-Regulated Learning
Self-regulated learning refers to self-regulation processes employed during a
learning practice, where the aim is an aspired level of accomplishment (Sitzmann & Ely,
2011). Self-regulated learning actions are a function of a student’s desire to achieve
objectives, such as those set to accomplish academic goals (Edmondson, Boyer, & Artis,
2012; Lee & Choi, 2013). Self-regulated learning is described as the ability of a learner to
proactively, strategically, and independently participate in thoughts and behaviors to
achieve self-set, personal goals (Zimmerman, 1989). From the standpoint of information
processing, SRL has been described by Winne and Hadwin as a metacognitively driven
and directed process (1998).
Studies regarding online learner motivation are frequently conducted from the
viewpoint of the learner to include extrinsic and intrinsic motivational elements (Hartnett,
St. George, & Dron, 2011; Malinovski, Vasileva, Vasileva-Stojanovska, & Trajkovik,
2014). Extrinsically motivated learners may participate in classes if there is the promise
of a desired reward or if they believe classwork will enhance their performance (Cheng,
Wang, Moormann, Olaniran, & Chen, 2012). Other extrinsic motivational factors include
the desire to meet others’ expectations, the perception of tasks as valuable and relevant,
and negative consequence avoidance (Hartnett et al., 2011; Kim & Frick, 2011).
For learners who are motivated intrinsically, the autonomous online learning
environment is best since there are circumstances to adjust and customize individual

35. 29
learning avenues (Hartnett et al., 2011). Additional intrinsic motivational elements
include individual interest, individual satisfaction, and gratification in assignment
accomplishment (Hartnett et al., 2011). Compared to their extrinsically motivated peers,
intrinsically motivated learners are more tenacious and more apt to achieve educational
objectives in the virtual milieu (Malinovski et al., 2014). While intrinsic learner
motivation is more enduring than extrinsic motivation, external motivational influences,
like social influence and good grades, function as an effective impetus in online
educational environments (Malinovski et al., 2014).
Studies on SLR interventions show conflicting results. Some studies have found
a significant positive relationship between SRL intervention and academic success
(Heller & Marchant, 2015; Hudesman et al., 2014) while another found no positive effect
(Skinner et al., 2015). Accordingly, sources disagree on whether lack of SRL within an
online delivery method is resulting in the low success rate (Bol & Garner, 2011; Cho &
Heron, 2015; Mahlberg, 2015). In the online environment, academic motivation and
learner self-efficacy are closely linked (Kim & Frick, 2011). In an activity directed by
objectives, motivation is a sustaining process (Schunk & Mullen, 2012). Learner
motivation in an online environment has also been shown to be reliant on reliant upon
situational features, such as the perceived relevance of tasks and nature of tasks (Hartnett
et al., 2011). The following elements also influenced learner motivation: (a) learners’
prior knowledge of material; (b) if the course is a requirement; and (c) learner opinion
that in an online class good grades are achievable with marginal exertion (Hartnett et al.,
2011).

36. 30
As indicated by research findings, self-efficacy theory significantly contributes to
the online learning knowledge base and has important implications for learner success,
self-regulation and motivation (Kim & Frick, 2011). There are some conflicting opinions
in the field as applied treatments of self-efficacy and SRL theories may not enrich learner
results in virtual settings (Yang & Park, 2012). For example, investigators inserted SRL
approaches into a virtual class, to bring in tactics on increasing self-efficacy, and then
surveyed learners regarding the utilization of approaches (Yang & Park, 2012). The
inserted self-efficacy approaches involved peer feedback, such as learners praising and
boosting one another, and attribution feedback for example learners replying to
affirmative feedback from peers (Yang & Park, 2012). Results showed that learner use of
SRL approaches in the virtual setting was not significantly impacted by the inserted self-
efficacy approaches (Yang & Park, 2012).
Emotional intelligence, also termed trait emotional self-efficacy (Petrides &
Furnham, 2001), refers to the emotion-related behavioral dispositions and self-perceived
abilities of an individual (Petrides, 2011), for example, emotional self-efficacy,
flexibility, independence, assertiveness, and stress tolerance (Sparkman, Maulding, &
Roberts, 2012). Emotional factors such as aloneness, anonymity, isolation, apprehension
due to inexperience with online learning, and absence of face-to-face communications
along with emotional intelligence have been identified as non-cognitive predictors of
learner success in a college course (Reilly, Gallagher-Lepak, & Killion, 2012; Sanchez-
Ruiz, Mavroveli, & Poullis, 2013; Sparkman et al., 2012). Additionally, a student’s
degree of self-efficacy affects a learner’s personal and professional choices and

37. 31
satisfaction with the chosen domain and may influence a student’s academic and
vocational choices (Sanchez-Ruiz et al., 2013).
Time management to be one of the principal factors leading to success in
educational situations since students must create and follow schedules that provide
adequate opportunity to accomplish the assignments (Hart, 2012; Michinov et al., 2011).
Strong time management skills have been found to support positive academic
performance and progress in the online environment (Michinov, Brunot, Le Bohec, Juhel,
& Delaval, 2011; Yang & Park, 2012). There is a significant relationship between the
utilization of self-regulation strategies and academic outcomes (Heller & Marchant,
2015; Hudesman et al., 2014). However obstacles exist to the implementation of a SRL
program such as inadequate teacher preparation (Dignath-van Ewijk & van der Werf,
2012; Usta & Bozpolat, 2014; Vandevelde, Vandenbussche & Van Keer, 2012), teachers’
own learning experiences (Chatzistamatiou, M., Dermitzaki & Bagiatis, 2014; Peeters et
al., 2014; Usta & Bozpolat, 2014), challenges with SRL instruction (Lau, 2013), teachers’
beliefs about student development (Lau, 2013; Law, 2011), pedagogical experiences
(Usta & Bozpolat, 2014), curriculum constraints (Lau, 2013), and teachers’ expectations
for students (Lau, 2013; Law, 2011; Vandevelde, Vandenbussche, & Van Keer, 2012).
Additionally, students who are more used to traditional approaches need a good deal of
guidance to engage successfully in SRL (Law, 2011).
Models of Self-Regulated Learning
Starting in the 1980s, researchers have categorized self-regulated learning
processes that transpire in academic contexts, studied their function during learning, and
investigated ways to help students cultivate and transfer them outside the original

38. 32
learning environment (Zimmerman and Schunk, 2011). This collection of research, most
of which performed in instructional settings, has revealed that self-regulated learning
processes impact academic achievement and motivation (Zimmerman and Schunk, 2011).
Barry Zimmerman, Paul Pintrich, and Philip Winne have been prominent in research
regarding self-regulated learning. Zimmerman (1989), coming from a social cognitive
approach, advanced a three-phased theory of learning to clarify how learners engage in
different practices to independently support their efforts to learn. Utilizing an Information
Processing and social cognitive approach Pintrich (2004) largely described SRL
analogous to Zimmerman but stressed context as a topic that needs attention in addition
to SRL’s encompassing motivational, cognitive, and behavioral dimensions. While
Winnie & Hadwin’s framework offers another perspective guided by the Information
Processing Theory (IPT). According to Winne and Hadwin’s paradigm of self-regulation,
when self-regulated students tackle a learning task, they consider their environmental
structuring, prior knowledge, beliefs, time, and collection of study strategies to best
fathom what the task requires of them (Winne, 2001; Winne & Hadwin, 1998).
Theories of self-regulated educational learning vary in many ways but have
common elements. One element is that self-regulated learning entails being behaviorally,
metacognitively, cognitively, and motivationally dynamic in one’s performance and
learning (Bernacki et al., 2015; Pintrich, 2004; Sitzman & Ely, 2011; Winne & Hadwin,
1998; Zimmerman, 1989). A second common element is that self-regulated learning is an
active and cyclical practice involving feedback loops (Bernacki, Nokes-Malach &
Aleven, 2015; Lee & Choi, 2013; Pintrich, 2004; Sitzman & Ely, 2011; Winne &
Hadwin, 1998; Zimmerman, 1989). Self-regulated students establish goals and

39. 33
metacognitively monitor their movement toward them. They react to their monitoring,
along with outside feedback, in numerous ways to accomplish their goals, such as
changing their strategy or by working harder. Attained achievements steer them to
establish new goals. Third, goal setting activates self-regulated learning by steering
learners’ focus on goal-directed actions and usage of task-relevant tactics (Hudesman et
al., 2014; Mahlberg, 2015; Pintrich, 2004; Sitzmann & Ely, 2011; Winne & Hadwin,
1998; Zimmerman, 1989). Goals that contain learning skills and enhancing competencies
result in superior self-regulation than those focused on performing tasks (Hudesman et
al., 2014; Mahlberg, 2015). Lastly is a stress on motivation, or why individuals elect to
self-regulate and continue their efforts. Motivational variables are significant for learning
(Castro-Villarreal, Guerra, Sass, & Hseih, 2014).
The Self-Efficacy Effect on SRL
One of the most highly researched constructs in psychology and organizational
behavior is self-efficacy (Ortiz de Guinea & Webster, 2011). Self-efficacy has been
shown to have an important effect on SRL because it affects the extent to which learners
are willing to participate in and persist at difficult tasks (Diseth, 2011; Simon, Aulls,
Dedic, Hubbard, & Hall, 2015). Social cognitive theorists also suggest that self-efficacy
is a key variable affecting self-regulated learning (Diseth, 2011; Simon, Aulls, Dedic,
Hubbard, & Hall, 2015). Learners with high self-efficacy set objectives that are more
demanding and are better at selecting successful learning strategies (Diseth, 2011; Simon
et al., 2015).
Educational researchers acknowledge that a learner’s confidence in his or her
abilities impacts the motivation to learn and actual accomplishment in an academic

40. 34
environment, including the online learning setting (Flowers, 2011; Matthews, Banerjee,
& Lauermann, 2014; Petty & Carter, 2011; Taipjutorus, Hansen, & Brown, 2012; van
Dinther, Dochy, & Segers, 2011). Bandura defined self-efficacy as the attitude of an
individual’s capability to organize and execute the structure necessary to produce given
achievements (1996). Bandura’s theory of self-efficacy is an important variable in
individual learning because a student’s degree of perceived self-efficacy impacts a
student’s attitudes about his or her capacity to execute specific actions at a predetermined
level of competency (van Dinther et al., 2011). A student’s degree of perceived self-
efficacy in a particular situation effects the amount of effort put forth, the choice of
activity, degree of persistence, and performance (Petty & Loboda, 2011). As self-efficacy
decisions affect performance, a student’s beliefs in his or her capacity to effectively
function in an online environment may directly affect course completion and academic
achievement (Flowers, 2011; Petty & Carter, 2011).
A learner’s sense of self-efficacy is dynamic and varies over time as a
consequence of different situations and new experiences (Bernacki et al., 2015; Ortiz de
Guinea & Webster, 2011; Taipjutorus et al., 2012). For instance, a student’s sense of self-
efficacy may vary as a consequence of a change from a F2F learning environment to an
online learning environment (Maathuis-Smith et al., 2011). Mastery experiences are
thought to be the foremost precursor that furthers a strong sense of efficacy (Petty &
Loboda, 2011) since these occurrences provide learners with authentic evidence that they
will achieve a given task (van Dinther et al., 2011). Experience in a specific area is the
strongest determining factor of self-efficacy beliefs since learners assess their abilities
based on the evidence of previous experiences (Ortiz de Guinea & Webster, 2011).

41. 35
Furthermore, success in a certain situation emboldens the learner to repeat the activity;
for instance, a student who successfully finishes an online class is apt to acquire a high
self-efficacy belief concerning online learning and will probably enroll in a subsequent
online class (Petty & Loboda, 2011).
Social persuasion, in the form of communications or evaluative feedback, also
performs a role in the formation of a sense of self-efficacy. Verbal persuasion is an
additional source of self-efficacy data, and verbal persuasion could take the form of
social or verbal information obtained from others (Petty & Loboda, 2011). Learners are
apt to develop a sense of self-reliance in their skills if those who persuade them are
deemed to be reliable and knowledgeable (van Dinther et al., 2011). Another source of
self-efficacy data is psychological/physiological states (Petty & Loboda, 2011). A
learner’s psychological or emotional state also contributes to his/her self-efficacy (Artino
& Jones, 2012; Marchand & Gutierrez, 2012). Feelings of stress, anxiety, or tension when
construed as a sign of imminent failure, consequently, result in a low degree of perceived
self-efficacy; equally, a positive attitude can result in a higher degree of perceived self-
efficacy in a particular situation (van Dinther et al., 2011).
A learner commonly displays low aspirations and low dedication to goals if
he/she identifies the activity to be difficult; a learner with a high degree of self-
confidence in his/her abilities tends to create challenging objectives and persists to
achieve those objectives (Petty & Loboda, 2011). A learner with a high degree of
perceived self-efficacy tends to put forth sufficient effort in a given situation to produce
successful outcomes (Lee & Mendlinger, 2011). Individuals with high amounts of
perceived self-efficacy are more apt to set high objectives and to work to achieve these

42. 36
objectives; these learners choose more challenging tasks, generate more effort to
complete the tasks, and exhibit a high degree of determination when confronted with a
challenging task (Petty & Loboda, 2011).
The aforementioned research was mostly conducted utilizing a self-reporting
measure. A caveat to measuring self-efficacy with a self-reporting measure is that there
could be a problem with construct validity (Bowman, 2010; Cleary, Callan, Malatesta, &
Adams, 2015). This problem could be due to a variety of reasons, even if a contributor is
attempting to be honest, they may not have the introspective aptitude to provide an
accurate answer to a question this may be especially true for participants who have low
self-efficacy. Also, utilizing rating scales can produce their own problems, individuals
could interpret and use scales differently causing variations in the results. Moreover, with
the online questionnaire, there is little deterrent for individuals to respond with spurious
answers, and there is insignificant control over how much attention the respondent pays
to portions of the questionnaire.
The Adult Learner and College Readiness
Approximately 54 percent of California community college students are between
the ages of 20 and 34. Fifty-three percent are female, and sixty-seven percent are from
diverse ethnic backgrounds (CCCC0, 2016). Countless learners are resuming school
confronted by a changed educational system. The gap in time since they graduated high
school or was in a classroom has left them behind and grappling with the requirements of
present culture’s college academic minimal skills. The student is faced with the need to
take developmental courses before actually pursuing a college degree (Perin, 2013).

43. 37
The disparity between non-college preparedness and college preparedness is
broadening, and as it persists in widening, students become more askew in their objective
of obtaining a higher education. Across the nation, the substantial increase in enrollments
of learners who are not ready for college has yielded retention and success concerns and
validates a necessity to assist these students attain realization of their goals Hachey,
Conway, & Wladis, 2013). Developmental education or academic under-preparedness is
a topic in higher education that should be dissected and not be ignored. Students entering
community college today are discovering they are not sufficiently equipped for post-
secondary level courses (Hart, 2012; Mann & Henneberry, 2012). A key challenge when
creating courses for developmental students is understanding how adults learn.
Furthermore, understanding adults’ participation in online college courses is an emerging
area (Hachey et al., 2013).
Andragogy denotes adult learning style while pedagogy denotes children’s
learning style (Bahhouth & Bahhouth, 2011). An important difference adult learners have
is they have more life experience than the child to bring to the learning experience. Not
only does the adult learner have a varied personal history of experiences that afford
valuable learning resources but is a person who can guide their own learning.
As academics teaching adult learners investigate the idea of self-directed learning,
the evidence mounts that recognizing the needs of adult students, and especially the non-
college ready adult student is valuable in confronting a growing concern faced by
universities and colleges currently (Hachey et al., 2013). Underprepared students that are
entering higher education institutions require extra aids in place to support retention and
achievement (Hachey et al, 2013). As the number of adults returning to college and

44. 38
enrolling in online classes increases, their introduction to online learning necessitates
accommodating their learning profile. Universities, colleges, and educators should
acknowledge the variances in student ability and assist these students (Crisp & Delgado,
2014).
Resuming their education is frequently a major choice for an adult, and
discovering that they are not prepared can be demoralizing. Additionally, the change
from the previous F2F format to an online learning format presents another impediment.
Results have been mixed in prior literature concerning online learning (Garman & Good,
2012, Mgutshini, 2013). A variance based on subject area has been indicated in much of
the literature on student learning and overall satisfaction (Chen, Jones, & Moreland,
2013; Xu, & Jaggars, 2014). Chen et.al. (2013) found the communication between the
instructor and student accounted for a considerable variance in student results when
studying online versus F2F accounting education courses. Mgutshini (2013) found no
significant differences in the academic outcomes between the F2F and online sections.
However, Garman and Good (2012) found results contrary to Mgutshini (2013)
observing that students in F2F courses had a significantly lower attrition rate than those
choosing online courses. Educators need to understand these adult students and diagnose
their needs, motivations, and concerns. Even though enabling distance learning has its
challenges, numerous technologies exist to assist instructors in producing an online
course that supports student achievement (Croxton, 2014; Huffman, & Huffman, 2012;
Karvounidis, Chimos, Bersimis, & Douligeris, 2014).
In summary, when designing courses for developmental learners in an online
format understanding the aspects of adult learners is useful. Since adults have had various

45. 39
experiences, and are also engaged in family and work, not youngsters in school engaged
full-time, an education program that is conducive to their life combined with innovative
technologies is vital to achieving a college degree (Soares, 2013). Nevertheless, online
class courses in developmental math and English can have a harmful influence on learner
achievement (Xu & Jaggers, 2011).
Community College Role in Developmental Education
The U.S. Department of Education (2013) defines community colleges as
institutions where the preponderance of the degrees conferred are at the certificate or
associate level. Although community colleges have many purposes, their primary mission
is to serve the community (American Association of Community Colleges, 2012; Brown,
2012; Brown, King, Stanley, National Resource Center for the First-Year Experience &
Students in Transition, & American Association of Community Colleges, 2011).
Community colleges are a uniquely American contribution to postsecondary education
(Bidden, 2011; Boggs, 2010). Much like the nation that conceived them, community
colleges offer an open door to the possibility of a better life to all who would come
(Boggs, 2010). Community colleges are agile and innovative in meeting workplace and
economic needs and provide service and value to communities and individuals.
Community colleges have become increasingly imitated around the globe (Boggs, 2010)
and have become the largest and fastest-growing division of U.S. higher education
(Biden, 2011; Snyder & Dillow, 2013; Topper & Powers, 2013).
Around the world these institutions have diverse names: community colleges,
technical universities, technical colleges, institutes of technology, polytechnics, colleges
of technology, further education institutions, technical and further education institutions,

46. 40
and junior colleges (Elsner, Boggs, & Irwin, 2008). In Saudi Arabia, the first community
college was created in 1976 to train high school graduates to teach elementary schools
(Almannie, 2015). Under the supervision of the Ministry of Education, these two-year
junior teacher colleges eventually were upgraded to four-year colleges to develop
enhanced teachers for elementary schools (Almannie, 2015). Under the supervision of
General Presidency of Technical Education and Training, the initial two-year technical
college that was considered a community college was established in 1983 (Almannie,
2015). First established in 2001 in Vietnam, community colleges offer continuing
education programs, 3-year college programs, certification programs, and vocational
training (Le, 2013). Similarly, Malaysia established the first community college in 2001
(Southeast Asian Ministers of Education Organization (SAMEO), 2016).
However, unlike American community colleges foreign institutions commonly
restrict admittance, usually with an exam (European Education Directory, 2016; Kanaan,
Taher, and Hanania, 2009; Le, 2013; Salehi-Isfahani, & Egel, 2009; SAMEO, 2016;
Sayre, & Al-Botmeh, 2009). Nevertheless, in most countries, the community colleges
lack the status of the elite institutions (Elsner et al., 2008). This lack of status has effected
American community colleges recruitment of Chinese students (Zhang & Serra-
Hagedorn, 2014). Students and parents usually held negative attitudes and biased
opinions regarding attending a community college. In China, families often associated
U.S. community colleges with the two- or three-year vocational colleges found in China,
which have no accreditation to bestow degrees. Parents of prospective students expressed
negative images of U. S. two-year colleges, including limited choices of majors, low

47. 41
quality of education, and ill-prepared students and faculty (Zhang & Serra Hagedorn,
2014).
Enrollment in community colleges has greatly increased into the 21st century.
Since 1970, the number of students attending community colleges has increased 323%
compared to 209% for four-year schools (Snyder & Dillow, 2013). Approximately 60%
of community college students come underprepared (Pretlow & Wathington, 2013).
The students attending community colleges tend to be more characteristic of a diverse
America than four-year schools demonstrated by the higher percentages of females,
minorities, older students, academically underprepared, first-generation students (neither
parent earned a college degree), and those receiving federal aid (Beach, 2011; Brown,
2012; Snyder & Dillow, 2013; Wolfle & Williams, 2014). Furthermore, first generation
students are more apt to be categorized as underprepared students (Ward, Siegel, &
Davenport, 2012). Greater than half of the students of color, defined as Hispanic, African
American, Asian, and Native American, register at a community college (Talbert, 2012).
The percentage of Hispanic community college students is 19% compared to 13% in
four-year institutions; 16% compared to 12% for African American; and 7% compared to
6% for Asian/Pacific Islanders (Snyder & Dillow, 2013). Generally, community colleges
serve 52% of all Hispanic students, 44% African American, 45% of Asian/Pacific
Islanders, and 55% of Native American (Brown, 2012). And of African American males
most attend a community college (Strayhorn, 2012). The percentages have stayed fairly
constant over the past twenty-five years (Beach, 2011), the total number of minority
students has increased due to the overall enrollment increase. Moreover, compared to
students at four-year institutions, a higher percentage of community college students are

48. 42
employed (Snyder & Dillow, 2013). The amount of hours employed has been observed to
negatively affect graduation and retention rates (Brown, 2012; Nakajima, et al., 2013;
Saenz et al., 2011).
In the early 20th century great challenges faced the United States, including global
economic competition. National and local leaders recognized that a more skilled
workforce was needed to continue the country's economic strength. This need called for a
dramatic escalation in college attendance; however, during the early 20th century, three-
quarters of high school graduates were electing not to extend their education, partially
because they were hesitant to leave home for a faraway college (AACC, 2016).
Throughout the 20th century, community colleges were aided by legislators,
policymakers, and educational leaders in transforming into postsecondary institutions
offering admission to any and all students (Arendale, 2011). Community colleges, with
rare exceptions, subsisted as intended feeder schools to the four-year post-secondary
institutes until the mid-1960s. At this time large numbers of African American and Latino
students started to enroll, that community colleges began to initiate significant numbers
of vocational curriculums and increase focus on services for non-transfer students
(Hughes, 2012). Community colleges also produced developmental studies programs to
remediate learners’ skills. Instead of a combined curriculum that anticipated
underprepared learners and their requirements within F2F courses, community colleges
designed separate tracks that directed learners into vocational programs or a prolonged
developmental studies track. While these curriculums developed in the sixties were
envisioned to allow community college learners to improve skills or to acquire a terminal

49. 43
two-year occupational degree, in reality, they generated additional obstacles to
baccalaureate degree attainment (Hughes, 2012).
Distance learning in the California Community Colleges systems began in
1979 according to the California Community Colleges Chancellor’s Office (CCCCO),
(2011). The California community colleges provided limited distance education courses
which were transferable to 4-year institutions. In 1994, the growing demand for distance
education courses allowed California to adopt temporary regulations to initiate a pilot
program for seven years (CCCCO, 2011). Between 1995 and1996, distance education
courses were 0.63% of all course sessions. Recent figures show distance education as
10.54% of all course sessions (CCCCO, 2013). The goal of distance education was and is
today to produce an equivalent in value educational as a face-to-face course for the
student (CCCCO, 2011).
For the time period of 1994 to 2001, a report was submitted to summarize the
activities during the pilot period to the board. In January 2002, the first distance
education report was released by the Chancellor’s Office that comprised a seven-year
study from 1994 to 2001. The Board of Governors then approved in March 2002 the Title
5 regulations to expand distance education to credit and noncredit non-transferable
courses. The board also recommended continuing the collection and review of distance
education data. The data is collected every two years and the reports on learner access
and success in all distance education courses by gender, age, ethnicity, and type of
disability of the learners enrolled (CCCCO, 2011).
By 2002, the regulatory modifications also allowed distance education courses to
be thought as equivalent to F2F courses rather than only as independent study. The

50. 44
criteria and standards for distance education courses were reworked in collaboration with
the Educational Technology Advisory Committee and Chancellors Office staff and
accepted by the Board of Governors in July 2007 (CCCCO, 2011). Today, California has
the largest community college system in the United States with 115 colleges.
Additionally, California community colleges educate 70 percent of our state’s nurses,
train 4 out of 5 emergency medical technicians, firefighters, and law enforcement
personnel (CCCC0, 2016). Nationally, in the Fall of 2013, more than 50% of first-time
Latino/a college learners begin their higher education journey in a 2-year college or
community college (American Association of Community Colleges, 2015). By fall 2013,
Latinos/as represented 40% of learners enrolled in the California Community College
system (CCCCO, 20). Regrettably, few Latinos/as complete a college degree once in
community colleges, largely due to elevated involvement in developmental education
(Acevedo-Gil, Santos, & Solórzano, 2014). A learner’s initial assignment to
developmental courses can place them on a route that effects their academic success
(Acevedo-Gil et al., 2014; Solórzano, Acevedo-Gil, & Santos, 2013).
To help offer advice and guidance, the Board of Governors created a distance
education technical advisory committee to assess the status of distance education classes
in California (CCCCO, 2011). The advisory committee focused on the technology,
design of pedagogy, and instructional systems for learners who are not in the same
geographical location as the instructor. As the methods available for distance education
change, more options are being utilized to deliver online classes globally. Learners can
use YouTube.com, iTunes, and the comparatively new Massive Open Online Courses
(MOOC); which are permitting greater entry to countries who do not have any online

51. 45
college course system or distance education. No statistics regarding the percentage of
MOOCs exist at this time for community colleges in California. The percentage of online
courses includes all forms of online; publisher’s sites, BlackBoard, and other in-house
Web-based platforms of delivery methods. While television-based classes were
decreasing, Internet-based classes were increasing. The total number of distance
education course sessions provided over the Internet expanded by 112% over the five-
year period (CCCCO, 2013).
Between the years of 2005 and 2011, twenty percent of community college
learners in the United Stated attend a California community college and 52% of
California State University and 28% of University of California graduates began at a
California community college (CCCCO, 2014). In 2014 California Governor Jerry Brown
signed legislation that allows a limited number of community college districts to create
baccalaureate degree programs in fields of study not currently offered by University of
California or by the California State University systems CCCCO, 2016). In response, the
California community college system is launching a pilot program allowing 15
community colleges across California to offer bachelor’s degrees in vocational fields.
The historic program provides learners with economical degree alternatives in fields with
an increasing demand for workers and a growth in employers wanting bachelor’s degrees
(CCCCO, 2016).
Educators and policymakers continually debate the effectiveness of current
developmental education offered at community colleges. Limited studies have been
performed to ascertain if developmental education contributes to the academic success of
learners (Emrey-Arras, 2013). The Government Accounting Office (GAO) has proposed

52. 46
a research center to focus on developmental education to assist policymakers and
instructors on improving learner outcomes (Emrey-Arras, 2013); however, currently the
GAO site does not list such a center. From the 1800’s, the debate on the effectiveness of
developmental education at the postsecondary level has continued (Brothen & Wambach,
2012). Researchers have been unable to show definitively that developmental courses at
the secondary level provide learners with enriched skills (Crisp & Delgado, 2014; Scott-
Clayton & Rodriguez, 2015). Some of the debate are over where and when to teach
postsecondary developmental education (Wilson, 2012). Several states such as Colorado,
Florida, North Dakota, New York, Louisiana, Tennessee, Missouri, and Minnesota have
guidelines preventing four-year institutions from offering developmental education
courses (Jacobs, 2012). However, at the two-year college level, the states of Nebraska,
Virginia, Oregon, and Indiana merely advise students to enroll in developmental
education courses (Wilson, 2012). Another debate is over whether unprepared learners
benefit from postsecondary developmental education (Melguizo, Bos, & Prather, 2011).
Math ability at the time of college entrance was found to be a powerful predictor of
student success, but developmental math classes had no effect (Bremer et al., 2013).
Neither developmental English, reading, and writing nor developmental math classes
helped students' GPA in non-developmental classes in the same disciplines (Bremer et
al., 2013). Notably, tutoring and financial aid showed a much greater relationship to
student success than developmental coursework (Bremer et al., 2013). A limitation of the
Bremer (2013) study is the definition of remediation as a binary treatment: either a
learner is placed into developmental education or placed in a college-level course. This is
problematic since developmental education in community colleges is usually offered as a

53. 47
sequence of courses (Bahr, 2012; Melguizo, Kosiewicz, Prather, & Bos, 2014). Normally,
learners delegated to developmental math education must sequentially pass each assigned
course in the series before they can register in college-level courses.
In addition to the controversies above, there are other matters. Educators and
legislators debate methods to increase completion rates of learners in developmental
courses. Moreover, they seek to ascertain the graduation rate of community college
learners who took a developmental course (Bahr, 2012).
Assessment Procedures to Determine College-Level Readiness
Learners who seek to enroll in most universities and colleges are required to take
placement tests. While the high-stakes tests do not determine academic needs and are not
diagnostic, they permit the college administration structures to work more cost-
effectively (Hughes & Scott-Clayton, 2011). Unfortunately, the majority of learners take
placement tests without preparing for the test (Fay, Bickerstaff, & Hodara, 2013). Some
researchers have found high school classes are better forecasters of course placement and
achievement in college (Belfield & Crosta, 2012; Ngo, Kwon, Melguizo, Prather, & Bos,
2013).
Various types of tests are utilized to ascertain the appropriate placement for the
learner. Learners who score does not qualify for college-level courses are placed in a
developmental course based on their test score (Wilson, 2012). By utilizing placement
tests, educators presume two things. First, that learners with a certain score are
adequately prepared for college. Second that a low score indicates that learners are
deficient in academic skills and will benefit from a developmental course (Belfield &
Crosta, 2012; Scott-Clayton, Crosta & Belfield, 2014). Learners from the one summer

54. 48
course and three fall courses of Basic Mathematics at southwestern community college
were surveyed, and 28% of the learners stated they had been improperly placed (Goeller,
2013). Furthermore, the learners’ comments showed they did not think the subject matter
of the placement test and the curriculum in Basic Mathematics were the same. Moreover,
interesting discrepancies for learners who scores were near the cutoff emerged. Learners
whose scores were just above the cutoff did not stay in college as long as learners whose
scores were just below (Hughes and Scott-Clayton, 2011). The results pose questions as
to the suitability of existing placement tests and cutoff scores. Policymakers believe
standardized placement tests and cutoff scores across all colleges would be more
effectual. Postsecondary educators would be able to place whose scores are near the
cutoff scores in the appropriate classes (Hughes & Scott-Clayton, 2011; Scott-Clayton et
al., 2014 According to critics, research indicates that using cutoff scores is unreliable for
learners who test near the cutoff (Hughes & Scott-Clayton, 2011; Scott-Clayton et al.,
2014). Additionally, some learners with scores marginally below the cutoff ignore
recommendations and enter college-level classes. These learners are just as successful as
learners in the class who scored marginally above (Hughes & Scott-Clayton, 2011).
Community college educators articulate the testing system does not always result in
learners being placed in the appropriate developmental class. Simply based on their test
score, numerous learners enroll in developmental classes (Hughes and Scott-Clayton,
2011). On the basis of their ACT scores, some learners are eligible for college-level
classes. Consequently, there are discrepancies among placement tests and entrance exams
(Saxon & Morante, 2014; Scott-Clayton et al., 2014). Qualitative investigations into
developmental education have revealed diversity in what style of placement and

55. 49
assessment policies institutions adopt (Melguizo et al., 2014). Melguizo et al. (2014)
examined the placement and assessment policies at community colleges and described
considerable variation in the method the nine colleges of the district were using to assess
and place students for the same developmental math courses. The inconsistencies and the
concerns addressed above complicate the matter of appropriate placement in
developmental classes (Belfield & Crosta, 2012; Burdman, 2012; Hughes & Scott-
Clayton, 2011; Scott-Clayton et al., 2014).
The Education Policy Improvement Center (EPIC) identifies four key areas in
determining readiness for college, content strategies, content knowledge, learning skills
and techniques, and transition knowledge and skills (Conley, 2012). Assessment exams
measure just two of the four college-readiness factors (Conley 2012). Researchers also
argue that colleges should be focused more on psychosocial or non-cognitive aspects to
comprehend the activities that shape learners’ capacity to learn, such as motivation and
academic persistence (Farrington et al., 2012).
Multiple measure assessment measures (e.g., prior academic coursework, non-
cognitive factors) have shown promise in assisting in placing learners in the level that
matches their ability (Fong, Melguizo, & Prather, 2015; Ngo & Kwon, 2014). Fong et al.
also found that utilizing multiple measures of assessment increases the probabilities of
passing each level. Ngo and Kwon (2014) described similar findings and determined that
learners who were placed in a higher level of developmental math because of a multiple
measure assessment for prior math education and high school GPA functioned just as
well as their peers. Therefore, procurement of multiple measure points is predictive of
learner success. Some community college organizations, such as Texas, have begun

56. 50
utilizing multiple measures in the placement and assessment process of learners (Texas
Higher Education Coordinating Board, 2013).
Developmental Math: Help or Hindrance?
Developmental math courses offered at community colleges prepare students to
take introductory college level mathematics—a prerequisite for most degrees (Ashby et
al., 2011; Bailey et al., 2010; Xu & Jaggars, 2011). However, estimates indicate 60%
percent of community college students are referred to a developmental math course
(Bailey et al., 2010) with 7% of those enrollments taking place in online courses (Xu, &
Jaggars, 2014). Moreover, of those online enrollments, only 20% will complete their first
college-level math course compared to 32% who completed their developmental math
sequence in a face-to-face class (Jaggars et al., 2013; Xu & Jaggars, 2011; Xu & Jaggars,
2013).
Since 2012, research on developmental education has studied success centered on
multiple levels of the remedial sequence (Bahr 2012; Melguizo, Bos, Ngo, Mills, &
Prather, 2016; Weiss, Visher, Weissman, & Wathington, 2015). Bahr (2012) investigated
the stages in developmental sequences to explore the degree to which learners placed into
lower levels experienced differential attrition related to learners placed into higher levels.
Employing a regression-discontinuity design within a discrete-time hazard analysis,
Melguizo et al. (2016) assessed the effect of placing into lower levels of developmental
math on passing the following course and accruing 30 transferable and 30 degree-
applicable credits. Both conducted their investigations in California, Bahr studied 104
California semester-based community colleges from 1995 to 2001 while Melguiso et al.
(2016) was conducted in seven colleges in the Los Angeles Community College District