The document summarizes a study that examined the relationships between psychological constructs in Garrison's model of self-directed learning, including self-management, self-monitoring, and motivation. 119 undergraduate students completed a questionnaire measuring these constructs. Results showed the constructs are interrelated and motivation mediates the relationship between self-management and self-monitoring. Self-management and self-monitoring significantly predicted academic achievement over two semesters, with self-management being the strongest predictor. Motivation significantly predicted achievement in the second semester only. The implications for self-directed learning and academic achievement are discussed.

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Sabry M. Abd-El-Fattah
Garrison's Model of Self-Directed Learning: Preliminary Validation and Relationship to
Academic Achievement The Spanish Journal of Psychology, vol. 13, núm. 2, 2010, pp.
586-596,
Universidad Complutense de Madrid
España
How to cite Complete issue More information about this article Journal's homepage
The Spanish Journal of Psychology,
ISSN (Printed Version): 1138-7416
psyjour@sis.ucm.es
Universidad Complutense de Madrid
España
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Non-Profit Academic Project, developed under the Open Acces Initiative

2. 586
Inthisproject,119undergraduatesrespondedtoaquestionnairetappingthreepsychologicalconstructs
implicated in Garrison’s model of self-directed learning: self-management, self-monitoring, and
motivation. Mediation analyses showed that these psychological constructs are interrelated and that
motivation mediates the relationship between self-management and self-monitoring. Path modeling
analyses revealed that self-management and self-monitoring significantly predicted academic
achievement over two semesters with self-management being the strongest predictor. Motivation
significantly predicted academic achievement over the second semester only. Implications of these
findings for self-directed learning and academic achievement in a traditional classroom setting are
discussed.
Keywords: self-directed learning, self-management, self-monitoring, motivation, path modeling, mediation
analysis.
En este trabajo, 119 estudiantes posgraduados fueron evaluados de acuerdo con un cuestionario que
mide tres constructos psicológicos implicados en el modelo de Garrison sobre aprendizaje autodirigido:
autogestión, autoseguimiento, y motivación.Los análisis de mediación mostraron que estos constructos
psicológicos están interrelacionados y que la motivación media en la relación entre autogestión y
autoseguimiento. Los análisis de Path modeling indicaron que la autogestión y el autoseguimiento
predecían significativamente el logro académico a lo largo de dos semestres en los que la autogestión
era el mejor indicador. La motivación predecía significativamente el éxito académico sólo a lo largo
del segundo semestre. Se discuten las implicaciones que estos resultados pueden tener sobre el
aprendizaje autodirigido y el éxito académico en el contexto de un aula tradicional.
Palabras clave: aprendizaje autodirigido, autogestión, autoseguimiento, motivación, Path modeling,
análisis de mediación.
Garrison’s Model of Self-Directed Learning:
Preliminary Validation and
Relationship to Academic Achievement
Sabry M. Abd-El-Fattah
Minia University (Egypt)
Correspondence concerning this article should be addressed to Sabry M. Abd-El-Fattah. Department of Educational Psychology.
Faculty of Education. Minia University. Minia. (Egypt). E-mail: sabryrahma@hotmail.com
Copyright 2010 by The Spanish Journal of Psychology
ISSN 1138-7416
The Spanish Journal of Psychology
2010, Vol. 13 No. 2, 586-596

3. GARRISON’S MODEL OF SELF-DIRECTED LEARNING 587
Self-directed learning (SDL) is a central concept in
the study and practice of adult education (Brockett &
Hiemstra, 1991). As important as the construct is to adult
education, little attention has been directed to the cognitive
and motivational factors implicated in SDL (Brookfield,
1986; Knowles, 1975). Long (1989, p. 3) stressed the
importance of the psychological perspective in SDL and
argued that “The critical dimension in self-directed learning
is not the sociological variable, nor is it the pedagogical
factor. The main distinction is the psychological variable.”
Furthermore, the overriding theme of SDL has mainly
been the external management of the learning process.
According to this perspective, the learner exercises a great
deal of independence in deciding what is worthwhile to
learn and how to approach the learning tasks, regardless
of entering competencies and contextual contingencies
(Brookfield, 1986). As such, SDL has largely been defined
in terms of external control and facilitation, rather than
internal cognitive processing and learning.
To address these concerns, Garrison (1997) proposed
a SDL model which integrated external management
(contextual control), internal monitoring (cognitive
responsibility), and motivational (entering and task) factors
associated with learning in an educational context. Garrison
defined SDL as an approach where learners are motivated
to assume personal responsibility and collaborative control
of the cognitive (self-monitoring) and contextual (self-
management) processes in constructing and confirming
meaningful and worthwhile learning outcomes. Garrison’s
collaborative perspective of SDL has the learner taking
responsibility for constructing meaning (cognitive
perspective) while including the participation of others in
confirming worthwhile knowledge (social perspective).
According to Garrison (1997), self-management
concerns task control issues including the enactment of
learning goals and the management of learning resources
and support. The essence of the term can be found in the
self-regulated motivational literature (Pintrich & DeGroot,
1990). Task control is determined by balancing the factors
of proficiency, resources, and interdependence (Garrison,
1993). Proficiency represents the abilities and skills of the
facilitator and the learner. Resources encompass a range of
support and assistance available in an educational setting.
Interdependence reflects institutional or subject norms and
standards as well as a learner’s integrity and choice. Self-
management of learning tasks represents a collaborative
experience between the teacher and the learner. The
teacher maintains an appropriate dynamic balance of
external control necessary for successful educational
outcomes (Prawat, 1992; Resnick, 1991).
Another component of Garrison’s SDL model is self-
monitoring. It addresses the cognitive and metacognitive
processes which include monitoring the repertoire of
learning strategies as well as awareness and an ability
to think about our thinking. It is the process whereby the
learner takes responsibility for the construction of personal
meaning through integrating new ideas and concepts
with previous knowledge. It facilitates a metacognitive
perspective on learning and a generalized ability to
learn reflectively. Reflective learning can help “develop
learners who are capable of monitoring themselves in a
variety of situations” (Candy, Harri-Augstein, &Thomas,
1985, p. 115). The teacher can provide effective feedback
to help the learner self-monitoring the quality (meaning
and validity) of the learning outcome because internal
feedback alone may lack accuracy and explicitness. The
degree of self-monitoring will depend upon the learner’s
proficiency (abilities and strategies) in conjunction with
contextual and epistemological demands (Butler & Winne,
1995; Garrison, 1991).
Lastly, motivation helps initiate and maintain effort
towards learning and the achievement of cognitive
goals. Motivation includes entering motivation and task
motivation. Entering motivation establishes commitment
to a particular goal and the intent to act. It can be perceived
as “commitment--the coming together of attitudes, feelings,
and goals” (Thompson, 1992, p. 103). Task motivation is
the tendency to focus on and persist in learning activities
and goals. As Corno (1989) suggested, “Motivational
factors . . . shape intentions and fuel task involvement”
(pp. 114-115). It is hypothesized that entering motivation
is determined by valence and expectancy. Valence reflects
the attraction to particular learning goals. The factors
that determine valence are personal needs (values) and
affective states (preferences). In a learning context,
expectancy refers to the belief that a desired outcome can
be achieved (Pintrich & DeGroot, 1990). Task motivation
is integrally connected to task control, self-management,
and the issue of volition. In a learning context, “volition
refers to bringing discordant affective and executional
preferences in line with one’s task goals” (Kanfer, 1989,
p. 381). Volition is concerned with sustaining intentional
effort or diligence that can influence persistence and task
performance (Pintrich & DeGroot, 1990).
The literature on SDL asserts that self-directed learners
demonstrate great awareness of their responsibility in
making learning meaningful and monitoring themselves
(Garrison, 1997). They were found to be curious and willing
to try new things, view problems as challenges, desire
change, and enjoy learning (Temple & Rodero, 1995).
Guthrie, McGough, Bennett, and Rice (1996) reported
that in a Concept-Oriented Reading Instruction (CORI)
program, self-directed learners demonstrated the ability to
search for information in multiple texts, employ different
strategies to achieve goals, and to represent ideas in different
forms (drawing and writing). Morrow and Young (1997)
observed that with proper planning and implementation,
self-directed learning can encourage students to develop
their own rules and leadership patterns. Thus, self-
directed learners take responsibility for the construction

4. ABD-EL-FATTAH588
of personal meaning, initiation and maintenance of effort
toward learning tasks, and achievement of cognitive goals,
and therefore they are more likely to be promoted as high
achievers (Garrison, 1997).
Linking self-directed learning to academic
achievement
For example, several studies have reported a significant
positive relationship between SDL and academic
achievement in a traditional classroom setting (Darmayanti,
1994), a non-web based distance learning setting (Hsu &
Shiue, 2005), a web-based learning setting (Haron, 2003),
and a distant education setting (Harriman, 1990). Other
studies have reported a significant positive relationship
within a specific content areas including nursing (Savoie,
1979), social and political sciences (Anderson, 1993),
business (Morris, 1995), business, communication, public
administration, and hospitality management (Ogazon, 1995),
and biology (Haggerty, 2000). In a recent study, Stewart
(2007), for example, found a positive relationship between
SDL readiness and the overall learning outcome ratings in
project-based learning in engineering with self-management
being the strongest predictor. Self-control and desire for
learning significantly predicted the overall learning outcome
ratings but their R2
values were relatively low.
Rational of the study
Self-monitoring is intimately linked to the external
management of reaming tasks and activities. An interesting
and important issue arises with regard to responsibility
(self-monitoring) and control (self-management) within
a learning context is whether responsibility must precede
control or vice versa.Although theoretically they go hand in
hand, it is very difficult for learners to assume responsibility
for their own learning without feeling they have some
control over the educational transaction. Without choice
and collaboration, it may well be unrealistic to expect
students to assume responsibility for their learning.
Meanwhile, absolute learners’ control may adversely
affect or reduce the efficiency of achieving quality learning
outcomes. There is research evidence that collaborative
control results in more effective self- monitoring and,
thereforeimprovedperformance.Inaddition,sharingcontrol
of learning activities and tasks provides opportunities for
instructional support while encouraging students to assume
cognitive responsibility (Butler & Winne, 1995).
The inseparability of monitoring and managing of the
learning process is further complicated by motivational
concerns. According to Garrison (1997, p. 9), “Motivation
reflects perceived value and anticipated success of learning
goals at the time learning is initiated and mediates between
context (control) and cognition (responsibility) during
the learning process.” Anticipated control is an important
perception when assessing expectancy of success and
making decisions regarding goal-directed behavior. It is
believed that control expectations “influence the direction
of much of our behavior; they help to determine where we
invest our achievement energies” (Weisz, 1983, p. 234). If
students are to have an expectation of control, they must
have some choice over their educational goals. Providing
opportunities for control and choice from the beginning
can significantly strengthen the entering motivational
state, which subsequently influences whether students will
take responsibility to be self-directed and persist in their
learning tasks.
Although the linkages among the psychological traits
implicated in Garrison’s SDL model may be theoretically
meaningful and conceptually sound, such relationships
have not been empirically validated using real world
data. In addition, most of the studies on SDL have been
conducted using Western samples (Garrison, 1997). It is,
therefore, imperative to examine the practical implications
of Garrison’s SDL model in Non-western contexts to
determine the generalizability of the findings reported
using Western samples. Actually, it is possible that careful
examination and validation of Garrison’ SDL model in
Non-western contexts bring about profitable use of the
model and its applications. On the other hand, uncritical
use of Garrison’s SDL model in Non-western contexts
to make students more self-directed learners may not
necessarily result in better academic outcomes, since
countries may differ in values and beliefs about education
and opportunities offered to students.
Thus, one goal of the present study is to examine the
relationships among self-management, self-monitoring,
and motivational factors, associated with learning in an
educational context. Of particular interest is to investigate
whether motivational factors may mediate the relationship
between self-management and self-monitoring. A second
goal of the present study is to examine the relationships of
these psychological constructs to academic achievement
measured over two semesters in a traditional classroom
setting. Of particular interest is to relate academic
achievement measured over two semesters to the earlier
collected psychological indices.
Methods
Participants
Subjects of the present study included 119 first year
undergraduates enrolled in an education program in a
public university in Minia, Egypt. There were 65 males and
54 females. Fifty-six students majored in science and 63
majored in arts. The mean age of the participants was 18.7
years (SD = 1.7) with a range from 18 to 21 years.

5. GARRISON’S MODEL OF SELF-DIRECTED LEARNING 589
Measurements
The Self-Directed Learning Aptitude Scale. The
instrument most widely used in educational research to
measure SDL readiness is Guglielmino’s (1977) Self-
Directed Learning Readiness Scale (SDLRS). Based on
problems with validity testing of the SDLRS, Field (1989)
suggested discontinuing this tool. Furthermore, several
studies have raised questions about the reliability of the
SDLRS when used in different racial and class populations
(Straka 1995). Even though scales such as the SDLRS
have been developed, they do not translate easily and are
not readily available and incur a cost for their use. Thus,
the development of a new scale should allow for these
problems to be addressed.
The Self-Directed Learning Aptitude Scale (SDLAS)
was developed for the purpose of the present study. The
initial item pool comprised 40 items designed to measure
students’ aptitude to SDL based on a review of the research
of Knowles (1975), Brookfield (1986), and Garrison (1991,
1993). Using a Delphi technique (Sharicey & Sharpies,
2001), the members of an expert panel independently
assessed each of the 40 items using a 5-point scale.Ascore of
1 denoted Absolutely Inappropriate and a score of 5 denoted
Absolutely Appropriate. Panel members were given space
to modify each item if they chose. Following the guidelines
suggested by Kline (1986) and Crocker and Algina (1986),
an item was retained when the panel consensus for that
item was of at least 80% of appropriateness. Items where
consensus was not achieved, but where less than 20% of the
panel denoted Inappropriate (i.e. 80% either Appropriate,
Absolutely Appropriate, or were Unsure) were retained for
the subsequent round.Atotal of 28 items were retained after
two rounds of the Delphi technique.
Procedures
Students were recruited to participate voluntary during
their normal classes at their university.All participants were
informed that they could deny their participation in data
collection without any explanation, penalty, or cost to them.
The initial 28 items of the SDLAS were administered to the
sample of the study during the third week of the 2008/2009
academic year. Students were asked to describe themselves
by indicating on a 4-point Likert-type scale the extent to
which each item was descriptive of their own characteristics
with a score of 1 denoted Strongly Disagree and a sore of
4 denoted Strongly Agree. In addition, students’ academic
achievement scores over two semesters were obtained,
with permission, from the university records. These scores
were the courses aggregated total score (i.e., the sum of on-
courses assignments and examinations score).
Results
Exploratory factor analysis
Anexploratoryfactoranalysiswithprincipalcomponents
was conducted to identify a viable factor structure of the 28-
item pool of the SDLAS. The resulting factors were rotated
to a simple structure using oblimin rotation. The number of
factors retained was determined using the following criteria:
(1) Kaiser’s rule of retaining factors with eigenvalues greater
than 1, (2) Cattell’s Scree test of the plot of eigenvalues, and
(3) each factor had to have at least three items. Inclusion
criteria for items on the retained factor were that they had
loadings of at least .30 on that factor. Items with high cross-
loadings, wherein an item had a loading of .30 or greater
on more than one factor, were assigned to a factor on the
basis of logical fit. A corrected item-total correlation of .30
or above was required to confirm the assignment decision.
The factors that were identified were named on the basis of
their content (Nunnally, 1978).
The analysis retained three factors: self-management (8
items), motivation (9 items), and self-monitoring (9 items).
These three factors explained 22, 18, and 15 per cent of
the total variance extracted respectively. The loadings of
two items, “I enjoy studying” and “I would like to make
decisions for myself” fall substantially below the traditional
accepted criteria of .30 for retained item loadings (Velicer
& Jackson, 1990) and therefore were dropped from the
scale. The 26 items of the SDLAS and their loadings on
corresponding factors are shown in Table 1. There were no
mean differences in self-management, motivation, or self-
monitoring, reported in Table 2, due to the effect of gender
or academic major (p > .05).
Path modeling
The relationships among self-management, motivation,
self-monitoring, and academic achievement over two
semesters were analyzed through path modeling, using the
partial least squares (PLS) method. This analytical procedure
was chosen because it does not hinge upon large samples, it
can be used with both formative and reflective constructs,
and it does not make assumptions about the underlying data
distribution when estimating the model parameters (Chin &
Newsted, 1999; Ringle, Wende, & Will, 2005). The software
used was SmartPLS 2.0 (Ringle et al., 2005).
In practice, a PLS model is developed in two stages.
In the first stage, the measurement model is tested by
performing reliability and discriminative validity analyses
on each of the measures to ensure that reliable and valid
measures of the constructs are being employed. In the
second stage, the inner or structural model is tested by
estimating the paths between the constructs, determining
their significance as well as the predictive ability of the
model. The PLS procedure calculates an estimate for each

6. ABD-EL-FATTAH590
construct or latent variable derived from corresponding
observed variables and thus partitioning the hypothesized
inner model into its component constructs. To evaluate the
model against observed data, an iterative procedure fits
observed measures to corresponding latent variables, then
estimates the relationships between the latent variables. A
least squares fit between observed and modeled parameters
is computed. A best-fit solution is regarded as found when
the least squares function stabilizes between iterations
(Ringle et al., 2005).
Since the approach is variance-based (as distinct from
covariance-based), the PLS procedure has been described
as soft modeling, and is claimed to be more useful in
investigating descriptive and predictive relationships
than confirmatory analysis (Sellin & Keeves, 1997). A
readable review of the procedure has been published
by Haenlein and Kaplan (2004). Two recent examples
of researchers using the PLS method within behavioral
research are Abd-El-Fattah (2005) and Boman, Smith,
and Curtis (2003).
Table 1
Oblimin factor loadings, PLS loadings, T values, average variance extracted (AVE), and composite and Cronbach’s alpha
reliability coefficients of the Self-Directed Learning Aptitude Scale
Factor/Statistic
Factor
loadings
PLS
loadings
T
values*
AVE
Composite
reliability
Cronbach’s
alpha
Self-management .73 .75 .82
1. I am well-organized in my learning. .83 .81 8.3
8. I set up strict timeframes to learn something new. .80 .77 6.7
14. I have good management skills. .75 .68 10.3
7. I set up planned solutions to solve my problems. .74 .73 9.4
3. I can decide about the priority of my work. .73 .72 11.3
2. I can manage pursuing my own learning. .72 .67 5.5
5. I prefer to plan my own learning. .70 .65 6.4
18. I am efficient in managing my time. .68 .60 8.3
Motivation .70 .80 .84
2. I take the challenge to learn. .80 .76 7.6
15. I am a ‘why’ person. .78 .80 10.8
11. I critically evaluate new ideas and knowledge. .75 .73 5.6
22. I would like to evaluate the level of my learning progress. .71 .65 4.9
26. I would like to learn from my mistakes. .67 .77 7.8
9. I believe in effort to improve my performance. .66 .63 5.6
1. I enjoy learning new things. .65 .62 4.8
17. I trust my abilities to learn new things. .64 .73 8.3
12. I have positive expectations about what I am learning. .62 .75 10.7
Self-monitoring .68 .81 .86
6. I am a ware of my own weaknesses. .81 .76 12.7
4. I can link pieces of information when I am learning. .80 .77 8.5
13. I pay attention to all details before taking a decision. .77 .73 4.7
19. I would like to set up my goals. .75 .72 6.3
23. I correct myself when I make mistakes. .71 .80 8.9
25. I am a responsible person. .69 .74 10.4
21. I judge my abilities fairly. .66 .73 7.5
24. I think deeply when solving a problem. .65 .68 6.6
16. I prefer to set up my criteria to evaluate my performance. .63 .70 10.3
Note. N = 119. *T-values are significant for all instances, p < .01.

7. GARRISON’S MODEL OF SELF-DIRECTED LEARNING 591
The assessment of the measurement model
In the PLS method, a reflective measurement model is
assessed by evaluating: (a) the reliability of single items,
(b) the convergent validity of a construct, and (c) the
discriminant validity of a construct (Hulland 1999).
For the assessment of individual item reliability, a rule
of thumb recommends accepting items with loadings of at
least .5, particularly when new items or newly developed
scales are employed (Chin, 1998; Hulland 1999). Table 1
shows that all items loaded on their corresponding factors
at .60 or higher (t > 4.7, p < .01 for all items).
For the assessment of convergent validity (also referred
to as internal consistency) of a construct, a rule of thumb
recommends accepting a construct with Cronbach’s alpha
and/or composite reliability coefficient that exceeds the
threshold of .6 as proposed by Bagozzi and Yi (1988), or .7
as suggested by Nunnally (1978). Table 1 shows that both
Cronbach’s alpha and the composite reliability coefficients
for self-management, motivation, and self-monitoring
were > .75. Another index that is used to assess a construct
convergent validity is the average variance extracted
(AVE). The AVE measures the amount of variance captured
by the construct relative to the amount of variance due to
measurement error. The AVE should exceed a value of .5 to
highlight a construct convergent validity (Fornell & Larcker
1981). Table 1 shows that the AVE for self-management,
motivation, and self-monitoring were > .68.
For the assessment of discriminant validity of a construct,
a rule of thumb recommends accepting a construct if the
square root of the AVE of that construct is larger than the
Pearson’s correlation coefficients of that construct to all
other latent constructs in the model (Fornell & Larcker,
1981). Unfortunately, guidelines about how much larger the
AVE should be than these Pearson’s correlation coefficients
are not available (Gefen, Straub, & Boudreau, 2000).
Table 3 shows that the square roots of the AVEs for self-
management, motivational factors, and self-monitoring are
greater than the Pearson’s correlation coefficients of these
constructs with each other. Based on the findings, one can
concluded that the measurement model fits the present data
set adequately.
The assessment of the structural model
To evaluate the specified structural model, displayed in
Figure 1, one should report the standardized ß coefficients
and t values along with the R² (Chin 1998; Hulland 1999).
The standardized ß coefficient is a measure of the strength
of the relationship between an independent and a dependent
variable while holding constant the effects of the remaining
Table 2
Gender and academic major mean differences in self-management, motivation, self-monitoring
Variables /Statistic n M SD F (1,117)
Gender
Self-management 1.3
Males 65 23.7 3.4
Females 54 22.9 3.2
Motivation .97
Males 65 24.9 3.6
Females 54 24.3 3.0
Self-monitoring .83
Males 65 26.3 4.2
Females 54 25.7 3.5
Major
Self-management 1.4
Science 56 21.6 2.9
Arts 63 20.9 2.5
Motivation .61
Science 56 25.7 3.8
Arts 63 25.3 3.4
Self-monitoring .67
Science 56 23.7 4.3
Arts 63 23.2 3.8
Note. N = 119. df = 1, 117. * F-values are nonsignificant for all instances, p > .05.

8. ABD-EL-FATTAH592
independent variables. The R² represents the proportion of
variance on a dependent variable explained by all variables
jointly. A t statistics is used to examine the significance of
the parameter estimates (Hulland, 1999). In sum, it was
found that self-management significantly predicted all
constructs in the model (p < .01). Motivation significantly
predicted self-monitoring and achievement in Semester 1
(p < .01) but not achievement in Semester 2 (p > .05). Self-
monitoring significantly predicted academic achievement
over the two semesters (p < .01). Finally, academic
achievement in Semester1 significantly predicted academic
achievement in Semester 2.
Table 3
Pearson’s correlation coefficients, the square root of average variance extracted (AVE), means, and standard deviations of
self-management, motivation, and self-monitoring
Variables 1 2 3 M SD
1. Self-management .87 .36** .31* 3.2 .74
2. Motivation .84 .34** 2.8 .64
3. Self-monitoring .82 3.4 .66
Note. N = 119. Values on diagonal represents the square root of the average variance extracted (AVE) statistic as used within PLS.
Means are expressed along a four point type-Likert scale, 1-4, summed across items.*p < .05; **p < .01.
Figure 1. PLS model of the relationship among self-management, motivation, self-monitoring, achievement in Semester 1, and achievement
Semester 2 (Dashed line indicates nonsignificant effect)
.81
.77
.68
.73
.72
.67
.65
.60
.76 .80 .73 .65 .77 .63 .62 .73
.75
.76
.77
.73
.72
.80
.74
.73
.68
.70
.29
(t = 6.74)
.31
(t = 5.52)
.30
(t = 6.51)
.36
(t = 7.62)
.32
(t = 7.53)
.35
(t=
8.42)
.28
(t = 7.53)
.12
(t=1.32)
.41
(t = 9.33)
.31(t=3.62)
R2 = .09
R2 = .19
R2 = .30 R2 = .40
Motivation
Achievement
Semester1
Achievement
Semester 2
Item 2 Item15 Item 11 Item 22 Item 26 Item 9 Item 10 Item 17 Item 12
Self-
managment
Item 18
Item 5
Item 20
Item 3
Item 7
Item 14
Item 8
Item 1
Self-
monitoring
Item 6
Item 4
Item 13
Item 19
Item 23
Item 25
Item 21
Item 24
Item 16

9. GARRISON’S MODEL OF SELF-DIRECTED LEARNING 593
Mediation analysis
According to Baron and Kenny (1986, p. 1176), a
variable may be considered a mediator “to the extent that
it accounts for the relation between the predictor and the
criterion”. The mediation model tested in the present study
conceptually denotes self-management as an independent
variable (X), motivational as a mediator variable (M), and
self-monitoring as a dependent variable (Y). Gender and
academic major were also included as covariates (COVs) to
test for their possible partial effect on self-monitoring (Y).
The SPSS script developed by Christopher Preacher from
the University of Kansas and Andrew Hays from the Ohio
State University (Preacher & Hayes, 2004) was used to run
the mediation analysis and the results are summarized in
Table 4.
Baron and Kenny technique showed that self-
managementsignificantly predicted self-monitoring(b=.78,
SE = .07, t = 11.14, p < .01), self-management significantly
predicted motivation (b = .67, SE =.08, t = 8.38, p < .01),
and motivation significantly predicted self-monitoring
(b = .74, SE = .07, t = 10.57, p < .01). When motivation
was controlled for, self-management significantly predicted
self-monitoring (b = .66, SE = .08, t = 8.25, p < .01). Gender
(b = .13, SE = .11, t = 1.18) and academic major (b = .17,
SE = .10, t = 1.70), as covariates, did not have significant
effects on self-monitoring (p > .05).
The Sobel test showed identical findings to that of Baron
and Kenny technique (effect (z) = .55, SE = .10, z = 5.5, p <
.05). The ‘effect’ in Sobel test represents the indirect effect
of the mediator. This indirect effect is the product of paths
ab and is equivalent to (c - c’) where path c is the total effect
and c’ is the direct effect (Sobel, 1982).
The bootstrapping technique (Chernick, 1999) further
supported the mediation effect of motivation with a value
of zero not included within the lower bound (.26) and the
upper bound (.73) of the 95% bias-corrected and accelerated
bootstrap confidence intervals. Based on these findings, one
can conclude that the motivation mediated the relationship
between self-management and self-monitoring.
Discussion
The present analyses revealed several notable
findings. First, self-management significantly predicted
self-monitoring. This means that increasing a learner’s
control through self-management brings with it elevated
responsibilities, particularly with regard to the learning
process itself and the construction of meaning. The
immediate benefit of increased self-management is
increased awareness of the need to make learning more
meaningful, that is, to take greater responsibility in the
monitoring of the learning process itself. It is difficult to
get learners to accept responsibility for meaningful learning
outcomes when they have little control of, and input into,
the learning process (Garrison, 1991, 1997).
However, as necessary as a sense of control is, without
appropriate support and guidance, learners may not persist
or achieve the desired educational outcomes. From an
educational perspective, quality of learning outcomes is
not simply a question of student control and responsibility.
The teacher also plays an integral role in education as a
transactional process and has his or her own legitimate and
often necessary control and responsibility concerns. It is the
teacher who is charged with the responsibility of clarifying
goals, shaping learning activities, and assessing learning
outcomes. Furthermore, in collaboration with students,
teachers are responsible for establishing the balance of
control by providing support, directions, and standards to
ensure worthwhile outcomes and continuing efforts to learn
(Garrison, 1993).
The second notable finding concerned motivation as
a mediator of the relationship between self-management
and self-monitoring. Learners are intrinsically motivated
to assume responsibility for constructing meaning and
understandingwhentheyhavesomecontroloverthelearning
experience. Garrison (1997) theorized that “Motivation
reflects perceived value and anticipated success of learning
goals at the time learning is initiated and mediates between
context (control) and cognition (responsibility) during the
learning process.” (p. 9). Garrison (1993) also proposed
that “Control and choice strengthen motivation, which in
turn builds a sense of responsibility” (p. 30) Consistent
with Peters (1998), consideration should be given to
metacognitive and motivational factors if SDL is to be a
viable and relevant concept to education in 21st century. It
would seem that, for any student, SDL is predicated on the
student having earned how to learn and having acquired the
necessary epistemic and metacognitive awareness. If SDLis
about controlling one’s learning and achieving meaningful
and worthwhile outcomes, then epistemic cognition (i.e.,
cognitive development), metacognitive awareness, and
motivational factors are preconditions.
Garrison’s (1997) model of SDL has considerable
potentials to integrate issues related to motivational
disposition (entering task), strategic planning (self-
management), and metacognitive awareness (self-
monitoring). It is strongly suggested that consideration
be given to these higher-order cognitive dispositions and
perspectives so that SDL can truly reflect autonomous
learning. For SDL to have real pragmatic value in an
educational context, the focus must shift to cognitive
responsibility issues and transactional support for critical
reflection. The ability of the teacher to provide control of
the external learning tasks through well-designed learning
packages must be balanced against concerns associated
with modeling and diagnosing the internal cognitive (i.e.,
critically reflective) processes leading to higher-order
outcomes (learning to learn).

10. ABD-EL-FATTAH594
Finally, the analyses of the present study indicated
a relationship between SDL aptitude and academic
achievement. Specifically, self-management was marked
as the strongest predictor of academic achievement over
Semester 1 and Semester 2, followed by self-monitoring.
However, motivation significantly predicted academic
achievement over the second semester only. These findings
are consistent with what Stewart (2007) has recently
reported that self-management was the strongest predictor
of overall learning outcome ratings in project-based
learning in engineering and that self-control and desire for
learning had a positive relationship with overall learning
outcome ratings but their R2
values were relatively low.
These findings can also be explained within the framework
of the self-regulated motivational literature which indicates
that successful learners have more effective and efficient
learning strategies for accessing and using their knowledge,
are self-motivated, and can monitor and change their
strategies to improve their learning outcomes (Corno,
1989; Pintrich & DeGroot, 1990). When learners recognize
their learning needs, formulate learning objectives, select
contents, draw up learning strategies, procure teaching
materials and media, identify additional human and physical
resources and make use of them, and they themselves
organize, control, inspect, and evaluate their own learning,
they are more likely to perform highly on learning tasks.
Table 4
Summary of results of the mediation analyses based on Baron and Kenny technique, Sobel test, and bootstrapping
Technique Statistics
Baron and Kenny
b SE T
Total effect path c
Self-management (IV) > Self-monitoring (DV) .78 .07 11.14**
Path a
Self-management (IV) > Motivation (M) .67 .08 8.38**
Path b
Motivation (M) > Self-monitoring (DV) .74 .07 10.57**
Direct effect path c'
Self-management (IV) > Self-monitoring (DV) .66 .08 8.25**
Partial effect
Gender (COV) > Self-monitoring (DV) .13 .11 1.18
Academic major (COV) > Self-monitoring (DV) .17 .10 1.70
Sobel Test
Path ab
Indirect effects of IV on DV through proposed mediators
Effect SE Z
Total .55 .10 5.5*
Motivation .55 .10 5.5*
Bootstrapping
Path ab
Indirect effects of IV on DV through proposed mediators
Data Boot Bias SE
Total .65 .63 .02 .11
Motivation .65 .63 .02 .11
Bias Corrected and Accelerated Confidence Intervals
Lower Upper
Total .26 .73
Motivation .26 .73
Note. N = 119. ‘Data’ is the indirect effect calculated in the original sample, ‘Boot’ is the mean of the indirect effect estimates calculated
across all bootstrap samples. ‘Bias’ is the difference between “Data” and “Boot”. “SE” is the standard deviation of the bootstrap
estimates of the indirect effect. This standard deviation could be used as a bootstrap-derived estimate of the standard error of the indirect
effect. *p < .05, **p < .01.

11. GARRISON’S MODEL OF SELF-DIRECTED LEARNING 595
Zimmerman, Bonner, and Kovach (1996) suggested that
one of the major advantages of using the self-management
process is that it can improve not only one’s learning, but
it can enhance one’s perception of self-confidence and
control over the learning process. When one practices self-
observing of his or her current learning, one can determine
about the effective learning strategies to use. This process
can help one achieve at higher levels and become a more
self-directed and self-regulated learner.
In conclusion, self-direction is seen as essential if
students are to achieve the ultimate educational goal of
becoming continuous learners. Learning interests and
opportunities for control can promote self-direction and
continued learning. Opportunities for self-directed learning,
in turn, can enhance metacognitive awareness and facilitate
the conditions where students can learn how to learn.
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Received June 2, 2009
Revision received November 11, 2009
Accepted December 18, 2009