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A Meta-Analysis of Self-Regulated Learning in Work-Related Training and Educational Attainment: What We Know and Where We Need to Go


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Researchers have been applying their knowledge of goal-oriented behavior to the self-regulated learning domain for more than 30 years. This review examines the current state of research on self-regulated learning and gaps in the field's understanding of how adults regulate their learning of work-related knowledge and skills. Self-regulation theory was used as a conceptual lens for deriving a heuristic framework of 16 fundamental constructs that constitute self-regulated learning. Meta-analytic findings (k=430, N=90,380) support theoretical propositions that self-regulation constructs are interrelated-30% of the corrected correlations among constructs were .50 or greater. Goal level, persistence, effort, and self-efficacy were the self-regulation constructs with the strongest effects on learning. Together these constructs accounted for 17% of the variance in learning, after controlling for cognitive ability and pretraining knowledge. However, 4 self-regulatory processes-planning, monitoring, help seeking, and emotion control-did not exhibit significant relationships with learning. Thus, a parsimonious framework of the self-regulated learning domain is presented that focuses on a subset of self-regulatory processes that have both limited overlap with other core processes and meaningful effects on learning. Research is needed to advance the field's understanding of how adults regulate their learning in an increasingly complex and knowledge-centric work environment. Such investigations should capture the dynamic nature of self-regulated learning, address the role of self-regulation in informal learning, and investigate how trainees regulate their transfer of training.
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A Meta-Analysis of Self-Regulated Learning in Work-Related Training and
Educational Attainment: What We Know and Where We Need to Go
Traci Sitzmann
University of Colorado Denver Katherine Ely
Fors Marsh Group, Arlington, Virginia
Researchers have been applying their knowledge of goal-oriented behavior to the self-regulated learning
domain for more than 30 years. This review examines the current state of research on self-regulated learning
and gaps in the field’s understanding of how adults regulate their learning of work-related knowledge and
skills. Self-regulation theory was used as a conceptual lens for deriving a heuristic framework of 16
fundamental constructs that constitute self-regulated learning. Meta-analytic findings (k430, N90,380)
support theoretical propositions that self-regulation constructs are interrelated—30% of the corrected corre-
lations among constructs were .50 or greater. Goal level, persistence, effort, and self-efficacy were the
self-regulation constructs with the strongest effects on learning. Together these constructs accounted for 17%
of the variance in learning, after controlling for cognitive ability and pretraining knowledge. However, 4
self-regulatory processes—planning, monitoring, help seeking, and emotion control—did not exhibit signif-
icant relationships with learning. Thus, a parsimonious framework of the self-regulated learning domain is
presented that focuses on a subset of self-regulatory processes that have both limited overlap with other core
processes and meaningful effects on learning. Research is needed to advance the field’s understanding of how
adults regulate their learning in an increasingly complex and knowledge-centric work environment. Such
investigations should capture the dynamic nature of self-regulated learning, address the role of self-regulation
in informal learning, and investigate how trainees regulate their transfer of training.
Keywords: self-regulation, self-regulated learning, training, meta-analysis
Supplemental materials:
Scholars have been examining goal-oriented behavior for more
than 100 years to understand how people regulate their behavior
across a breadth of situations (Austin & Vancouver, 1996). Self-
regulation refers to processes that enable individuals to guide their
goal-directed activities over time, including the modulation of
affect, cognition, and behavior (Karoly, 1993). Self-regulation is
designed to maximize the long-term best interest of an individual,
resulting in people controlling their impulses and looking out for
their well-being (Hayes, 1989; F. H. Kanfer & Karoly, 1972;
Mischel, 1996; Muraven & Baumeister, 2000). This literature base
can be used to understand how people exert control over an
extensive range of behaviors from dieting to religious practices
(McCullough & Willoughby, 2009; Muraven & Baumeister,
2000). Furthermore, self-regulation theory can be used to explain
why both children and adults are willing to exert considerable
mental effort to learn the alphabet, solve a math problem, or
understand Newton’s law of motion. For adults, the ability to
self-regulate may be their most essential asset (Porath & Bateman,
2006). Self-regulation enables people to function effectively in
their personal lives as well as to acquire the knowledge and skills
needed to succeed in higher education and the workforce. Re-
searchers have been applying their understanding of goal-oriented
behavior to self-regulated learning for the past 30 years, and the
field of self-regulation has been instrumental in understanding how
adults regulate their learning of work-related training.
Self-regulated learning refers to the modulation of affective,
cognitive, and behavioral processes throughout a learning experi-
ence to reach a desired level of achievement. This definition
encompasses the core features of most definitions of self-
regulation (e.g., Boekaerts, Maes, & Karoly, 2005; Karoly, 1993;
Pintrich, 2000; Winne, 1995; Zimmerman, 1986): It reflects goal-
oriented behavior and includes multiple processes operating in
concert. However, this definition is distinct because it focuses on
goal striving within a learning context. The self-regulation litera-
ture has played a substantial role in shaping researchers’ under-
standing of the processes though which trainees systematically
adapt their actions during training to achieve their learning goals.
However, after more than 30 years of research, it is time to step
back and examine the state of self-regulated learning research and
identify gaps in the field’s collective understanding of how adults
regulate their learning of work-related knowledge and skills.
Understanding the role of self-regulation in a learning context is
increasingly important as the nature of training evolves. Over time,
work has become progressively more complex and knowledge-
centric, requiring employees to adapt to changing job demands (Bell
This article was published Online First March 14, 2011.
Traci Sitzmann, University of Colorado Denver; Katherine Ely, Fors
Marsh Group, Arlington, Virginia.
We would like to thank Ruth Kanfer for her invaluable insights as we
developed the theoretical framework for this article. Thanks also to Kris-
tina Bauer for her assistance coding the articles included in the meta-
Correspondence concerning this article should be addressed to Traci
Sitzmann, Business School, University of Colorado Denver, PO Box
173364, Denver, CO 80217. E-mail:
Psychological Bulletin © 2011 American Psychological Association
2011, Vol. 137, No. 3, 421–442 0033-2909/11/$12.00 DOI: 10.1037/a0022777
& Kozlowski, 2008). Furthermore, employees are often given control
over which training courses they participate in and over the content,
sequence, and pace of material in the training environment (Kraiger &
Jerden, 2007; Maurer & Tarulli, 1994; Sitzmann, Kraiger, Stewart, &
Wisher, 2006). Informal learning and peer production of training
material (e.g., YouTube, Wikipedia) are also becoming more preva-
lent, increasing the requirement for employees to evaluate what they
need to know and where they can obtain accurate information (Brown
& Sitzmann, 2011). All these changes are escalating the demands
placed on employees and higher education students to self-regulate
their learning. As such, researchers need to stop and examine the
current state of research on self-regulated learning and how this
understanding needs to evolve to accommodate how learning occurs
in the modern work and college environments.
The first objective of the current review was to develop a heuristic
framework of the self-regulated learning domain. The lack of a
comprehensive, yet manageable, list of self-regulation constructs is
one factor that hinders the field’s understanding of how adults regu-
late their learning activities (Vancouver & Day, 2005). Thus, we
examined several of the most frequently cited and influential self-
regulation theories in the training and education literatures to educe a
heuristic framework of the self-regulated learning domain. Our sec-
ond objective was to use the heuristic framework as a foundation for
a meta-analysis examining the interrelationships among the self-
regulation constructs and their effects on learning (k430, N
90,380). The results from the meta-analysis capture the degree of
measurement overlap in self-regulation constructs and provide insight
as to which constructs have the strongest effects on learning. In
addressing these two objectives, this article provides an overview of
the current state of research in the self-regulated learning domain and
identifies gaps in the literature that preclude a comprehensive under-
standing of the domain. We conclude with an examination of how
researchers’ understanding of self-regulated learning must be adapted
to reflect the changing nature of knowledge and skill acquisition in the
modern work environment.
Theoretical Overview and Heuristic Framework of
Self-Regulated Learning
The self-regulated learning domain includes a range of theories
that emerged from different disciplines. Some of the most influ-
ential theories have emerged from cybernetic engineering (control
theory, Carver & Scheier, 1981), clinical psychology (self-efficacy
theory, Bandura, 1977), industrial and organizational psychology
(goal setting, Locke & Latham, 1990, 2002; action regulation,
Frese & Zapf, 1994; Hacker, 1982; resource allocation, R. Kanfer
& Ackerman, 1989), and educational psychology (Pintrich, 2000;
Zimmerman, 1990). Despite their divergent backgrounds, the com-
monalities among these theories are vast, and together they provide
a fairly comprehensive understanding of self-regulated learning.
One commonality across all the theories is that goal setting
triggers self-regulation. Locke and Latham (1984, 1990, 2002) are
renowned in the goal-setting literature for examining the mecha-
nisms through which goals operate and the moderators of the
effects of goal setting on performance. Their meta-analytic results
indicate a positive and linear relationship between goal level and
performance with effect sizes (deffects) ranging from 0.52 to 0.82
(Locke & Latham, 1990). Goals operate by directing attention
toward goal-related activity, which leads to increases in both effort
and persistence and stimulates the discovery and use of task-
relevant knowledge and strategies (Locke & Latham, 2002). Re-
search has shown that goal setting is more effective when the goal
is specific and when individuals are committed to reaching the
goal, possess task knowledge, and receive feedback on their goal
progress. Within a training context, performance goals can have a
deleterious effect on the performance of complex tasks, whereas
learning goals lead to higher levels of performance on such tasks
(Seijts, & Latham, 2001; Winters & Latham, 1996).
Self-efficacy theory evolved from Bandura’s (1977) work in clin-
ical psychology. The theory focuses on the cognitive processes in-
volved in the acquisition and retention of new behaviors. When
trainees judge their self-efficacy for novel tasks, they rely on their past
performance in similar situations (Bandura, 1991, 1997; Wood &
Bandura, 1989). Self-efficacy then exerts a strong, positive effect on
performance through goal setting, effort, and persistence (Bandura,
1977, 1997). Trainees with high self-efficacy engage in positive
discrepancy creation by setting goals that are higher than their previ-
ous performance levels, exerting more effort, and persisting in stress-
ful situations. The relationship between self-efficacy and performance
tends to be stronger when an individual has knowledge of the task to
be performed, when measures of self-efficacy are collected in tem-
poral proximity to performance measures, and when the self-efficacy
items are task specific (Bandura, 1997).
Control theory is based on a machine model derived from
cybernetic engineering (Carver & Scheier, 1981, 1990; Powers,
1978). According to control theory, one source of motivation is a
negative feedback loop that eliminates goal–performance discrep-
ancies (Powers, 1978). Once a goal is reached, individuals turn
their attention toward other goal pursuits. During training, trainees
rely on their self-efficacy to determine how much effort to exert to
reach their goals (Ilies & Judge, 2005; Thomas & Mathieu, 1994).
Higher levels of self-efficacy result in trainees devoting more
resources toward their goal. Moreover, trainees’ rate of goal prog-
ress influences their affect (Carver & Scheier, 1990). Affect is
positive when trainees’ self-evaluation indicates that their progress
is quicker than expected and negative when their progress is
slower than expected.
Action regulation theory originated in Germany (Hacker, 1985)
and was translated to English by Frese and Zapf (1994). The theory
proposes that the psychology of work should be concerned with
actions, which are defined as goal-oriented behaviors. The action
process begins with developing a goal and deciding between
competing goals. Individuals must then decide on their orientation
(i.e., collect information relevant to competing goals and develop
a prognosis of future events), generate plans, make decisions by
selecting a plan from the range of available plans, execute the plan
while monitoring progress, and review feedback on progress to-
ward their goals. External feedback is necessary for learning to
occur. Errors also play an essential role in action regulation theory
because they are a critical component of feedback and influence
There has been extensive debate regarding Bandura’s and Carver and
Scheier’s perspectives on positive discrepancy creation (for more information,
see Bandura & Locke, 2003; Phillips, Hollenbeck, & Ilgen, 1996). Rather than
restate the differences between these theories, the current review focuses on
commonalities across theories to derive a heuristic framework that specifies
the core constructs in the self-regulated learning domain.
the efficiency of action. When trainees are given the opportunity to
make errors in training, it stimulates metacognition as trainees
reflect on the causes of their errors (Ivancic & Hesketh, 2000;
Keith & Frese, 2005).
R. Kanfer and Ackerman’s (1989) resource allocation theory
emerged from the industrial and organizational psychology liter-
ature. They proposed that attentional resources are an undifferen-
tiated pool that must be allocated among competing task demands,
such that trainees divide their attention between on-task activities,
off-task activities, and self-regulation. Proximal and distal moti-
vational processes are used to allocate resources among competing
task demands. Distal motivational processes are antecedents to
task engagement, referring to the choice of whether to pursue a
goal and how much of one’s resources to devote toward goal
attainment. Proximal motivational processes comprise self-
regulatory activity; they determine the distribution of attention
across on-task and off-task activities during task engagement.
Additional theories of self-regulated learning have emerged
from the educational psychology literature, including theories by
Pintrich (2000) and Zimmerman (1990, 2000). These researchers
proposed phase models of self-regulation that focus on a broad
range of self-regulatory processes. In addition to goal setting,
self-efficacy, and the other processes included in the aforemen-
tioned theories, these theories include learning strategies, time
management, and environmental structuring as part of the reper-
toire of strategies that trainees employ to succeed in learning
Zimmerman (1990, 1996, 1998, 2000) proposed a social cogni-
tive model based on the work of Bandura (1986) and elaborated on
the processes enacted in the forethought, performance, and reflec-
tion phases of learning. The forethought phase precedes perfor-
mance and prepares trainees for learning. The performance control
phase occurs during learning and affects trainees’ attention and
action. Finally, self-reflection occurs after performance as trainees
react to their efforts. Zimmerman (2000) acknowledged that learn-
ing conditions are constantly changing, necessitating continuous
observation and monitoring of self-oriented feedback loops.
Pintrich (2000) developed a framework that proposes that self-
regulation occurs in four phases. Phase 1 involves planning, goal
setting, and activation of knowledge and motivational factors
relevant to the task. During Phase 2, trainees monitor various
aspects of themselves, the task, and the context, which they sub-
sequently control and regulate in Phase 3 and react and reflect
upon in Phase 4. During the four phases of self-regulation, trainees
focus on four areas: cognition, motivation, behavior, and the
context. Pintrich then provided a 4 4 grid for classifying
self-regulatory processes based on the phase and the focus area
(e.g., pretraining self-efficacy occurs during Phase 1 and in the
motivation focus area).
Taken together, these theories focus on a wide range of self-
regulation constructs. By examining their commonalities, it is
possible to derive a heuristic framework of the fundamental con-
structs that constitute self-regulated learning. Our review of self-
regulation theories suggests that there are 16 core self-regulated
learning constructs.
Table 1 lists the self-regulated learning con-
structs as well as the theories that indicate the construct is included
in the self-regulation domain. Each of the constructs can be clas-
sified as a regulatory agent, regulatory mechanism, or regulatory
appraisal. Trainees’ goal levels serve as regulatory agents; they
initiate self-regulated learning on a path toward achieving one’s
objectives. Regulatory mechanisms are the processes trainees use
to maximize progress toward their goals in an efficient and orga-
nized manner. Looking across self-regulation theories, we identi-
fied 12 core regulatory mechanisms: planning, monitoring, meta-
cognition, attention, learning strategies, persistence, time
management, environmental structuring, help seeking, motivation,
emotion control, and effort. Regulatory appraisals are instrumental
in evaluating trainees’ progress and determining whether trainees
will either begin or continue striving to make progress toward their
goals. The three regulatory appraisals discussed in self-regulation
theories are self-evaluation, attributions, and self-efficacy. In the
following sections, we provide a broad theoretical overview of
each of the regulatory agents, mechanisms, and appraisals and
discuss whether the current state of the literature provides an avid
understanding of the role of each construct in facilitating learning.
Regulatory Agents
Regulatory agents are instrumental for initiating self-regulated
learning. Goals are regulatory agents, and numerous experiments
have examined their effects on training outcomes. Goals reflect the
standard for successfully accomplishing a task, and self-regulation
theories agree that goals provide a criterion for monitoring, eval-
uating, and guiding self-regulatory activity (Bandura, 1977; Carver
& Scheier, 2000; R. Kanfer & Ackerman, 1989; Locke & Latham,
2002; Pintrich, 2000; Zimmerman, 1986). Goals initiate action
(Frese & Zapf, 1994). They direct trainees’ attention, increase
effort and persistence, and lead to the use of relevant task strategies
(Locke & Latham, 2002).
There is an extensive knowledge base on the effects of goal
setting (see Locke & Latham, 2002, for a review). However, in
research on self-regulated learning, goals are often experimentally
To develop the heuristic framework, we identified the most frequently
cited and influential theories in the adult self-regulated learning domain.
First, we identified 15 self-regulation theories that were included in pre-
vious self-regulation review articles (e.g., Diefendorff & Lord, 2008;
Kanfer, 1990; Puustinen & Pulkkinen, 2001; Vancouver, 2000). From this
list we eliminated content theories, which do not focus on the components
of self-regulation (i.e., Deci & Ryan, 2000; Dweck, 1986; Higgins, 1997).
Then the seven aforementioned theories as well as Boekaerts and Niemi-
virta (2000), Borkowski (1996), Corno (1993), Kuhl (1992), and Winne
and Hadwin (1998) were compared in terms of their number of citations in
Web of Science and Google Scholar. There was a clear cutoff in the
number of hits per theory such that those included in our review received
more than 100 citations in Web of Science and more than 200 citations in
Google Scholar and those not included in the review fell below both of
these criteria. After choosing the theories, each theory was reviewed by
two independent raters to establish which constructs constitute the self-
regulated learning domain. The raters independently developed a list of the
core constructs in each of the theories (interrater agreement was .89) and
then reached a consensus on the construct lists. There is a range of
constructs included in self-regulation theories, and many theories include
constructs that do not have analogous components in other theories (e.g.,
orientation in Frese & Zapf, 1994, and context evaluation in Pintrich,
2000). Thus, each of the constructs included in the heuristic framework
was a component of at least two of the reviewed theories. The next step in
the rating process involved classifying the constructs as regulatory agents,
mechanisms, and appraisals. Interrater agreement was .93, and once again
a consensus was reached regarding all coding discrepancies.
manipulated, as opposed to having trainees report the goals they
are pursuing. Research on goals tends to focus on the effects of
qualitatively different goals and how goal content influences self-
regulatory processes. For example, researchers have manipulated
whether trainees pursue learning or performance goals (e.g., Bar-
ron & Harackiewicz, 2001; Kozlowski & Bell, 2006) and whether
trainees strive for proximal or distal goals (e.g., Bandura & Sc-
hunk, 1981; Kozlowski & Bell, 2006; Kozlowski et al., 2001;
Latham & Seijts, 1999). However, manipulating trainees’ goals in
experimental research does not provide insight into the relation-
ships between goals and other self-regulatory processes. When
goals are assessed, most self-report measures assess the perfor-
mance goal level that trainees are striving to achieve (Vancouver
& Day, 2005). Thus, the construct included in this meta-analytic
review is trainees’ self-set goal level for performance in the
training environment. For example, Vancouver and Kendall (2006)
asked trainees to report what grade they were aiming for on an
upcoming test. Many studies have examined the effects of trainees’
self-set goal level on learning, but limited correlational research
has examined how trainees’ self-set goal level is related to other
self-regulated learning constructs.
Regulatory Mechanisms
Regulatory mechanisms are the crux of self-regulated learning
because they are largely under the control of trainees and have an
instrumental role in determining whether trainees make progress
toward their goals in an efficient and organized manner. Further-
more, the majority of these constructs have been subjected to
extensive empirical investigations.
Planning. When trainees engage in planning activities, they
think through what they need to learn and set task-specific goals
(Pintrich, 2000; Zimmerman, 2000). When facing a novel task,
individuals create plans to determine which strategies they can use
to reach their goals (Locke & Latham, 2002). Carver and Scheier
(2000) acknowledged that people do not plan too far into the future
(Anderson, 1990). Although individuals tend to have a general
idea of how to reach their goals, they often have only a few
specific steps planned out at a time, and the plan evolves as
trainees carry out the task (Frese & Zapf, 1994).
Monitoring. Monitoring refers to paying attention to one’s
performance and understanding of the course material (R. Kanfer
& Ackerman, 1989). Monitoring is a critical component of self-
regulation because it provides awareness of one’s knowledge level,
which then leads to changes in one’s affect, cognition, and behav-
ior (Pintrich, 2000). Accurate monitoring enhances the regulation
of learning because it reveals what trainees already know and
where they need to focus their resources (Dunlosky, Kubat-Silam,
& Hertzog, 2003; Zimmerman, 2000).
Metacognition. As its name implies, metacognition is a
metaconstruct that subsumes various components of self-
regulation. However, theories differ in the range of constructs that
fall in the metacognition domain. R. Kanfer and Ackerman (1989)
used the term extremely broadly, such that metacognition seems to
include all aspects of self-regulation. Pintrich (2000) used the term
to refer to an implicit awareness of various aspects of the self, task,
and context. Zimmerman (2000) proposed that metacognition en-
compasses all aspects of cognitive self-regulation. Action regula-
tion theory suggests that metacognition is an aspect of personality
(Frese & Zapf, 1994).
Attention. Attention refers to the degree to which trainees are
able to maintain their cognitive focus and concentrate during
training (Zimmerman, 2000). According to resource allocation
theory, trainees divide their cognitive resources between on-task,
off-task, and self-regulatory activities (R. Kanfer & Ackerman,
Table 1
A Heuristic Framework of the Self-Regulated Learning Domain
(1977, 1997,
Carver & Scheier
(1981, 1990,
Frese & Zapf (1994);
Hacker (1985)
Kanfer &
Locke & Latham
(1984, 1990,
1996, 1998,
Regulatory agent
Goal level X X X X X X X
Regulatory mechanisms
Planning X X X X X
Monitoring X X X X X
Metacognition X X X X
Attention X X X X
Learning strategies XX
Persistence X X X X X X X
Time management XX
Environmental structuring XX
Help seeking X X X
Motivation X X X X X X X
Emotion control XX
Effort X X X X X X X
Regulatory appraisals
Self-evaluation X X X X
Attributions X X X X
Self-efficacy X X X X X X
Note. An Xdenotes the theory suggests that the construct is a component of self-regulation.
1989). Proximal motivational processes determine the distribution
of resources across on-task and off-task activities, and goals direct
trainees’ attention toward on-task activities (R. Kanfer & Acker-
man, 1989; Locke & Latham, 2002).
Learning strategies. A core cognitive control activity is the
selection and use of learning strategies, including elaborating on
the training material as well as integrating all the components of
the material with each other and with one’s existing knowledge
(Elliot, McGregor, & Gable, 1999; Pintrich, 2000; Pintrich, Smith,
Garcia, & McKeachie, 1991). Learning strategies are useful for
breaking a task into smaller parts and reorganizing the parts
(Zimmerman, 2000). They assist trainees in building knowledge
structures that are meaningful and coherent so that information can
be stored in long-term memory (Winne, 1996; Zimmerman, 2000).
Persistence. Persistence enables trainees to devote effort to
learning and concentrate on the training material, despite boredom
or failure to make progress toward their goals (Elliot et al., 1999).
Persistence is a function of trainees’ outcome expectancy for a
given task (Carver & Scheier, 2000). Goal setting, self-efficacy,
and feedback all have positive effects on persistence (Bandura,
1977; Frese & Zapf, 1994; Locke & Latham, 2002).
Time management. Time management involves making
study schedules and allocating time for study activities (Pintrich,
2000). Trainees monitor their time and effort levels to meet task
deadlines. Procrastination is the opposite of time management and
involves voluntarily delaying an intended course of action, despite
expecting to be worse off for the delay (Steel, 2007). Zimmerman
(2000) suggested that procrastination is a defensive self-reaction.
Trainees procrastinate to avoid future dissatisfaction, but procras-
tination undermines successful adaptation and limits personal
growth (Garcia & Pintrich, 1994; Zimmerman, 2000).
Environmental structuring. Environmental structuring in-
volves choosing a study location that is conducive to learning (i.e.,
quiet and free from distractions; Pintrich, 2000). Monitoring one’s
learning environment for distractions and removing the distrac-
tions to create an environment that is advantageous for studying
are critical components of self-regulated learning (Pintrich, 2000;
Zimmerman, 1998). Environmental structuring is imperative in
online training, as trainees tend to have control over where and
when they review the training material (Lynch & Dembo, 2004).
However, environmental structuring is mentioned only in two of
the theories included in this review, the educational psychology
theories of Pintrich (2000) and Zimmerman (1998).
Help seeking. Help seeking refers to the degree to which
trainees seek assistance when they have difficulty understanding
concepts during training (Pintrich et al., 1991). Good students
know when, why, and to whom they should turn when seeking
help (Pintrich, 2000; Zimmerman & Martinez-Pons, 1986, 1988).
Help seeking plays an essential role in both Pintrich’s (2000) and
Zimmerman’s (1986) theories and has been included in research
on action regulation theory (e.g., Brodbeck, Zapf, Pru¨mper, &
Frese, 1993).
Motivation. Motivation reflects trainees’ willingness to en-
gage in learning and desire to learn the course content (Noe, 1986;
Noe & Schmitt, 1986; Pintrich, Smith, Garcia, & McKeachie,
1993). Trainees’ beliefs about the incentives or value of learning
have a direct effect on learning because trainees show little interest
in activities that they do not value (Schunk & Ertmer, 2000).
Specific, difficult, but attainable goals motivate performance as
long as trainees are committed to achieving the goal (Locke &
Latham, 2002).
Emotion control. Emotion control limits the intrusion of
performance anxiety and other negative emotions during task
performance (R. Kanfer, Ackerman, & Heggestad, 1996). Trainees
can engage in relaxation exercises, self-encouragement, and self-
talk to regulate their emotional states (R. Kanfer et al., 1996;
Pintrich, 2000). Emotion control facilitates performance by keep-
ing off-task concerns from diverting attention away from the
current task (Keith & Frese, 2005; Porath & Bateman, 2006).
Effort. Effort reflects the amount of time that trainees devote
to learning (Fisher & Ford, 1998; Wilhite, 1990; Zimmerman &
Risemberg, 1997). Trainees regulate the amount of effort that they
devote to learning by monitoring their behavior and feedback on
their performance (Pintrich, 2000). When trainees detect a nega-
tive goal–performance discrepancy, they adjust their concentration
or effort to reduce the discrepancy (Carver & Scheier, 2000).
Current state of research on regulatory mechanisms. Ex-
tensive empirical research has examined the associations among
multiple regulatory mechanisms (e.g., DiBattista & Gosse, 2006;
Garcia, McCann, Turner, & Roska, 1998), their interrelationships
with regulatory agents and appraisals (e.g., Orvis, Horn, & Be-
lanich, 2008; Yeo & Neal, 2008), and their effects on learning
(e.g., Nisbet, Tindall, & Arroyo, 2005; Quin˜ones, 1995). However,
limited empirical research has focused on four regulatory mecha-
nisms—planning, monitoring, environmental structuring, and
emotion control—and these regulatory mechanisms have not been
examined in concert with the full range of self-regulation con-
structs. Only five studies have examined the role of environmental
structuring in self-regulated learning (Al-Ansari, 2005; Klomegah,
2007; Kumrow, 2007, who reported correlations from two sam-
ples; Pintrich, 1989; Plant, Ericsson, Hill, & Asberg, 2005). Emo-
tion control scales (Keith & Frese, 2005; Warr & Downing, 2000)
recently appeared in the literature, and only 11 studies have ad-
opted these scales or developed other measures of emotion control
in self-regulation research (e.g., Bourgeois, 2007; Warr, Allan, &
Birdi, 1999).
Both planning and monitoring tend to be measured as part of
metacognition scales (e.g., Ford, Smith, Weissbein, Gully, & Sa-
las, 1998; Motivated Strategies for Learning Questionnaire
[MSLQ], Pintrich et al., 1991), rather than as separate constructs.
These metacognition scales assess a combination of planning and
monitoring (as well as attention for the MSLQ) but do not include
all aspects of self-regulation that are suggested by theory as
belonging to the metacognition construct domain. Thus, there is a
disconnect between theory and measurement that limits the field’s
understanding of the role of metacognition in self-regulated learn-
Regulatory Appraisals
Regulatory appraisals are instrumental in assessing goal prog-
ress as well as determining whether trainees will either begin or
continue striving to make progress toward their goals. A scarcity of
empirical evidence exists regarding the role of two regulatory
appraisal constructs—self-evaluation and attributions—in self-
regulated learning, but extensive research has focused on the third
regulatory appraisal: self-efficacy. It is also important to note that
self-efficacy judgments can occur before trainees undertake a task
as well as during or after task engagement, whereas self-evaluation
and attributions typically occur during or after task engagement.
Self-evaluation. Self-evaluation refers to assessing goal
progress by comparing one’s current level of knowledge or per-
formance with the desired goal state (R. Kanfer & Ackerman,
1989). Self-evaluation has important implications for affective
states. Unfavorable self-evaluations diminish trainees’ self-
efficacy, motivation, and self-satisfaction unless the individuals
believe that they can adapt their self-regulatory processes by using
different strategies, seeking help, or restructuring their environ-
ment (Bandura, 1977; R. Kanfer & Kanfer, 2001; Schunk &
Ertmer, 1999, 2000).
Attributions. People attempt to understand the causes of
outcomes in achievement situations and attribute the outcome to
several causal dimensions, including ability versus effort (Dweck,
1986). Trainees’ attribution analysis is one component that influ-
ences whether they continue to pursue their goals following self-
evaluation (Carver & Scheier, 1982). Trainees react negatively and
are unlikely to try to improve when errors are attributed to internal,
stable factors, such as low ability (Zimmerman, 2000), but effec-
tive self-regulators tend to attribute failure to low effort and poor
use of learning strategies (Pintrich, 2000; Zimmerman & Kitsan-
tas, 1997).
Self-efficacy. Self-efficacy refers to trainees’ beliefs regard-
ing their capability to succeed in training and perform training-
related tasks (Bandura, 1997). Previous performance in similar
situations is a powerful predictor of self-efficacy (Bandura, 1986;
Carver & Scheier, 1990). Compared with less efficacious trainees,
trainees with high self-efficacy set challenging goals, develop
useful task strategies, persist, expend effort, and perform at a high
level (Bandura, 1977; Carver & Scheier, 2000; Locke & Latham,
2002; Pintrich, 2000; Schunk & Ertmer, 2000; Thomas & Mathieu,
1994; Vancouver & Kendall, 2006; Zimmerman, 2000).
Current state of research on regulatory appraisals. Self-
efficacy is one of the most extensively studied constructs in
self-regulation research (Vancouver & Day, 2005) and is the only
regulatory appraisal that has been examined in concert with the
majority of self-regulation constructs. Attributions are included in
the MSLQ (Pintrich et al., 1991). Thus, attributions have been
studied in concert with other MSLQ scales (e.g., learning strategies
and persistence) but have not been widely researched outside
educational psychology. As monitoring is an implied precursor to
self-evaluation, it is difficult to tease apart trainees’ monitoring of
their performance from their evaluation of their performance (Pin-
trich, Wolters, & Baxter, 2000). As such, we are unaware of any
studies that have measured self-evaluation as an independent con-
struct; therefore, self-evaluation could not be included in the
meta-analysis. In the following section, we outline the objectives
of the meta-analytic investigation.
Meta-Analytic Objectives
The first goal of the meta-analysis was to examine the interre-
lations among the self-regulation constructs. As highlighted by the
heuristic framework, theory suggests that self-regulation is a broad
domain and encompasses 16 fundamental constructs. Thus, one
cannot fully understand the self-regulated learning domain without
understanding their associations. Our meta-analytic review exam-
ined the strength of the relations among self-regulation constructs
and identified where there are gaps in research regarding how
these constructs are related.
Second, we examined the effect of self-regulation on learning.
Only 12 of the studies included in the review examined whether
self-regulation during training predicted training transfer, which
we are defining as the maintenance of trained skills after trainees
leave the training environment (Bell & Roberson, 2006; Gardner,
Moorcroft, & Metford, 1989; Gist, Stevens, & Bavetta, 1991;
Myers, 1997; Nietfeld & Schraw, 2002; Poteet, 1996; Ramirez,
2000; Simmering, 1999; Smith, 1996; Towler & Dipboye, 2001;
Warr & Bunce, 1995; Yi & Davis, 2003). Furthermore, these
studies focused on different self-regulation constructs, such that
only three constructs (i.e., metacognition, motivation, and self-
efficacy) have been examined in concert with training transfer in
more than two studies, attention has been examined in concert with
training transfer in one study, and the remaining 12 constructs have
never been examined in concert with training transfer. Thus, we
focused our meta-analytic investigation on posttraining assess-
ments of learning.
Third, one of the advantages of meta-analysis is that it allows for
a comparison of studies that differ in experimental rigor and other
methodological factors (Lipsey, 2003). The current meta-analysis
examined whether five moderators influenced the associations
between self-regulatory processes and learning: study population
(college students or employees), length of the training course,
publication status (published or unpublished), research design (ex-
perimental or correlational), and year of the publication, disserta-
tion, or presentation. This set of analyses permitted an examination
of whether the relations between self-regulation constructs and
learning generalize across courses that differ in length, reports that
differ in the population sampled and experimental rigor, and recent
versus older research reports.
Taken together, the heuristic framework and meta-analysis clar-
ify the constructs that constitute the self-regulated learning do-
main, how these constructs are interrelated, their effects on learn-
ing, and gaps in the field’s understanding of the self-regulated
learning domain. A meta-analysis of the self-regulated learning
domain is also valuable for determining whether the theoretical
definitions of constructs correspond with how these constructs are
measured in the literature. Next, we review discrepancies between
construct definitions and measurement, followed by meta-analytic
methods and results.
One of the challenges in the self-regulated learning domain is
developing reliable and valid measures that tap only the target
construct. Several measures have been validated and employed in
a breadth of studies for the majority of self-regulation constructs.
However, some of the measures do not tap the full range of
learning activities that theoretically fall in the construct domain,
and a few of the scales include items that tap multiple self-
regulation constructs. Researchers’ understanding of a domain is
limited by the quality of the measures employed, so we begin the
results section with a discussion of the construct validity of pop-
ular self-regulated learning measures, including criterion defi-
ciency and contamination regarding some frequently used mea-
Theoretically, metacognition is an umbrella construct that sub-
sumes multiple self-regulatory processes (R. Kanfer & Ackerman,
1989; Pintrich, 2000; Zimmerman, 1990). The metacognition
scales of Ford et al. (1998) and Schmidt and Ford (2003) assess a
combination of planning and monitoring, whereas Pintrich et al.’s
(1991) scale also assesses attention. Thus, we expect to see strong
intercorrelations with these constructs due to common item con-
Several measures of different constructs are also intricately
related. Popular attention scales (e.g., R. Kanfer & Ackerman,
1989; Weinstein, Schulte, & Palmer, 1987) ask trainees whether
they focused their cognitive resources on the training material,
whereas motivation scales (e.g., Noe & Schmitt, 1986; Pintrich et
al., 1991; Yeo & Neal, 2004) assess trainees’ willingness to engage
in learning and desire to learn the training material. Measures of
persistence (e.g., Elliot et al., 1999; Pintrich et al., 1991; Warr &
Downing, 2000) overlap substantially with these scales, except
that they target trainees’ ability to concentrate and remain moti-
vated specifically when they are bored or dissatisfied with the
training material.
Expectancy (Vroom, 1964), motivation to learn (Noe, 1986;
Noe & Schmitt, 1986), and task value (Pintrich & De Groot, 1990;
Pintrich et al., 1993) theories have all influenced the measurement
and naming of motivation scales. However, a recent meta-analysis
demonstrated that the various types of motivation—including task
value and motivation to learn—have similar nomological net-
works (Bauer, Orvis, Ely, & Sitzmann, 2010). Thus, it is appro-
priate to average across these aspects of motivation when exam-
ining the role of motivation in self-regulated learning.
Emotion control is an important construct in R. Kanfer and
Ackerman’s (1989) and Pintrich’s (2000) theoretical paradigms,
but few measures have been developed to assess it. Keith and Frese
(2005) developed an eight-item measure of emotion control, but
several of the items tap attention (e.g., “When difficulties arose, I
was able to focus all of my attention on the task”) and persistence
(e.g., “When difficulties arose, I was able to motivate myself to
continue”) when difficulties arose during training, as well as the
degree to which trainees tried to combat feelings of anxiety and
worry (e.g., “When difficulties arose, I did not allow myself to lose
my composure”). This “contamination” may explain why two
studies have found that Keith and Frese’s measure correlated .77
with attention (Sitzmann, Bauer, & Ely, 2008), .69 with a cognitive
regulation scale, and .39 with an affective regulation scale (Yeo &
Frederiks, in press). Thus, the scale may be capturing cognitive
regulation to a greater extent than affective regulation.
Measures of learning strategies ask trainees whether they used
strategies such as elaboration and deep processing to help them
learn the material (e.g., Pintrich et al., 1991). However, certain
learning strategies may be more or less beneficial depending on
situational factors, such as the nature of the training content and
trainees’ preexisting knowledge of the course topic. Thus, the way
in which learning strategies are measured does not account for the
utility of different strategies across trainees and training contexts.
Finally, the four items in Pintrich et al.’s (1991) attribution scale
assess both whether trainees’ believe that they can learn the course
material—similar to self-efficacy scales—and the reasons why
they are able to understand the material. A sample item is “If I try
hard enough, then I will understand the course material.” Thus, the
most popular attribution scale includes several double-barreled
questions that assess both trainees’ confidence in their ability and
whether their success is determined by factors within or outside
their control.
In short, our review of self-regulation measurement suggests
that there is evidence of criterion overlap in several of the most
popular measures used to assess self-regulation constructs.
searchers’ collective understanding of the self-regulated learning
domain is limited by the quality of the measures employed, and the
meta-analytic findings must be interpreted in light of these validity
issues. However, a meta-analysis of the domain is needed for
diagnosing measurement problems and providing the empirical
support needed to identify where further validation research is
Literature Search and Meta-Analytic Sample
Computer-based literature searches of PsycINFO, ERIC, Pro-
Quest, and Digital Dissertations were used to locate studies in the
training and education literatures. To be included in the initial
review, abstracts had to contain terms relevant to self-regulation or
one of the self-regulation constructs and training or education.
Initial searches resulted in 26,767 possible studies. Next, we man-
ually searched reference lists from meta-analyses in the training
domain (e.g., Colquitt, LePine, & Noe, 2000; Payne, Youngcourt,
& Beaubien, 2007; Sitzmann, Brown, Casper, Ely, & Zimmerman,
2008; Sitzmann, Ely, Brown, & Bauer, 2010). An extensive search
for unpublished studies was also conducted. First, several confer-
ence programs (e.g., the Society for Industrial and Organizational
Psychology) were manually searched. Second, practitioners and
researchers with expertise in training were asked to provide leads
on unpublished work. In all, we contacted 156 individuals.
Studies were included in the meta-analysis if (a) participants
were nondisabled adults ages 18 or older, (b) training facilitated
potentially job-relevant or education-relevant knowledge or skills
(i.e., not coping with physical or mental health challenges), and (c)
relevant between-subjects correlations were reported or could be
calculated given the reported data. The first two criteria support
generalization to adults participating in workplace training or
college education. The vast majority of studies that were not
included in the meta-analysis were eliminated for the following
reasons: participants were children, self-regulatory processes were
discussed in the manuscript but were not measured, or the authors
did not report correlations either among the self-regulatory pro-
cesses or between self-regulation and learning.
The 369 research reports contributing data to the meta-analysis
included 210 published studies, 135 dissertations, and 24 unpub-
It is imperative that a meta-analysis of the self-regulated learning
domain use only scales that assess a single self-regulated learning con-
struct. Thus, we excluded scales that assessed a combination of multiple
constructs, with the exception of metacognition, which by definition is
multidimensional. For example, the Learning and Study Strategies Inven-
tory motivation scale (Weinstein et al., 1987) assesses a combination of
planning (e.g., “I set goals for the grades I want to get in my classes”) and
persistence (e.g., “When work is difficult, I give up or only study the easy
parts”). Although Weinstein et al. (1987) labeled the scale motivation,we
did not include it with the other motivation measures in the meta-analysis
because the scale label does not match the construct assessed by other
motivation scales. This scale, along with many others, was excluded from
the meta-analysis because the only way to clarify this domain is to focus on
clean measures of self-regulated learning constructs.
lished studies. These studies included 430 independent samples
with data gathered from 90,380 trainees. Trainees were university
students in 82% of studies, employees in 16% of studies, and
military personnel in 2% of studies. Across all studies providing
demographic data, the average age of trainees was 23 years, and
43% of participants were male.
Coding and Interrater Agreement
Table 2 presents definitions of the self-regulation constructs and
examples of scales used to assess the constructs. All the constructs
in the heuristic framework were included in the meta-analysis
except for self-evaluation, for which correlational data were not
available. Furthermore, learning was coded based on Kraiger,
Ford, and Salas’s (1993) multidimensional framework and in-
cluded assessments designed to measure whether trainees remem-
bered concepts presented in training or their ability to perform the
skills taught in training. Learning was assessed posttraining with a
written test (e.g., Vancouver & Kendall, 2006) or through partic-
ipation in a posttraining performance-based activity, such as a
simulation (e.g., Yeo & Neal, 2004). Finally, five moderators were
coded: population (college students vs. employees), length of the
course (hours spent in training), publication status (published vs.
unpublished), research design (experimental or quasi-experimental
vs. correlational), and year of the publication, dissertation, or
Two raters independently categorized the self-regulation mea-
sures and recorded the moderators, correlations, sample sizes, and
reliabilities for the self-regulation and learning measures. The
absolute agreement across raters was 99% for categorizing the
study measures and 97% for moderators. Coders then discussed
discrepancies and reached a consensus.
Meta-Analytic Methods
The corrected mean and variance in validity coefficients across
studies were calculated with formulas for a random-effects model
from Hunter and Schmidt (2004). The mean and variance of the
correlations across studies were corrected for sampling error and
unreliability in the predictor and criterion. Artifact distributions of
the reliability coefficients were created for each construct based on
formulas from Hunter and Schmidt. Reliabilities for self-regulation
constructs and learning measures from all coded studies were
included in the distributions. Range restriction estimates were
unavailable, so no attempt was made to correct for this bias.
Prior to finalizing the analyses, a search for outliers was con-
ducted with a modified Huffcutt and Arthur (1995) sample-
adjusted meta-analytic deviancy statistic with the variance of the
mean correlation calculated according to the formula specified by
Beal, Corey, and Dunlap (2002). On the basis of the results of
these analyses and inspection of the studies, no studies warranted
Some of the studies included in the meta-analysis reported
correlations with multiple learning measures (e.g., Kozlowski &
Bell, 2006). However, single studies contributing multiple corre-
lations to a single analysis can result in biased sampling error
estimates. Thus, when multiple learning measures were present in
a sample, the Hunter and Schmidt (2004) formula was used to
calculate a single estimate that took into account the correlations
among the measures. Studies that included multiple independent
samples were coded separately and treated as independent.
There are a variety of techniques for detecting moderators in
meta-analytic research. Steel and Kammeyer-Mueller (2002) dem-
onstrated that weighted least squares regression provides the most
accurate results. Thus, we used weighted least squares regression
to examine the joint effect of the moderators on the self-regulation/
learning relationships. Correlations were weighted by the study
sample sizes, and categorical variables were dummy coded. Pop-
ulation was dummy coded such that college students (coded 1)
were compared to employees (coded 0). Publication status was
dummy coded such that 0 indicates that the document was unpub-
lished and 1 indicates that the document was published. Research
design was dummy coded such that 0 indicates that the design was
correlational and 1 indicates that the design was experimental or
Meta-Analytic Results and Discussion
Relationships Among the Self-Regulatory Processes
Table 3 presents the corrected correlations among the self-
regulatory processes. The corrected correlations ranged from .30
to .83. One of the strongest correlations was between metacogni-
tion and learning strategies (␳⫽.83, k39, N9,529). These
two constructs also had similar patterns of association with other
self-regulatory processes, and self-regulation theories suggest that
they are distinct but intricately related constructs (Pintrich, 2000;
Zimmerman, 2000). For example, Zimmerman (2000) proposed
that metacognition is a broad construct that encompasses all as-
pects of trainees’ cognitive self-regulation. Learning strategies are
one aspect of cognitive self-regulation; they enhance learning by
breaking a task down into its essential components and meaning-
fully reorganizing the parts. Pintrich’s (2000) theory proposes that
metacognitive monitoring of one’s knowledge is closely related to
the use of learning strategies for increasing one’s knowledge
levels. Thus, learning strategies are theoretically one component of
the multidimensional construct of metacognition (Butler & Winne,
1995; Nelson & Narens, 1990; Zimmerman, 1989, 1994). In em-
pirical research, both metacognition and learning strategies are
captured with self-report measures, and meta-analytic evidence
suggests that trainees may not distinguish between these processes.
Thus, researchers should be aware that there is unlikely to be
incremental validity in measuring both metacognition and learning
strategies. Similar results are likely regardless of whether a meta-
cognition or learning strategies scale is employed.
The attention–time management meta-analytic corrected corre-
lation was .78, based on data from 29 effect sizes and 10,143
trainees. However, these constructs exhibited different patterns of
relations with other self-regulatory processes, suggesting that there
may be some (albeit minimal) incremental validity in assessing
both of these constructs in self-regulation research.
Six other correlations were .70 or greater (i.e., monitoring with
persistence, planning with time management, monitoring with help
seeking, metacognition with emotion control, persistence with
time management, and motivation with emotion control), and 35 of
the 116 (30%) correlations on Table 3 were .50 or greater. How-
ever, five of the correlations that were greater than .70 were based
Table 2
Definitions of Self-Regulation Constructs, Representative Scales, and Sample Scale Items
Construct and definition Scale that assesses the construct Sample item
Regulatory agent
Goal level: Standards trainees aim to
achieve during training
Self-set goal level (1 item; Yeo & Neal, 2008) I aim to achieve an error penalty of less
than ___ on the next trial.
Goal level (1 item; Vancouver & Kendall,
What grade are you really aiming for
on the upcoming test?
Regulatory mechanisms
Planning: Thinking through what one
needs to learn, setting task-specific
goals, and deciding which
strategies to employ to achieve the
Planning (7 items; Schraw & Dennison, 1994) I think about what I really need to learn
before I begin a task. I set specific
goals before I begin a task. I think of
several ways to solve a problem and
choose the best one.
Planning (2 items; Young, 2005) I skim through the chapter to see how it
is organized before I read it
thoroughly. I set goals for myself in
order to direct my study activities.
Monitoring: Paying attention to one’s
performance and understanding of
the course material
Monitoring (3 items; Miller, Behrens, Greene,
& Newman, 1993)
As I read the text, I seldom checked my
understanding by trying to solve
practice problems. (Reverse)
Comprehension monitoring (7 items; Schraw
& Dennison, 1994)
I ask myself questions about how well I
am doing while learning.
Comprehension monitoring (5 items; Warr &
Downing, 2000)
I made a special effort to check how
well I understood what was being
Metacognition: Planning and
monitoring goal-directed behavior
and devoting attention toward the
course material
Metacognitive self-regulation (12 items;
Pintrich et al., 1991)
When I study for this class, I set goals
for myself in order to direct my
activities in each study period.
(Planning) I ask myself questions to
make sure I understand the material I
have been studying in this class.
(Monitoring) During class time I
often miss important points because I
am thinking of other things.
(Attention; reverse)
Metacognitive activity (12 items; Ford et al.,
I thought ahead to what I would do
next to improve my performance.
(Planning) I tried to monitor closely
the areas where I needed the most
study and practice. (Monitoring)
Metacognitive activity (15 items; Schmidt &
Ford, 2003)
During this training program, I tried to
think through each topic and decide
what I was supposed to learn from it,
rather than just jumping in without
thinking. (Planning) During this
training program, I asked myself
questions to make sure I understood
the things I have been trying to learn.
Attention: Concentrating and
maintaining one’s mental focus
during training
Concentration (8 items; Weinstein et al., 1987) I concentrate fully when studying.
Off-task attention (2 items; Kanfer &
Ackerman, 1989)
I daydreamed while doing the task.
Learning strategies: Techniques
employed to elaborate on the
training material as well as
integrate all the components of the
material with each other and with
one’s existing knowledge
Deep processing (5 items; Elliot et al., 1999) When a theoretical point or conclusion
is presented in lecture or in the text, I
try to decide if there is good
supporting evidence.
Elaboration (6 items; Pintrich et al., 1991) When I study for this class, I pull
together information from different
sources, such as lectures, readings,
and discussions.
(table continues)
Table 2 (continued)
Construct and definition Scale that assesses the construct Sample item
Information processing (8 items; Weinstein et
al., 1987)
I try to find relationships between what
I am learning and what I already
Persistence: Continuing to allocate
effort and attention toward the
training material, despite boredom
or failure to make progress toward
one’s goals
Persistence (4 items; Elliot et al., 1999) Regardless of whether or not I like the
material, I work my hardest to learn
Effort regulation (4 items; Pintrich et al.,
Even when course materials are dull
and uninteresting, I manage to keep
working until I finish.
Motivation control (5 items; Warr &
Downing, 2000)
Whenever I was feeling bored, I forced
myself to pay attention.
Time management: Making study
schedules and allocating time for
study activities
Time management (8 items; Weinstein et al.,
When I decide to study, I set aside a
specific length of time and stick to it.
Procrastination (3 items; McGregor & Elliot,
I procrastinated in my studying for the
exam. (Reverse)
Environmental structuring: Choosing
a study location that is conducive
to learning (i.e., quiet and free
from distractions)
Study environment (2 items; Plant et al.,
Percentage of time trainees reported
studying at the library versus at
Environmental restructuring (3 items; Gredler
& Garavalia, 2000)
I turn off the TV/radio so I can
concentrate on what I am doing.
Help seeking: Seeking assistance
when one has difficulty
understanding concepts during
Help seeking (4 items; Pintrich et al., 1991) I ask the instructor to clarify concepts I
don’t understand well.
Motivation: Willingness to engage in
learning and desire to learn the
course content
Motivation to learn (8 items; Noe & Schmitt,
I am motivated to learn the skills
emphasized in the training program.
Task value (6 items; Pintrich et al., 1991) It is important for me to learn the
course material in this class.
Expectancy (7 items; Noe & Schmitt, 1986) I am willing to exert considerable effort
in the training program in order to
improve my skills.
Emotion control: Keeping negative
emotions (e.g., anxiety and worry)
at bay while learning
Emotion control (5 items; Warr & Downing,
I told myself not to worry when things
were difficult.
Emotion control (8 items; Keith & Frese,
When difficulties arose, I calmly
considered how I could continue the
Effort: The amount of time that
trainees devote to learning
Effort (1 item; Fisher & Ford, 1998) Total time spent learning.
Time on task (1 item; Brown, 2001) Total time spent in self-paced training.
Study time (1 item; Wilhite, 1990) Trainees’ estimates of weekly study
Regulatory appraisals
Attributions: Trainees’ beliefs about
the causes of outcomes in
achievement situations
Control of learning beliefs (4 items; Pintrich
et al., 1991)
If I try hard enough, then I will
understand the course material.
Self-efficacy: Trainees’ beliefs
regarding their capability to
succeed in training and perform
training-related tasks
Self-efficacy for learning and performance (8
items; Pintrich et al., 1991)
I’m certain I can understand the basic
concepts in this course.
Performance expectations (3 items;
Harackiewicz et al., 2000)
Considering the difficulty of this course
and my skills, I think I will do well
in this class.
Confidence expectancy (2 items; Elliot &
Church, 1997)
I expect to do well in this class.
Note. One sample item is provided when all the scale items are fairly similar and the exemplar is representative of all scale items; several sample items
are provided for multidimensional scales. Self-evaluation was not included on the table because there are no existing measures of this construct.
on a single study, and one of the correlations was based on data
from three studies. Basing a meta-analytic effect size on data from
few studies is less likely to affect estimates of mean corrected
correlations than to affect estimates of the variance of the corre-
lations (Hunter & Schmidt, 1990). This suggests that we can be
confident that the effect sizes reported on Table 3 are close
approximations of the actual population values.
It is interesting to note that all the correlations that are .70 or
greater are between pairs of regulatory mechanisms. It is likely that
trainees are not able to mentally distinguish among all 12 regula-
tory mechanisms in the heuristic framework. Also, it may not be
possible to engage in some of these self-regulatory processes
without influencing other interrelated processes. For example,
trainees who are managing their time during training should nat-
urally focus their attention on the training material. Thus, there
may be only limited incremental validity for measuring both time
management and attention as well as other strongly related con-
structs in self-regulated learning research.
There was also evidence that some of the self-regulation con-
structs were only weakly related. Twenty-two (19%) of the corre-
lations reported on Table 3 were less than .20. However, several of
these correlations were based on a limited number of studies.
When we focused exclusively on meta-analytic correlations cal-
culated from a minimum of three effect sizes, 10 correlations
among the constructs were less than .20, and the majority of the
weak correlations occurred with help seeking, effort, and pretrain-
ing self-efficacy. Effort is the only construct in the heuristic
framework that is often collected with objective rather than self-
report measures. This construct was weakly related to six of the 16
other constructs in Table 3 and only strongly related to two
constructs (i.e., goal level and time management). Relying on
objective assessments of self-regulatory processes may reduce
common method variance and, thus, the intercorrelations among
the measures.
There was great variability in the sample sizes for the relation-
ships between self-regulatory processes. Specifically, the corrected
correlations among 25% (29 out of 116) of the constructs were
based on data from 15 or more studies. Conversely, only five
studies reported correlations with environmental structuring, 11
studies with emotion control, and self-evaluation could not be
included in the meta-analysis due to the lack of correlational
research on this construct. Moreover, 17% (20 out of 116) of the
correlations on Table 3 could not be calculated because of missing
data, and 42% (49 out of 116) of the correlations were based on
only one or two studies. Future research on self-regulation should
examine these underresearched constructs to better understand the
relationships among self-regulatory processes.
The absence of empirical research on the interrelationships
among the full range of self-regulation constructs precluded an
empirical test of the optimal conceptualization of the self-regulated
learning domain. For example, some researchers have focused on
understanding self-regulation by grouping the constructs into dif-
ferent regulatory pathways (e.g., affective, cognitive, and behav-
ioral), whereas others have used phase models of self-regulation,
grouping constructs together based on when they occur during goal
pursuit (Diefendorff & Lord, 2008). Understanding the correct
specification of domains with multiple dimensions is necessary for
demonstrating construct validity and for making correct inferences
from empirical tests (Edwards, 2001; Law, Wong, & Mobley,
1998; LePine, Piccolo, Jackson, Mathieu, & Saul, 2008). As such,
researchers need to examine the pattern of correlations among all
16 self-regulation constructs to inform theory as to which concep-
tualization is a more accurate representation of the domain.
Predicting Learning
Table 4 presents the meta-analytic correlations between each of
the self-regulatory processes and learning.
According to the heu-
ristic framework, regulatory agents (i.e., goal level) had the largest
effect on learning with a moderate to strong effect size. Both
regulatory mechanisms and appraisals had effect sizes ranging
from weak to moderate (0.08 0.28 and 0.18 0.35, respectively).
The self-regulation constructs with the strongest corrected cor-
relations with learning were goal level (␳⫽.44, k24, N
3,565), self-efficacy (␳⫽.35, k160, N25,798), effort (␳⫽
.28, k61, N8,569), and persistence (␳⫽.27, k30,
N6,979). Each of the theories included in this review acknowl-
edges the essential role of these four constructs in the self-
regulated learning domain (the one exception is that self-efficacy
is not mentioned as part of action regulation theory; Frese & Zapf,
1994). Goals initiate action (Frese & Zapf, 1994), whereas high
self-efficacy leads to setting more difficult goals, developing use-
ful task strategies, persisting, and expending effort to reach one’s
goals (Bandura, 1977; Carver & Scheier, 2000; Locke & Latham,
2002; Pintrich, 2000; Schunk & Ertmer, 2000; Thomas & Mathieu,
1994; Zimmerman, 2000). Learning requires considerable time;
thus, trainees who exert substantial effort also learn more from
training (Brown & Sitzmann, 2011). Finally, persistence enables
trainees to continuously devote effort to learning and concentrate
on the training material, despite boredom or dissatisfaction with
their current performance (R. Kanfer et al., 1996).
Meta-analytic true score regression analysis with maximum
likelihood estimates was used to examine the joint effect of these
four self-regulatory processes on learning. In this analysis, we
controlled for cognitive ability—the strongest predictor of learning
(Ree & Earles, 1991)—and pretraining knowledge to account for
potential reciprocal effects between trainees’ knowledge levels and
self-regulatory processes. Together cognitive ability and pretrain-
ing knowledge accounted for 32% of the variance in posttraining
knowledge (␤⫽.24, .43, respectively, p.05). Goal level,
persistence, effort, and self-efficacy accounted for an additional
17% of the variance in learning (␤⫽⫺.29, .37, .28, .07, respec-
tively, p.05; total R
.49; harmonic mean 796). Thus,
collectively the most influential regulatory agent, mechanism, and
appraisal constructs captured 17% of the variance in learning, after
controlling for cognitive ability and pretraining knowledge. This
finding confirms that self-regulatory processes play an indepen-
dent and instrumental role in the learning process. It is also worth
noting that the effect of goals on learning was positive when
examined in concert with cognitive ability, persistence, effort, and
self-efficacy (␤⫽.17). However, setting more challenging goals
resulted in trainees learning less when pretraining knowledge was
Many trainees contributed data to multiple analyses (e.g., both pre-
training and posttraining self-efficacy with learning). Thus, although
90,380 trainees contributed data to the meta-analysis, the total sample in
Table 3 is 175,389.
also included as covariate in the model. Trainees with high pre-
training knowledge are likely to set more challenging goals but
also have less room for improvement.
The self-regulation constructs with the weakest correlations
with learning were help seeking (␳⫽.08, k24, N4,827),
emotion control (␳⫽.08, k9, N13,051), and pretraining
motivation (␳⫽.10, k52, N18,402). In addition, 76% (13
out of 17) of the confidence intervals did not include zero, indi-
cating that the corrected correlations are statistically significant.
Planning, monitoring, help seeking, and emotion control had con-
fidence intervals that included zero, suggesting that they are not
significant predictors of learning, which is inconsistent with the-
ories that support their role in self-regulation (e.g., R. Kanfer &
Ackerman, 1989; Pintrich, 2000). For example, both Pintrich
(2000) and Zimmerman (2000) proposed that planning occurs
along with goal setting during the forethought phase of self-
Table 3
Meta-Analytic Correlations Among the Self-Regulation Constructs
Goal level 1 2 3 4 5 6 7
1. Planning — — —
2. Monitoring 9 (1,171) .66
3. Metacognition 4 (650) .20 3 (618) .68 2 (230) .47
4. Attention 1 (234) .33 2 (279) .66 4 (753) .52
5. Learning strategies 6 (1,350) .45 5 (752) .52 39 (9,529) .83 28 (9,952) .31
6. Persistence 1 (259) .51 1 (234) .69 1 (152) .83 25 (7,168) .69 1 (234) .67 25 (8,204) .58
7. Time management 1 (234) .72 1 (164) .28 29 (10,143) .78 26 (9,603) .24 1 (235) .72
8. Environmental
structuring 1 (103) .20 —————————
9. Help seeking 1 (234) .51 1 (152) .71 19 (4,569) .45 1 (234) .23 17 (4,420) .36 18 (3,837) .24 2 (370) .51
10. Pretraining motivation 1 (26) .05 1 (67) .37 2 (219) .32 8 (1,266) .41 2 (339) .40 3 (614) .42 3 (685) .30 1 (224) .06
11. Motivation 3 (331) .32 2 (480) .30 3 (736) .32 26 (6,940) .55 5 (708) .23 27 (7,708) .54 19 (5,609) .48 7 (1,482) .23
12. Emotion control 2 (812) .12 1 (152) .53 3 (520) .71 1 (234) .12 2 (500) .67 2 (386) .35 1 (234) .09
13. Effort 3 (388) .43 1 (233) .38 2 (270) .33 10 (1,428) .19 7 (1,657) .15 3 (441) .29 1 (234) .28 1 (233) .47
14. Attributions 2 (592) .27 3 (844) .41 3 (662) .33 17 (5,323) .38 1 (279) .59 22 (6,391) .42 16 (5,560) .36 3 (750) .55
15. Pretraining self-efficacy 4 (283) .53 3 (299) .05 2 (195) .24 15 (2,532) .30 5 (853) .11 6 (1,481) .21 2 (533) .37 1 (224) .19
16. Self-efficacy 16 (2,632) .50 8 (1,155) .37 8 (1,499) .27 48 (10,338) .49 9 (2,076) .38 33 (8,940) .49 21 (6,353) .49 8 (1,638) .23
Note. Self-evaluation was not included in the table, as correlations were not available from any of the studies included in the meta-analysis. Dashes (other
than those on the diagonal) indicate that the meta-analytic correlation could not be calculated due to missing data.
Table 4
Meta-Analytic Correlations for Self-Regulation Constructs With Learning
Construct kTotal N
mean r
Var (e)
Var (a)
Variance due to
artifacts (%)
95% CI 80% CrI
Regulatory agent
Goal level 24 3,565 .37 .44 .01 .03 22.75 .33 .56 .22 .66
Regulatory mechanisms
Planning 9 1,022 .11 .15 .01 .01 71.93 .08 .38 .05 .25
Monitoring 12 1,185 .12 .17 .01 .05 26.30 .04 .38 .13 .46
Metacognition 77 12,996 .12 .16 .01 .02 32.89 .11 .21 .02 .34
Attention 39 9,949 .19 .24 .00 .01 43.83 .17 .31 .12 .36
Learning strategies 72 16,613 .12 .16 .00 .01 38.20 .10 .22 .02 .30
Persistence 30 6,979 .20 .27 .00 .03 20.14 .18 .37 .04 .50
Time management 31 8,518 .17 .21 .00 .00 64.95 .13 .30 .14 .29
Environmental structuring 6 779 .14 .20 .01 .00 100.00 .08 .31 .20 .20
Help Seeking 24 4,827 .06 .08 .01 .04 21.60 .04 .21 .16 .33
Pretraining motivation 52 18,402 .08 .10 .00 .01 30.93 .03 .16 .03 .22
Motivation 67 11,612 .15 .18 .01 .03 25.10 .12 .24 .03 .39
Emotion control 9 13,051 .06 .08 .00 .00 40.55 .02 .17 .02 .13
Effort 61 8,569 .22 .28 .01 .04 22.89 .21 .35 .02 .54
Regulatory appraisals
Attributions 35 8,667 .14 .18 .00 .02 26.12 .12 .24 .00 .37
Pretraining self-efficacy 86 22,857 .18 .22 .00 .01 28.95 .17 .27 .06 .37
Self-efficacy 160 25,798 .29 .35 .01 .04 19.65 .31 .38 .10 .59
Note. Self-evaluation was not included in the table as correlations were not available from any of the studies included in the meta-analysis. Var (e)
Var (a) sampling error variance plus variance due to unreliability in the predictor and criterion; CI confidence interval; CrI credibility interval; LL
lower limit; UL upper limit.
regulation and influences subsequent self-regulatory activity and,
thus, learning. However, there may be mediators of the effects of
these self-regulatory processes on learning. For example, the qual-
ity of trainees’ plans and whether they follow through on their
plans may explain the effect of planning on learning. Examining
whether the effects of these nonsignificant self-regulatory pro-
cesses (as well as other processes) on learning are indirect via the
quality of self-regulatory activity is an essential avenue for future
Moderator Results
Next, we examined whether the relationships between self-
regulatory processes and learning were influenced by five potential
moderators: study population (college students or employees),
length of the training course, publication status (published or
unpublished), research design (experimental or correlational), and
year of the publication, dissertation, or presentation. Together the
five moderators accounted for between 4% and 49% of the vari-
ance in the relationships between self-regulatory processes and
learning (see Table 5). However, the impact of four of the mod-
erators—study population, length of course, research design, and
year—were minimal. The population and research design moder-
ator results were never statistically significant. The length of
course analysis was significant only for goal level, such that
trainees’ goal level had a stronger effect on learning in shorter than
longer courses (␤⫽⫺.49, p.05). The year of publication
moderator was significant only for self-efficacy: Self-efficacy had
a stronger effect on learning in recent than older publications (␤⫽
.21, p.05). Thus, the effects of self-regulatory processes on
learning tend to generalize across trainee populations, shorter and
longer courses, experimental and correlational designs, and recent
and older publications. In contrast, there was some evidence of
publication bias in self-regulated learning research. Publication
bias is often referred to as the “file-drawer problem” and occurs
when the probability that a study is published is dependent on the
magnitude, direction, or significance of a study’s results (Begg,
1994). Three of the constructs—pretraining motivation, pretrain-
ing self-efficacy, and posttraining self-efficacy—tended to have
stronger relationships with learning in published than unpublished
research (␤⫽.40, .47, .47, respectively). Thus, there is some
evidence that weaker results are less likely to be published than
stronger results.
Meta-Analytic Conclusions
Self-regulation theories tend to be extremely broad, and together
seven of the most influential theories suggest that there are 16 core
constructs that account for the extent to which trainees learn from
adult work-related training. Moreover, in examining which con-
structs were included in each theory, we found that the number of
theories that discussed a given construct was significantly related
to the strength of the self-regulation/learning relationship (r
.48). That is, the constructs included in more theories are also the
ones that have stronger effects on learning. This provides initial
evidence that by examining communalities across theories, a con-
cise list of core self-regulation constructs might be derived.
Ideally, a heuristic framework of self-regulated learning should
be comprehensive, parsimonious, and internally consistent (Austin
& Vancouver, 1996). However, the meta-analytic results provide
evidence that trainees may not mentally distinguish among all the
processes when regulating their learning activity, as suggested by
the strong intercorrelations among the constructs. Thus, we pro-
pose a parsimonious framework, which focuses on nine self-
regulatory processes (see Figure 1). To develop the framework, we
started by identifying the constructs that had significant effects on
learning. We then combined constructs if they met three criteria:
strong intercorrelations with one another, similar patterns of cor-
relations with the other self-regulation constructs and learning, and
self-regulation theories suggest that the constructs are strongly
Table 3 (continued)
8 9 10 11 12 13 14 15 16
1 (224) .04 4 (848) .05
1 (336) .04 15 (4,459) .25 19 (2,503) .68
3 (549) .54 3 (14,779) .51 1 (372) .76
1 (83) .30 1 (234) .26 3 (362) .22 1 (234) .36 1 (233) .13
1 (336) .37 13 (2,632) .10 18 (5,514) .60 1 (184) .12
1 (224) .21 3 (696) .04 36 (20,594) .53 8 (2,816) .23 2 (14,627) .47 8 (615) .03 2 (164) .09
2 (439) .22 17 (4,934) .11 12 (2,251) .29 45 (11,765) .48 18 (3,428) .10 29 (6,929) .53 54 (7,821) .51
The outer ring of Figure 1 includes goal level and self-efficacy,
which have moderate to strong effects on learning and are not
redundant with other self-regulatory processes. Effort is included
in the center ring of the framework because of its moderate effect
on learning. As noted earlier, metacognition measures routinely
assess a combination of both planning and monitoring.
metacognition and learning strategies are strongly interrelated,
have similar patterns of correlations with other constructs, and are
linked theoretically (Pintrich, 2000; Zimmerman, 2000). Thus, we
propose that metacognition (and its narrower components of plan-
ning and monitoring) should be combined with learning strategies
into a broader construct, which we label metacognitive strategies.
Attention and time management are also strongly related and have
similar relationships with learning, but their pattern of correlations
with other self-regulatory processes differs, so these constructs are
included separately in the center ring of the framework. Although
persistence is among the constructs with the strongest effects on
learning, it also has strong correlations with nine self-regulatory
processes: goal level, planning, monitoring, metacognition, atten-
tion, learning strategies, time management, motivation, and self-
efficacy. Persistence and metacognition have the greatest evidence
of criterion overlap with the other self-regulatory processes, but
persistence does not have a similar pattern of relationships with
any other construct, suggesting that persistence should not be
combined with other constructs. Thus, persistence is excluded
from the parsimonious framework to reduce criterion overlap
across constructs. Finally, the remaining constructs in the frame-
work with weak to moderate effects on learning are environmental
structuring, motivation, and attributions.
Two self-regulated learning constructs—help seeking and emo-
tion control—had nonsignificant effects on learning and were
therefore not included in the parsimonious framework of adult
self-regulated learning. Help seeking may influence learning only
if trainees are able to find the correct answers to their questions.
Thus, future research on help seeking should examine the mech-
anisms that mediate and moderate the effect of seeking help on
learning. Emotion control may be beneficial in learning situations
only if trainees are able to control their emotions without pulling
significant attentional resources away from task engagement while
gaining control over their emotional responses. Finally, given the
strong theoretical link between monitoring and self-evaluation and
the weak effect of monitoring on learning, self-evaluation is not
included in the framework.
This reduced list of nine self-regulatory processes allows for a
more parsimonious explanation of how trainees regulate their
learning activity. Furthermore, it may help to guide self-regulation
research by providing a manageable list of the processes that
explain meaningful components of the learning process. Future
research should elucidate how trainees self-direct their learning
activities outside training environments and how these core pro-
cesses interact over time as adults strive to acquire work-related
knowledge and skills.
Integrating Meta-Analytic Findings
Several previous training meta-analyses have examined predic-
tors of learning. Comparing our results with the results of previous
meta-analyses provides a comprehensive understanding of the
predictors of learning and how self-regulation constructs compare
with other predictors.
Colquitt et al. (2000) used meta-analytic techniques to test a
model of motivation to learn. They found moderate relationships
between pretraining self-efficacy and both declarative knowledge
(␳⫽.30, k16, N2,806) and skill acquisition (␳⫽.32, k
20, N2,745) and between motivation and both declarative
knowledge (␳⫽.27, k11, N1,509) and skill acquisition (␳⫽
.16, k9, N1,615). Although we found slightly weaker
relationships (␳⫽.22, k86, N22,857, for pretraining
self-efficacy and ␳⫽.18, k67, N11,612, for motivation with
learning), our results are based on data from about 4 times as many
trainees, adding confidence in our results.
Compared with goal orientations and trainee reactions, the ma-
jority of self-regulation constructs are stronger predictors of learn-
ing. Payne et al. (2007) examined the effects of goal orientations
on learning and found small meta-analytic relationships (mastery
goal orientation, ␳⫽.12, k43, N8,676; prove perfor-
mance goal orientation, ␳⫽⫺.01, k38, N7,598; avoid
performance goal orientation, ␳⫽⫺.13, k13, N2,856).
Thus, the relationships between the goal orientation dimensions
and learning tend to be weaker than the relationships between
The MSLQ metacognition scale (Pintrich et al., 1991) also includes
items assessing attention. To reduce criterion overlap with attention, meta-
cognition scales should eliminate items that tap attention and focus more
narrowly on planning and monitoring.
Table 5
Weighted Least Squares Regression Results for Moderators of the Relationships Between Self-Regulation Constructs and Learning
level Planning Monitoring Metacognition Attention
strategies Persistence
—— — .21 .29 — .26
Hours spent in training .49
.17 .21 .18 .20 .10 — .43
Publication status
.06 — .17 .21 .33 .17 .24 .04
Research design
— — .10 .13 .11 .01 .10 .49
Year of publication .11 .09 .60 .17 .17 .09 .01 .05
.25 .04 .46 .13 .21 .05 .15 .40
Note. Dashes indicate that the effect of the moderator could not be examined due to limited variability in the moderator variable.
1college students, 0 employees.
1published, 0 unpublished.
1design was experimental or quasi-experimental, 0 design was
self-regulatory processes and learning. However, Payne et al.
noted that learning is a distal outcome of trainees’ goal orientations
and suggested that the effects of goal orientations on learning are
likely mediated by self-regulatory processes, such as goals, learn-
ing strategies, and self-efficacy. Sitzmann, Brown, et al. (2008)
found small meta-analytic relationships between trainee reactions
and both declarative (␳⫽.12, k78, N11,005) and procedural
knowledge (␳⫽.15, k43, N4,688). This pattern suggests
that the majority of self-regulation constructs have stronger rela-
tionships than trainee reactions with learning. Together these find-
ings indicate that self-regulation has a substantial role in predicting
learning and may mediate the effects of trainees’ goal orientations
on learning. Furthermore, self-regulatory processes collectively
account for more variability in learning than the strongest inde-
pendent predictor: cognitive ability (Ree & Earles, 1991).
In addition to previous training meta-analyses, a meta-analysis
was conducted in the performance domain to examine the effect of
regulatory agents. Specifically, Wood, Mento, and Locke (1987)
found evidence of a moderate positive effect of goal level on
performance (d0.58, k72, N7,548). Converting the dto
an rto aid comparison with our findings yields a corrected corre-
lation of .28. Although our study is not directly comparable to
Wood et al. in that we examined the effect of self-set goal levels
on learning (whereas they focused on the effect of assigned goal
levels on performance), our results are generally consistent with
these findings and support the benefits of goals in enhancing
learning. Overall, the current findings and previous research point
out the instrumental role of self-regulation in predicting learning
and suggest that trainees who engage in self-regulatory activity
tend to learn more than those who fail to self-regulate.
Goals are a central construct in all self-regulation theories (e.g.,
Locke & Latham, 2002; Pintrich, 2000; Zimmerman, 2000), yet a
paucity of research has examined the interrelationships between
trainees’ self-set goal levels and the majority of self-regulation
constructs. In most self-regulation studies, trainees’ goals are
implied (e.g., to learn the course material) rather than explicitly
measured. However, trainees may be striving for different goals.
Some may be trying to outperform other trainees, whereas others
may be striving for an A in the course or want to improve their
knowledge of the training material. As demonstrated by Payne et
al. (2007), these goals have different relations with self-regulation
constructs and learning. It is also likely that the interrelationships
among self-regulation constructs differ based on the goals trainees
are pursuing. Thus, future research should explicate the goals
trainees are striving for by measuring trainees’ goals (including
both the level that they are striving for and the content of their
goals) and examine the relationships between goal level and con-
tent with self-regulatory processes.
Study Limitations
As with any research, there are limitations to the current study.
First, although we identified 16 core constructs in the heuristic
framework, gaps in existing primary research resulted in missing
correlations among self-regulatory processes. This limitation can
be addressed only when more primary studies are conducted that
assess these understudied constructs and measure them in concert
with other self-regulatory processes. Second, correlations between
some of the self-regulation constructs are likely inflated by com-
mon method bias, as many of the measures are self-reported and
completed at the end of training. To reduce this bias in the future,
researchers should examine which processes can be assessed with
non-self-report measures. Third, according to self-regulation the-
ory, self-regulation is a dynamic and cyclical process (Carver &
Scheier, 2000; R. Kanfer & Ackerman, 1989; Pintrich, 2000;
Zimmerman, 2000). However, our meta-analytic results cannot
provide evidence of causal or reciprocal relationships. Longitudi-
nal research is needed to further examine the relationships among
the self-regulatory processes and how these relationships change
over time. We return to this point in the next section.
Directions for Future Research
After reviewing theoretical models of self-regulation and con-
ducting a meta-analysis of the self-regulated learning domain, we
believe that the field is on the verge of a paradigm shift in the
topics examined and methods used to collect data. Specifically, we
believe that the future of self-regulation research involves exam-
ining the optimal timing of measurement and using longitudinal
designs to capture the dynamic nature of self-regulated learning.
Our review also suggests that as organizational training shifts
away from instructor-driven classroom learning, theory and re-
search need to adapt to address the role of self-regulation in
informal learning. Finally, we must begin to examine how self-
regulation after trainees leave the training environment influences
training transfer. The following sections review some of the ques-
Table 5 (continued)
motivation Motivation
control Effort Attributions
self-efficacy Self-efficacy
.05 .09 .06 .74 .07 .07 .01 .10
.30 .06 .01 .17 .06 .13 .06
.40 .40
.24 .22 .37 .47
.55 .20 .22 .22 .08 .27 .08 .12 .16
.33 .34 .09 .07 .48 .12 .30 .01 .21
.34 .12 .23 .09 .49 .07 .34 .26
tions that need to be answered in the next generation of self-
regulation research.
Dynamic nature of self-regulated learning. The majority of
research included in the current review assessed self-regulation
constructs pre-, mid-, or posttraining. However, research is needed
to better understand the progression of self-regulation over time
and to determine the right episodic unit of analysis in different
learning situations. For example, in academic learning, where a
typical undergraduate course might include two or three tests
spread across the semester, self-regulatory processes are likely to
look different across the semester. Gersick’s (1988) punctuated
equilibrium model proposes that groups undergo periods of stag-
nation punctuated by concentrated periods of activity. Self-
regulation may also entail long periods of inactivity followed by
spurts of intense activity. For example, college students may go
several weeks without engaging in self-regulated learning only to
fervently begin to regulate as a key milestone approaches—such as
in the weeks (or days) before a test—and then return to a period of
decreased regulation immediately following the test. In contrast,
during organizational training, trainees may be motivated to con-
tinually regulate to ensure that they are learning the knowledge and
skills that are necessary for their jobs. These examples highlight
the need for qualitative research to better understand how self-
regulation plays out over time in different environments. Research
in this area could provide insight as to when self-regulatory pro-
cesses should be measured as well as the optimal timing for
implementing training interventions designed to induce self-
regulation. Furthermore, quantitative research needs to collect
more data at the point in training when changes in self-regulatory
processes are likely to occur, rather than equally space the waves
of data (Singer & Willett, 2003).
Additionally, limited research has examined differences in the
effects of self-regulatory processes at the within- and between-
subjects levels of analysis. Theoretically, self-regulation is a cy-
clical process by which trainees establish training goals, develop
metacognitive strategies, channel their attention toward learning,
and subsequently modify their self-regulatory processes over time
(Carver & Scheier, 2000; R. Kanfer & Ackerman, 1989; Pintrich,
2000; Zimmerman, 2000). Thus, it is a within-person process that
evolves over time. Switching from the between- to within-subjects
level of analysis requires researchers to rethink self-regulated
learning theory, adopt new research methodologies and analytic
techniques, and contemplate how self-regulatory processes evolve
over time and in the context of work and family demands that may
compete for trainees time (Lord, Diefendorff, Schmidt, & Hall,
2010). Some progress has been made in this area. Sitzmann and
Ely (2010) found that trainees’ learning performance had a posi-
tive effect on self-regulatory activity in the subsequent module.
Both Vancouver and Kendall (2006) and Yeo and Neal (2006)
found that self-efficacy was negatively related to performance at
Figure 1. A parsimonious framework of adult self-regulated learning. Metacognitive strategies encompasses
metacognition (including planning and monitoring) and learning strategies.
the within-subject level but positively related to performance at the
between-subjects level.
Moreover, Vancouver and Kendall found
a cyclical relationship between self-efficacy and test performance:
Past performance was a positive predictor of subsequent self-
efficacy magnitude, but self-efficacy was negatively related to
future performance. Furthermore, Sitzmann and Johnson (2011)
found a cyclical relationship between the amount of time that
trainees planned to devote to studying, effort, and learning perfor-
mance. Planned time on task had a positive effect on effort, which
led to higher learning performance. However, performing well
resulted in trainees planning to allocate less time to the subsequent
module, relative to when they performed poorly on the learning
assessment. Future research should continue in this direction,
employing longitudinal designs to examine changes in self-
regulatory processes as well as the dynamic interplay between
learning and self-regulatory processes over time.
Informal learning. All the studies in the meta-analysis fo-
cused on formal learning, that is, a planned and systematic effort
to teach knowledge and skills. However, the majority of learning
in the workplace is informal, via looking up information online,
experimentation (i.e., trial and error), and discussions with col-
leagues (Brown & Sitzmann, 2011). It is likely that self-regulation
has a stronger effect on learning in informal than formal settings.
Although learning opportunities are explicitly defined in formal
settings, in informal settings employees must engage in self-
regulation to identify or create learning opportunities (Enos, Keh-
rhahn, & Bell, 2003). Additionally, the self-regulatory processes
with the strongest effects on learning may differ across these
contexts (Boekaerts & Minnaert, 1999). For example, trainees may
receive less externally generated feedback when engaged in infor-
mal than formal learning where feedback on exams and assign-
ments is built into the curriculum. Thus, in informal learning
environments, monitoring may be the sole source of feedback on
trainees’ knowledge, suggesting that the accuracy of monitoring
has an essential role in determining the effectiveness of informal
learning. Moreover, in informal settings employees must indepen-
dently identify knowledge gaps, determine where they can access
relevant and accurate information, monitor the accuracy of infor-
mation obtained, and control their emotions if relevant information
is difficult to obtain.
Given the prevalence of informal learning in the modern work
environment, researchers should begin to examine the effect of
self-regulation in this context. New measures need to be developed
or existing measures adapted to capture the nuances of informal
learning. For example, measures should capture the psychological
process by which trainees self-assess their training needs and the
types of planning activities that they engage in to locate accurate
and relevant information. Research should also examine the effects
of self-regulation failure in informal learning. What are the nega-
tive effects of trainees acquiring inaccurate information and ap-
plying them on the job? Additionally, are there environmental
factors, such as strong mentor relationships, that may minimize the
effect of self-regulated learning failure on job performance?
Self-regulation of transfer. Only 12 of the studies included
in the review examined the effect of self-regulation during training
on the maintenance of trained skills posttraining. Furthermore,
none of the studies examined the effect of self-regulation after
trainees left the training environment on training transfer. This is
a critical gap in researchers’ understanding of the self-regulation
process, because an implicit assumption underlying training is that
learning will transfer to the work environment (Brown & Sitz-
mann, 2011). However, some researchers have offered dismal
assessments of training transfer, specifically that only 10% of
training transfers (e.g., Baldwin & Ford, 1988). Examining how
trainees regulate their transfer of material from training to the job
may explain essential variance in the transfer process.
Action regulation theory is the only self-regulation theory in our
review that discusses training transfer (Frese & Zapf, 1994;
Hacker, 1982). This theory focuses on job design and changing
employees’ mentality during training to enhance training transfer.
However, we are not aware of any theories that address how
engaging in self-regulation after returning to the job enhances
training transfer. That is, what is the role of self-regulation after
leaving the training environment in determining whether trainees
transfer knowledge and skills learned in training to the job?
We propose that self-regulation of transfer after trainees return
to the job is essential for ensuring meaningful change in work-
related knowledge and skills. Self-regulation of transfer refers to
striving to apply knowledge and skills learned in training to the job
via control over affective, cognitive, and behavioral processes.
Motivational outcomes of training may be key factors for initiating
self-regulation of transfer. During training, motivation and self-
efficacy are essential for initiating a wide range of self-regulatory
activities (Pintrich, 2000; Zimmerman, 2000). This should also
hold true for self-regulation of transfer: Employees will not set
transfer goals if their motivation and self-efficacy are low. Just as
goals catalyze self-regulation in training, when returning to the
work environment, employees must set specific transfer goals and
devise plans for how to achieve them. Specifically, trainees must
develop plans to unlearn their old work routines and replace them
with the routines taught in training (Frese & Zapf, 1994). This
argument suggests that the majority of self-regulation of transfer
processes have analogous self-regulated learning components. Re-
search examining self-regulation of transfer would aid the field’s
understanding of the role of self-regulation in the modern work
environment and help researchers design interventions to increase
the transfer of trained knowledge and skills back to the job.
The past 30 years of research on self-regulated learning have
been extremely fruitful. Self-regulation theories provide a tremen-
dous knowledge base for understanding how adults regulate their
acquisition of new information. They clarify the fundamental
constructs that constitute self-regulated learning, how these con-
structs are interrelated, and how they work in concert to predict
knowledge acquisition. Our review of self-regulation theories
identified 16 core self-regulated learning constructs. These con-
structs can be classified as regulatory agents, mechanisms, and
appraisals based on whether they are instrumental in initiating
self-regulated learning, ensuring goal progress proceeds in an
efficient and organized manner, or determining whether trainees
sustain their goal-striving behavior.
Recent research suggests that performance ambiguity moderates this
relationship, with self-efficacy having a negative effect on performance
when ambiguity is high and a positive effect when ambiguity is low
(Schmidt & DeShon, 2010).
Together the meta-analytic findings and heuristic framework of
the self-regulated learning domain provide insight as to the current
state of the literature. Meta-analytic findings revealed that the
majority of self-regulatory processes have moderate to strong
relationships with one another, suggesting that the processes are
highly interrelated. Additionally, examining the intercorrelations
between self-regulation constructs suggests that there is measure-
ment overlap in the assessment of some constructs (e.g., metacog-
nition and learning strategies). Furthermore, most of the self-
regulatory processes exhibited positive relationships with learning,
goal level, persistence, effort, and self-efficacy having the stron-
gest effects. Together these four constructs accounted for 17% of
the variance in learning after controlling for cognitive ability and
pretraining knowledge. However, counter to self-regulation theory,
several key regulatory mechanisms—planning, monitoring, help
seeking, and emotion control—did not have significant effects on
learning. Thus, we presented a more parsimonious framework of
the self-regulated learning domain, focusing on a subset of self-
regulatory processes that have both limited overlap with other core
processes and meaningful effects on learning.
We are hopeful that self-regulation research will continue to
progress over the next 30 years. To make this goal a reality,
researchers must collectively regulate their efforts toward advanc-
ing a parsimonious theory of self-regulated learning and adjust
their focus to accommodate how learning occurs in the modern
work and higher education environments.
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Received November 4, 2009
Revision received December 13, 2010
Accepted December 20, 2010
... This study was conducted because engineering education is continually evolving, and we must prepare engineering graduates to become lifelong learners so they can continue to adapt to changes throughout their careers. Implementing self-regulated learning (SRL) strategies in engineering courses can inculcate lifelong learning in students (Babakhani 2014;Eilam, Zeidner, and Aharon 2009;Gupta and Sudhesh 2019;Kappe and van der Flier 2012;Peng 2012;Sitzmann and Ely 2011;Wandler and Imbriale 2017;Zimmerman 1990Zimmerman , 2015. The instructor (one of the authors) of the target course in this study implemented various SRL strategies as part of the course. ...
... The idea of self-regulated learning (SRL) started to formalize in the educational research domain shortly before 1980 (Bandura 1977), and since then, many studies have linked SRL to academic achievement and lifelong learning (Babakhani 2014;Eilam, Zeidner, and Aharon 2009;Gupta and Sudhesh 2019;Kappe and van der Flier 2012;Peng 2012;Sitzmann and Ely 2011;Wandler and Imbriale 2017;Zimmerman 1990Zimmerman , 2015. SRL is understanding and controlling one's learning process (Bandura 1977;Panadero 2017;Sitzmann and Ely 2011;Zimmerman 1990). ...
... The idea of self-regulated learning (SRL) started to formalize in the educational research domain shortly before 1980 (Bandura 1977), and since then, many studies have linked SRL to academic achievement and lifelong learning (Babakhani 2014;Eilam, Zeidner, and Aharon 2009;Gupta and Sudhesh 2019;Kappe and van der Flier 2012;Peng 2012;Sitzmann and Ely 2011;Wandler and Imbriale 2017;Zimmerman 1990Zimmerman , 2015. SRL is understanding and controlling one's learning process (Bandura 1977;Panadero 2017;Sitzmann and Ely 2011;Zimmerman 1990). According to a recent review of the literature (Panadero 2017), SRL includes cognitive, metacognitive, behavioral, motivational, and emotional/affective aspects of learning; the review confirmed that higher education students tend "to have better results if the interventions are aiming at […] self-efficacy and goal setting" (Panadero 2017). ...
In Spring 2020, engineering faculty transitioned to emergency remote instruction due to COVID-19. This mixed-methods study was done to understand the correlation between self-regulated learning and how students experienced the emergency transition to remote learning. The participants were from an upper-level engineering course, with 33 students surveyed four times during the semester. Seven students were interviewed after the semester. Findings revealed that during the transition to remote learning, students perceived less change if they had higher degrees of self-regulation. The qualitative analysis also revealed that transparency in learning and teaching also played an important role in students’ perceptions.
... Even though the aim of collaborative tasks in school settings is development of knowledge and skills, while workplace teams often work towards the development of new products or services, the value and importance of these regulatory skills is recognized in both contexts. For over a decade, self-regulatory mechanisms are seen to enable employees to function adequately and efficiently in the workplace and to gain the necessary skills and knowledge to realize their potentials (Gijbels et al., 2010;Sitzmann & Ely, 2011). Workers are required to function under the constant pressure of time and deadlines, and they need to deal with multiple tasks and sometimes conflicting goals at the same time (Lord et al., 2010). ...
... What most conceptualizations of social regulation have in common is that it concerns a process in which different activities alternate in a general time-ordered sequence (Hadwin et al., 2017). First, planning in generally refers to setting goals and determining which strategies to use in order to reach the goal (Sitzmann & Ely, 2011). Such strategies include discussing how to go about solving problems, determining task directions, and translating these directions into a clear plan (Rogat & Linnenbrink-Garcia, 2011). ...
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Teams are nowadays seen as the cornerstones of organizations. Previous research has shown that team reflexivity is positively related to team performance. Traditionally, team reflexivity is conceptualized as a process that occurs during transition moments, ignoring reflexive moments during teams’ action phases. Moreover, most studies used self-reported questionnaires and cross-sectional designs and thus provided limited insights into how team reflexivity unfolds during both the action and transition phases of teams. In this study, we adopt a social regulation perspective to develop an analytical framework to study team reflexivity in the flow of work. The study was conducted in a software development setting and included 50 h of video recordings of different types of team meetings of six professional self-managing teams (a total of 33 team members). Using concepts from social regulation theory as developed in student learning settings as an analytical lens, an analytical framework with four components of social regulation (knowledge co-construction and regulation; regulation activities; focus of regulation, and type of interaction) was developed and applied. Outcomes show that in more than half of their conversations, the teams jointly engaged in regulation-related activities, of which most concerned planning activities and a very low occurrence of evaluation activities. Different patterns of team reflexivity were found in the action and transition phase but zooming in on the interactions also showed high interrelatedness of the different activities. The analytical framework could assist future research to further study the interaction between the different components and how they mutually relate to team performance.
... Numerous researchers have found empirical evidence of SRL as a widespread social phenomenon (e.g., Volet et al., 2009;Järvelä et al., 2013), and previous review studies have consistently shown that SRL is related to higher levels of student achievement (Dignath et al., 2008;Sitzmann and Ely, 2011;Boer et al., 2014). Numerous studies of SSRL have focused on how groups regulate their collaborative work and how this affects their learning experience as a group (Järvelä et al., 2013;Panadero and Järvelä, 2015), and have found that the type of regulation that develops over time is related to the degree of collaborative success. ...
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Education research is increasingly focused on fostering self-regulated learning (SRL) and socially shared regulation of learning (SSRL) among students. However, previous meta-analyses have rarely focused on the specific types of regulated learning scaffolding. Therefore, this meta-analysis examines the effects of different types of regulated learning scaffolding on regulation strategies and academic performance. A total of 46 articles met the inclusion criteria and were included in the final analysis. The findings showed that overall, regulated learning scaffolding had a moderate effect ( g = 0.587). In addition, moderation analyses were performed using a random effects model that focused on four types of scaffolding. The results showed that overall, composite tools had the greatest effect, while the most useful scaffolding for SRL and SSRL were group awareness tools ( g = 0.61) and composite tools ( g = 0.53), respectively. In terms of learning outcomes, composite tools had the greatest effect on regulation strategies, while intelligent pedagogical agents had the greatest effect on academic performance. We also performed a meta-regression analysis to identify the moderators that had the greatest influence on the effects of regulated learning scaffolding. The results showed that grade level, academic subject, and cooperation all had a significant impact. In conclusion, these findings provide evidence for validating the effectiveness of four regulated learning scaffolding and for discovering their function for SSRL, and presented some practical implications of our findings.
... Many studies investigated the significant roles of SRL strategies in enhancing students, graduates, and postgraduates' performance and academic achievement in higher education, as self-learning is a prerequisite for learning in higher education (Sitzmann and Ely 2011). Learners who possess high self-regulated levels are intrinsically motivated, autonomous individuals, proactive in pursuing their own goals for their learning and taking control of the process of their learning (Kizilcec et al., 2017;Cavalcanti et al., 2018;Cicchinelli et al., 2018). ...
... In addition, there is also evidence that cognitive self-regulation can help individuals cope with high demands and have a positive impact on well-being [21]. Cognitive self-regulation includes strategies such as goal setting, planning, monitoring, and self-reflection, which are useful in coping with a large amount of learning material [22] -a major academic stressor in medical school [14]. However, cognitive self-regulation strategies may need to be adapted to suit new requirements in the first year of medical school. ...
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Objective: Previous meta-analytic data have demonstrated the propensity for mental morbidity among medical students (Rotenstein et al. JAMA. 2016;316(21):2214-36). However, there is a lack of research on medical students' varying depression vulnerabilities and predictive factors. The present study aims to gain a better understanding of the development of mental health morbidity and its predictive factors among first-semester medical students. Methods: In November 2020 and January 2021, 184 first-semester students from two medical schools were surveyed regarding depression (PHQ-9), self-efficacy, resilience, and cognitive self-regulation. Using latent profile analysis, we identified distinct depression development profiles. We applied a multinomial logistic regression analysis to determine how self-efficacy, resilience, and cognitive self-regulation and their changes predicted profile membership. Results: Five profiles of depression development were identified: profile 1, no depression (53.8%); profile 2, mild depression (26.1%); profile 3, depression increase I (9.2%); profile 4, depression increase II (9.8%); and profile 5, persistent depression (1.1%). Students with initially high self-efficacy, resilience, and cognitive self-regulation levels were more likely to belong to the no depression profile. A decrease in self-efficacy and cognitive self-regulation was associated with both depression increase profiles (profiles 3 and 4), and a decrease in resilience was found to be a predictor of profile 4. Conclusion: Students who enter medical school have varying states of mental health, and they differ in their vulnerability to developing depressive symptoms. The promotion of resilience, self-efficacy, and cognitive self-regulation strategies may be key in preventing students' depression in the first semester of medical school.
... Metacognition defined as thinking about one's own thought processes; it is a major component of selfregulated learning. Developing a general understanding of self-regulated learning which indicated that students who explore, and establish, or have the use of these skills will be more academically successful than those who do not have or use these skills (Sitzmann& Ely, 2011). ...
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This study aimed at investigating the relationship among metacognition and goal orientations of Yarmouk University students by their gender, academic level and their level of achievement. The sample consisted of (290) students' males and females.To achieve the aim of the study, two scales were used including an Arabic version of metacognitive scaleAl-Jarrah and Obidat (2011); and a scale of Abu-Ghazal, Al-Hamouri, ,and Ajloni, (2013) to measure goal orientations. The results indicated that students had a high level of metacognition thinking and had a moderate level of goal orientations at the total score and at each domain separately. In addition, the results of the study revealed that there was statistically significant positive correlation betweenMastery-Approach and all domains of metacognition.
Issue: Many current educational approaches are intended to cultivate learners' full (learning) potential by fostering self-regulated learning (SRL), as it is expected that those learners with a high degree of SRL learn more effectively than those with a low degree of SRL. However, these attempts to foster SRL are not always successful. Evidence: We considered complexities related to fostering self-regulated learning by use of an analogy. This analogy was based on two (Dutch) children's games: the treasure hunt (children can find a "treasure" by following directions, completing assignments and/or answering questions) and the dropping (pre-teens are dropped in the woods at nighttime with the assignment to find their way back home). We formulated four interrelated philosophical questions. These questions were not formulated with the intention to provide clear-cut answers, but were instead meant to evoke contemplation about the SRL concept. During this contemplation, the implications of definitional issues regarding SRL were discussed by use of the first question: What are the consequences of the difficulties to explicate what is (not) SRL? The second question (How does SRL relate to autonomy?) touched upon the intricate relationship between SRL and autonomy, by discussing the role of social interaction and varying degrees of instruction when fostering SRL. Next, a related topic was addressed by the third question: How much risk are we willing and able to take when fostering SRL? And finally, the importance of and possibilities to assess SRL were discussed by the fourth question (Should SRL be assessed?). Implications: From our contemplations it has become clear that approaches to foster SRL are often insufficiently aligned with the experience and needs of learners. Instead these approaches are commonly defined by contextual factors, such as misconceptions about SRL and lack of leeway for learners. Consequently, we have used principles that apply to both treasure hunts and droppings, to provide guidelines on how to align one's approach to foster SRL with the educational context and experience and needs of learners.
The author proposes a comprehensive process for applying established self-regulated learning models, strategies, scaffolding, and assessments to any online or blended program, course, or experience. The process draws upon the self-regulated learning model proposed by Barry J. Zimmerman, one of the leading self-regulated learning scholars, as well as contributions from a variety of self-regulated learning scholars, pedagogical researchers, and learning scientists. The chapter provides a step-by-step procedure for incorporating self-regulated learning strategies regardless of the reader's familiarity with online and blended learning, instructional design processes and principles, and self-regulated learning strategies and techniques.
Presents an integrative theoretical framework to explain and to predict psychological changes achieved by different modes of treatment. This theory states that psychological procedures, whatever their form, alter the level and strength of self-efficacy. It is hypothesized that expectations of personal efficacy determine whether coping behavior will be initiated, how much effort will be expended, and how long it will be sustained in the face of obstacles and aversive experiences. Persistence in activities that are subjectively threatening but in fact relatively safe produces, through experiences of mastery, further enhancement of self-efficacy and corresponding reductions in defensive behavior. In the proposed model, expectations of personal efficacy are derived from 4 principal sources of information: performance accomplishments, vicarious experience, verbal persuasion, and physiological states. Factors influencing the cognitive processing of efficacy information arise from enactive, vicarious, exhortative, and emotive sources. The differential power of diverse therapeutic procedures is analyzed in terms of the postulated cognitive mechanism of operation. Findings are reported from microanalyses of enactive, vicarious, and emotive modes of treatment that support the hypothesized relationship between perceived self-efficacy and behavioral changes. (21/2 p ref)
In this chapter we provide an overview of the conceptual and methodological issues involved in developing and evaluating measures of metacognition and self-regulated learning. Our goal is to suggest a general framework for thinking about these assessments- a framework that will help generate questions and guide future research and development efforts. Broadly speaking, we see the main issue in assessing metacognition and self-regulated learning as one of construct validity. Of critical importance are the conceptual or theoretical definitions of these constructs and the adequacy of the empirical evidence offered to justify or support interpretations of test scores obtained from instruments designed to measure them. In speaking to this issue of construct validity, we organize our chapter into four main sections. First, we review the various theoretical and conceptual models of metacognition and self-regulated learning and propose three general components of metacognition and selfregulation that will guide our discussion in subsequent sections. Second, we briefly describe a set of criteria proposed by Messick (1989) for investigating construct validity and suggest a set of guiding questions and general issues to consider in evaluating measures of metacognition and self-regulated learning. Third, we discuss in some detail several measures for assessing metacognition and self-regulated learning in light of the empirical evidence available to address issues of the construct validity of these measures. In the fourth and final section, we draw some conclusions about current measures of metacognition and self-regulated learning, suggest some directions for future research, and raise some issues that merit consideration in the development and evaluation of valid measures of metacognition.
This study utilized the goal-efficacy model to examine a) the extent to which index scores of student self-efficacy, self-set goals, assigned goals, and ability (four variables in the model) could predict academic performance of university students; and b) the best predictor of academic performance. The sample comprised 103 undergraduate students in a university in North Carolina who completed self-administered questionnaires voluntarily during the spring semester of 2005. The instrument used was the revised versions of Motivated Strategies for Learning Questionnaire (MSLQ) and Self-Efficacy for Self-Regulated Learning (SESRL) questionnaire. For analysis, zero-order correlation was performed to estimate the association between the independent variables (ability, self-efficacy, self-set goals, and assigned goals) and the dependent variable (course grade). Three other variables--high school GPA, hours worked weekly, and environmental restructuring--were included in the analysis for elaboration. A multiple regression analysis was also performed to determine the predictive power of the independent variables. In both bivariate and multivariate analyses, high school GPA and student self-efficacy were strongly correlated with academic performance. Of the four variables in the model, high school GPA is a better predictor of student academic performance than sell-efficacy.