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A Conceptual Model of Self-Regulation Online
Anthony R. Artino, Uniformed
Abstract
Recently,
several educational psychologists have suggested that students require considerable
motivation and self-regulation to initiate and sustain their learning in online
settings. This article presents a conceptual model of academic self-regulation
in the context of online learning and provides theoretical and empirical
support for the concepts and relationships described in the model. Ultimately, this
article is meant to highlight the importance of self-regulated learning in
online settings and to encourage instructors to consider these processes when
teaching online.
Introduction
Distance
learning is hardly a new phenomenon. For example, in the
As online
distance learning has grown, so too has interest in self-regulated learning (Boekaerts, Pintrich, & Zeidner, 2000). Self-regulated learning (SRL) has been
defined as “an active, constructive process whereby learners set goals for
their learning and then attempt to monitor, regulate, and control their
cognition, motivation, and behavior, guided and constrained by their goals and
the contextual features of the environment” (Pintrich,
2000, p. 453). Self-regulated learners are described as active students who
efficiently control their own learning experiences in many different ways,
including establishing a conducive work environment and using resources
effectively; employing various cognitive and metacognitive learning strategies
to help comprehend information to be learned; regulating their emotions during
academic tasks; and adopting positive motivational beliefs about their
capabilities, the value of learning, and the factors that improve learning (Schunk & Zimmerman, 2008).
In the
last 10 years, several educational psychologists (e.g., Dabbagh
& Kitsantas, 2004; Hartley & Bendixen, 2001; Schunk &
Zimmerman, 1998) have suggested that students require considerable motivation
and self-regulation to stay engaged, guide their cognition, and regulate their
effort in online situations. This suggestion stems from the belief that
learning on the Web tends to be much more autonomous and self-directed (Allen
& Seaman, 2007). The highly independent nature of online learning is
thought to be due, in part, to the lack of structure and guidance that normally
comes from face-to-face, social interactions with an instructor and other students
(Moore & Kearsley, 2005).
Theoretical Framework
Self-regulated
learning refers to “learning that occurs largely from the influence of
students’ self-generated thoughts, feelings, strategies, and behaviors, which
are oriented toward the attainment of goals” (Schunk & Zimmerman, 1998, p.
viii). Also referred to as academic self-regulation, SRL has been studied in
traditional classrooms as a means of understanding how successful students
adapt their thoughts, feelings, and actions to improve learning. In general,
investigations have consistently found that students with adaptive
self-regulatory beliefs, emotions, and behaviors outperform their less-adaptive
counterparts (for a review, see Pintrich, 1999).
Although various
conceptualizations of academic self-regulation exist (for a review, see Boekaerts et al., 2000), several scholars have found social
cognitive models to be particularly useful in analyzing student success in online
contexts (e.g., Artino, 2007b; Hodges, 2005; Militiadou
& Savenye, 2003). Social cognitive models of self-regulation distinguish
themselves from purely cognitive theories in that they focus on the links
between students’ personal perceptions and their use of self-generated learning
strategies (Pintrich, 1999; Zimmerman, 2000). Moreover, social cognitive models
are concerned with explaining how these personal perceptions and associated
behaviors are ultimately influenced by contextual features of the learning
environment (Pintrich, 2000; Zimmerman, 2000).
Conceptual Model
The conceptual
model proposed here uses social cognitive self-regulation as its theoretical foundation.
At the macro level, the model includes four interacting components: (a) contextual
features of the online learning environment, (b) personal perceptions, (c)
personal behaviors, and (d) various academic outcomes that result, in part, from
these personal perceptions and behaviors. The following section provides an
in-depth description of the concepts and processes contained within these four interacting
components, with special emphasis on contextual features of the environment and
personal perceptions. Included in this section is theoretical and empirical
evidence for the concepts and relationships described in the model.
Contextual Features of
the Online Environment
In the classic
model of social cognitive theory, as conceptualized by Bandura (1986),
contextual features of the learning environment are considered one of three determinants
of human behavior. In particular, human functioning purportedly results from
the triadic, dynamic, and reciprocal interaction of personal factors, behaviors,
and the environment (Bandura, 1986). That is, personal factors (beliefs,
expectations, attitudes, and prior knowledge), the social and physical
environment (resources, consequences of actions, other people, and physical
settings), and behaviors (individual actions, choices, and verbal statements)
interact as determinants of one another.
The model
presented here is based on Bandura’s (1986) original
model; however, it differs slightly in that personal factors (beliefs and
emotions) and academic behaviors (use of learning strategies) are embedded
within, and ultimately influenced by, the learning environment. This difference
is meant to highlight the importance of contextual features of the learning
environment (classroom, online, or otherwise) and their ultimate affect—for
better or worse—on the other learner-centered aspects of academic
self-regulation (Boekaerts & Cascallar,
2006). Moreover, consistent with social cognitive theories, the model presented
here assumes that instructional contexts are perceived and evaluated by
students. That is, the same objective environment may be perceived (or
appraised) differently by different students. As such, it is the subjective environment that ultimately
shapes students’ beliefs, emotions, and academic behaviors (Roeser,
Marachi, & Gehlbach,
2002). This is not to say, however, that subjective perceptions of the
environment are fixed. Instead, these perceptions, as well as the objective
environment itself, can also change as students’ thoughts, feelings, and
actions vary over time (Bandura, 1997). Thus, SRL can
be thought of as an interactive phenomenon, the product of a dynamic
interchange between personal, behavioral, and environmental influences (Pajares, 2002).
The
importance of the environment and its influence on personal factors, as
proposed in the present model, is in keeping with social cognitive views of
self-regulation (Pintrich, 2000; Zimmerman, 2000).
For example, in their discussion of an instrument designed to measure various
aspects of SRL, Duncan and McKeachie (2005) argued that
personal components of self-regulation are not static traits of the learner,
but rather that “motivation is dynamic and contextually bound and that learning
strategies can be learned and brought under the control of the student” (p.
117). Stated another way, students’ motivations and emotions change from course
to course (e.g., depending on their interest in the course, self-efficacy for
performing in the course, etc.). Therefore, the extent to which students use adaptive
self-regulatory behaviors may vary as well, depending on the nature of the online
course and how that course relates to them personally (Boekaerts & Cascallar, 2006).
Examples
of how contextual features of the online environment might influence students’ self-regulatory
perceptions and behaviors are not difficult to imagine. For instance, a student
majoring in education and completing an online course in learning theories
might value the course more than another, non-education major. As a result, the
education major might also be more apt to utilized adaptive learning strategies,
such as elaboration (i.e., actively linking new information to prior knowledge)
and metacognition (i.e., planning, goal setting, and comprehension monitoring).
Artino (2008d) found just such an effect in his study of 481 service academy
undergraduates learning about aviation physiology in an online course.
Specifically, students who reported that they were planning to become aviators
upon graduation from the academy also reported higher mean scores on measures
of task value and metacognition than their non-aviator counterparts. Effect
sizes for the differences were moderate (Cohen’s d = 0.60 and 0.56 for task value and metacognition, respectively;
Cohen, 1988).
Features unique
to the online learning environment may also influence personal perceptions and
behaviors. In a descriptive case study of six graduate students in an online
technology course, Whipp and Chiarelli
(2004) found that various components of the online environment (e.g.,
instructor support, peer support, and course design) influenced students’ SRL strategy
use. For instance, students stated that the constant presence of the teacher
and peers in the online discussion forums added incentive for continued
participation in the discussions. Additionally, results revealed some
variations of traditional help-seeking and peer-assistance behaviors that
seemed to result, in part, from the unique affordances of the online context.
For example, several students discussed how they regularly used their peers’
online discussion posts to plan and shape their own work (Whipp
& Chiarelli, 2004).
Consistent
with these empirical findings, the conceptual model presented here highlights contextual
features of the online environment and their relationship to important aspects
of academic self-regulation. Moreover, the association between the environment
and behaviors are thought to be reciprocal. That is, not only does the
environment influence students’ behaviors, but students’ behaviors can actually
influence aspects of the environment (e.g., students can choose to find a quiet
area to study online materials, and they can make use of online tools, such as
electronic grade books and communication tools, to assist with self-regulation;
Dabbagh & Kitsantas, 2005). In short, the importance of the online instructional
context, and its influence on components of self-regulation, is critical to an
understanding of how students learn and perform online (Whipp
& Chiarelli, 2004).
Personal Perceptions:
Motivational Beliefs
Social
cognitive theories of self-regulation (Pintrich, 2000;
Zimmerman, 2000) stress the importance of students’ motivational beliefs in all
aspects of SRL. Knowledge of effective learning strategies is not enough to
promote and sustain learning; students must also be motivated to employ those
strategies when needed (Pintrich & De Groot, 1990). The model proposed here identifies two
specific motivational beliefs that researchers have found to be important in
both traditional and online contexts: students’ self-efficacy for learning (Bandura,
1997) and the extent to which students value learning tasks (i.e., their task
value beliefs; Eccles & Wigfield, 2002).
Self-efficacy beliefs. Bandura (1986) defined
self-efficacy as “people’s judgments of their capabilities to organize and
execute courses of action required to attain designated types of performances”
(p. 391). In general, highly self-regulated students tend to have greater self-efficacy
for learning than those with less-adaptive self-regulatory skills (Schunk, 2005). With this in mind, several investigations
have studied self-efficacy and its relations to other important variables in
online contexts. Overall, results have revealed that when compared to their
counterparts with lower perceived self-efficacy, efficacious students report fewer
negative achievement emotions (Artino & Stephens, 2007), greater use of SRL
learning strategies (Artino & Stephens, 2006, 2008; Joo, Bong, & Choi,
2000), more satisfaction with their learning experience (Artino, 2007a, 2008b;
Lim, 2001), increased likelihood of enrolling in future online courses (i.e., improved
continuing motivation; Artino, 2007a; Lim 2001), and superior learning and performance
(Joo et al., 2000; Wang & Newlin,
2002). Taken together, these empirical findings support the theoretical links
between students’ self-efficacy beliefs and their achievement emotions, SRL behaviors,
and academic outcomes (Bandura, 1997; Pekrun, 2006), as suggested in the conceptual model.
Task value beliefs. Eccles and Wigfield
(2002) defined task value as the extent to which students find a task
interesting, important, and/or useful. Like perceived self-efficacy, task value
beliefs are hypothesized to positively impact students’ learning and performance.
Moreover, Schunk (2005) concluded that “students with greater personal interest
in a topic and those who view the activity as important or useful are more
likely to use adaptive self-regulatory strategies” (p. 87). Over the past
decade, a few researchers have used task value as a predictor of adaptive
outcomes in online settings. In fact, findings from several studies have shown
task value to be negatively related to students’ negative achievement emotions
(Artino & Stephens, 2007), and positively related to their use of cognitive
and metacognitive learning strategies (Artino & Stephens, 2006, 2008),
overall satisfaction (Artino, 2008b; Miltiadou & Savenye, 2003), and continuing motivation (Artino, 2007a).
Overall, these findings support the theoretical relations between students’
task value beliefs and their achievement emotions, self-regulatory behaviors,
and academic outcomes (Eccles & Wigfield, 2002),
as presented in the conceptual model.
It is
worth noting that the links between students’ motivational beliefs (i.e.,
self-efficacy and task value), achievement emotions, and academic behaviors and
outcomes are thought to be reciprocal (Bandura, 1997; Pekrun, 2006; Zimmerman,
2000). For example, several studies (Artino & Stephens, 2006, 2008; Joo et al., 2000) have found students’ self-efficacy to be
related to adaptive academic behaviors, such as students’ use of cognitive and
metacognitive strategies during online learning. In turn, by using adaptive learning
strategies that result in “deeper and more elaborated processing of the information”
(Schunk, Pintrich, & Meece, 2008, p. 226), students
are likely to experience greater academic success in the form of improved
learning and better grades. These behaviors and the positive outcomes that
result then feed back into the system, conveying to students that they are “capable
of learning and performing well, which enhances their self-efficacy for further
learning (Schunk et al., 2008, p. 127).
Personal Perceptions: Achievement
Emotions
In recent
years, motivation researchers (e.g., Linnenbrink
& Pintrich, 2004; Pekrun,
Goetz, Titz, & Perry, 2002) have acknowledged the
importance of discrete academic emotions and their influence on cognitive
engagement and learning. For instance, Pekrun (2006) has developed a control-value
theory of achievement emotions that outlines hypothesized linkages between
students’ motivational beliefs, their emotions, and, ultimately, their learning
and performance. According to Pekrun’s theory, positive achievement emotions,
such as enjoyment and hope, and negative emotions, such as boredom and
frustration, are determined, in part, by students’ motivational beliefs, also
known as their cognitive appraisals. Furthermore, the effects of emotions on learning
and performance are thought to be mediated, in part, by several cognitive and
motivational mechanisms, such as students’ use of learning strategies, effort
allocation, and persistence (Pekrun et al., 2002).
Among the
many categories of motivational beliefs that are thought to be important
antecedents of achievement emotions, Pekrun (2006)
has suggested that two appraisals are critical in achievement contexts: the
perceived controllability of achievement activities, as indicated by competence
perceptions (e.g., self-efficacy), and the subjective value of those activities
(e.g., task value). Moreover, the relationship between motivational beliefs and
emotions are thought to be bidirectional. That is, “control and value
appraisals are posited to be antecedents of emotions, but emotions can reciprocally
affect these appraisals” (Pekrun, 2006, p. 327). For example, not only does
self-efficacy for learning impact achievement emotions, but negative feelings,
such as test anxiety, can also influence future self-efficacy beliefs. In fact,
according to Bandura (1997), information conveyed by
emotions is cognitively assessed by an individual and can positively or
negatively influence self-efficacy beliefs, depending on the level of arousal
and the person’s cognitive appraisal.
Using control-value
theory as a framework, a small number of studies with university students in
traditional classrooms have found that achievement emotions were related, as
predicted, to students’ use of learning strategies and various measures of academic
performance (Pekrun et al., 2002). For instance, negative achievement emotions
(e.g., boredom and anger) were negatively related to motivational variables
(e.g., interest and effort) and measures of learning strategies use (e.g.,
elaboration and metacognition); whereas positive emotions (e.g., enjoyment,
hope, and pride) were positively related to these same outcomes.
Findings
in online settings, although limited, are similar to the results described
above. For example, in a study of two samples of service academy undergraduates
(total N = 783), Artino (2008a) found
that online learners’ emotions were related to several adaptive outcomes. In
particular, findings from several multiple regressions revealed that students’
boredom and frustration were statistically significant predictors of
metacognition, with boredom emerging as a negative predictor and frustration
unexpectedly emerging as a positive predictor. On the other hand, enjoyment
emerged as a positive predictor of both elaboration and metacognition.
Although
inconsistent with the empirical work of Pekrun (e.g.,
Pekrun et al., 2002), the finding that frustration was
positively related to metacognition corroborates the theoretical suggestion
that certain negative emotions “may well facilitate the use of specific kinds
of learning strategies, even if such effects do not appear in more consistent
ways when self-report measures of learning strategies are used” (Pekrun et al., 2002, p. 99). If nothing else, this novel
finding is further evidence of the multifaceted, dynamic interplay between
cognition, affect, and behavior, as described by Linnenbrink
and Pintrich (2004). Nonetheless, the results reported
by Artino (2008a) generally support the tenets of control-value
theory (Pekrun, 2006), indicating that students’
achievement emotions are related, in important ways, to their use of SRL strategies
and their overall online success.
Conclusions
The
purpose of this article was to present a conceptual model of social cognitive
self-regulation as it relates to students learning in online contexts. To
achieve this objective, the article included a narrative description of academic
self-regulation, as well as theoretical and empirical support for the concepts
and relationships described in the model.
Altogether,
results from studies of online learners largely support the assumptions of social
cognitive theory and findings from research with traditional, classroom
students. In particular, it seems that adaptive self-regulatory beliefs,
emotions, and behaviors are important, if not essential, for effective learning
and performance in online settings (Artino, 2007b; Hodges, 2005; Miltiadou & Savenye, 2003).
Using the
conceptual model presented in this article, online teachers are encouraged to consider
and explicitly address their students’ academic motivation, achievement
emotions, and self-regulation as they strive to provide effective and engaging
online instruction. For detailed accounts of specific instructional techniques intended
to support motivation, emotion, and self-regulation in both traditional and online
classrooms, interested readers are directed to consult Ley
and Young (2001) and Artino (2008c).
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