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Challenges in supporting self-regulation in distance
education environments
Linda Bol •Joanna K. Garner
Published online: 12 April 2011
Springer Science+Business Media, LLC 2011
Abstract This article considers the application of selected components of self-
regulated learning (SRL; Zimmerman 2000) to student-content interaction in online
learning and distance education (DE). In particular we discuss how, when inter-
acting with electronically enhanced text, students must carefully employ self-reg-
ulated learning strategies that include planning, goal setting, self-monitoring
processes, and calibration judgments. Because the student is often learning inde-
pendently in DE courses, and because of the potential for non-linear navigation
through online learning materials, we argue that the careful deployment of SRL
skills is especially critical for successful outcomes. Consequently we discuss
examples of how the demands of student-content interactions put students with self-
regulation difficulties at risk of failure. We highlight research on learners who have
poor SRL skills, inadequate calibration capabilities, and low executive functions in
order to highlight areas of particular difficulty and areas in which support might be
most beneficial. We conclude with the recognition that while support strategies can
be derived from the research literature, there is a great need for research that
addresses questions about student-content interaction in DE course settings spe-
cifically, and pertains to the increasingly diverse group of learners who take these
courses.
Keywords Self-regulated learning Calibration Executive functioning
Instructional design Distance education
L. Bol (&)
Educational Foundations and Leadership, Darden College of Education,
Old Dominion University, Norfolk, VA 23508, USA
e-mail: lbol@odu.edu
J. K. Garner
The Center for Educational Partnerships, Darden College of Education,
Old Dominion University, Norfolk, VA 23508, USA
123
J Comput High Educ (2011) 23:104–123
DOI 10.1007/s12528-011-9046-7
Introduction
Students are faced with numerous, complex demands when engaged in distance
education and on-line learning. Chief among them is the need to be self-directed and
regulate one’s own learning in pursuit of academically relevant goals. Setting goals,
monitoring progress towards these goals, and reflecting on outcomes are hallmarks of
effective self-regulation, and these skills have been clearly linked to achievement
(e.g., Boekaerts et al. 2000). Some have argued that the demands in distance
education settings are greater than those faced by students enrolled in traditional
face-to-face courses due to the largely autonomous nature of on-line learning and the
lack of on-going, interactive support or scaffolding that a physically present
instructor typically provides (Azevedo et al. 2008; Dabbagh and Kitsantas 2004).
Therefore, the ability to effectively employ self-regulation skills may be even more
critical in distance education environments than in traditional classrooms. In essence,
distance education entails multiple episodes of on-line learning and multiple
opportunities to engage in self-regulated learning. However, many students find it
difficult to manage their learning in distance learning environments (Artino and
Stephens 2009). To compound this difficulty, instructors may also lack knowledge of
or may inadvertently minimize the importance of self-regulated learning skill (Zohar
1999) in academic achievement. Therefore, they may not design or deliver
instruction that supports and promotes self-regulation skills in their students.
Despite its import, scant attention has been focused on the role of self-regulated
learning in the design, delivery, and evaluation of the effectiveness of instruction in
distance education environments (Abrami et al. 2011). Abrami and his colleagues
call for the application of evidence-based approaches, including theories of self-
regulation, to guide improvements in the next generation of distance and on-line
learning. They contend, ‘‘It is possible to create instructional designs with many of
the features of self-regulation and to embed these designs as templates into existing
tools for distance and on-line learning’’ (p. 14). The impetus for the current article
was to respond to their call by illuminating how self-regulated learning theory and
research may inform and improve instructional practices in distance education
environments.
Before embarking on this ambitious undertaking, we recognized that discussing
every aspect of self regulated learning as it applies to all possible educational tasks
or interactions is beyond the scope of this paper. Therefore we narrowed our scope
in the following ways. First, we selected student-content interaction rather than
other type of interpersonal interactions described in the literature because SRL
research can be readily applied to this type of interaction. Second, we concentrated
on learning from multimedia environments given our focus on distance education
and the increasingly common use of instructional materials which have multimedia
and electronically enhanced features. Third, we emphasized Zimmerman’s Cyclical
Model of Self-regulation (2000), though we acknowledge there are other sound
models of self-regulated learning that share common characteristics (e.g., Pintrich
2000; Winne and Hadwin 1998), because its socio-cognitive nature presents both a
meaningful framework and specific challenges to instructional designers wishing to
promote SRL skills in students. Finally, we incorporated examples from the
Self-regulation and distance education 105
123
calibration and executive monitoring literatures to illustrate dysfunctional processes
that may impact the effectiveness of instructional design and student learning
processes.
Types of interactions
Understanding the types of interactions that occur in educational environments is a
useful heuristic for guiding practice as well as research. Anderson (2003)
distinguished among the three most common types of interactions addressed in
the distance education literature. The first two are interpersonal in nature and
include student–teacher and student–student interactions. Examples of student–
teacher interaction are receiving and responding to teacher feedback and help-
seeking in the form of asking for clarification or direction. Student to student
interactions may entail peer review of work, negotiating roles on collaborative tasks,
or responding to discussion threads posted by other students. The third type of
interaction is intrapersonal. Student-content interaction involves ‘‘reading instruc-
tional texts for meaning, using study guides, watching instructional videos,
interacting with multimedia, participating in simulations, or using cognitive
software’’ (Abrami et al. 2011).
Although the relative importance of the three types of interactions in promoting
learning has been hotly debated in the literature, Anderson (2003) argues that some
students deliberately select learning environments that minimize the extent of
interpersonal interactions. The preference of some students to work independently
may lead them to choose distance education options compounding the critical need
for effective self-regulation skills in student-content interactions. In the following
section we examine student-content interaction from the perspective of Zimmer-
man’s model: interacting with electronically enhanced instructional text, which we
take to include informational materials in which multimedia or hypermedia features
are embedded.
Zimmerman’s SRL model applied to student-content interaction
As noted Zimmerman’s cyclical model of self-regulation (see Fig. 1) shares
common features with other theoretical models of self-regulated learning found in
the literature and supported by empirical findings (e.g., Pintrich 2000; Winne and
Hadwin 1998). First and foremost is the dynamic nature of self-regulation where
learners cycle through the various phases of learning. It is further assumed that
individuals actively manage their learning by monitoring their progress and
selecting appropriate cognitive strategies needed to accomplish academic goals. In
other words, learning is inherently purposeful. Finally, motivation or self-beliefs
influenced by social factors play an important role in each of these models. A brief
overview of Zimmerman’s model is presented followed by a more detailed
discussion of how it is applied to student-content interactions.
106 L. Bol, J. K. Garner
123
In Zimmerman’s (2000) model, self-regulation cycles through three distinct
phases. The forethought phase encompasses task analysis that consists of setting
goals for a learning episode as well strategically planning the deployment of
personal and motoric strategies required for achieving these goals. Continuous
adjustment of goal setting and task analysis occurs as learners progress or
alternatively, fail to progress, when prerequisite skills are needed or context and
demands vary. However, goal setting and task analysis depend on motivational
beliefs. ‘‘Self-regulatory skills are of little value if a person cannot motivate
themselves to use them’’ (p. 16). The self-motivational components of forethought
include beliefs about self-efficacy, outcome expectations, intrinsic interest or value,
and goal orientation. The second phase is performance or volitional control and is
categorized into self-control and self-observation processes. Self control optimizes a
learner’s effort and involves the processes of self-instruction, imagery, attention
focusing, and task strategies. Self-observation processes consist of both self-
recording and self-experimentation. Learners monitor different aspects of their
performance in conjunction with the conditions influencing it and its outcomes. This
may lead to self-experimentation when attempting to diagnose the success or failure
of their efforts and what conditions may lead to improvement. The third and final
phase in this cyclical model is self-reflection that occurs at the conclusion of a
learning episode. A natural consequence of self-observation is self-judgment and
self-reaction. Learners evaluate their performance which can trigger causal
attributions that explain performance. These explanations can be adaptive (e.g.,
Performance or Volitional
Control
Processes that occur during
motoric efforts and affect
attention and action
Forethought
Influential processes that
precede efforts to act and
set the stage for action
Self-Reflection
Processes that occur
after performance efforts
and influence a person’s
response to that
experience
Fig. 1 Zimmerman’s (2000) cyclical model of self-regulation
Self-regulation and distance education 107
123
poor performance due to lack of effort) or maladaptive (e.g., poor performance due
to lack of ability) affecting the likelihood of persistence or continued effort and
ultimately successful mastery of a learning task. Self-reactions can incur positive
self-satisfaction leading to adaptive responses (e.g., selecting more effective
cognitive strategies) or more defensive self-evaluations leading to self-handicapping
behaviors (e.g., procrastination, task avoidance, and cognitive disengagement).
Zimmerman’s social cognitive model relies on environmental, social, and self-
influences that have clear implications for instructional design. In addition, by
situating our discussion within this model we recognize several important ideas.
First, the model reinforces that all students possess self-regulatory capacities, but that
the quantity and quality of these capacities, the degree of success with which these
capacities can be executed, and the adaptive or maladaptive nature of the goals that
they serve, differs between students. Underlying this idea is the assumption that self-
regulated learning capacities are well-characterized as a skill set that is subject to
individual, contextual and subject matter-related influences on its execution. From an
instructional design perspective this means that SRL skills can be moderated through
the student’s experience of instruction. The challenge for instructional designers of
distance education materials in particular is to support self-regulated learning
through means that are not dependent on synchronous interaction between the
teacher and student, or among students. Within the social cognitive framework, SRL
skills are learned through progressive internalization following the observation of
expert models. Thus, windows of opportunity exist for presenting, scaffolding,
supporting, and also releasing responsibility for SRL processes. Once again,
although synchronous interactions are becoming more prevalent, the predominantly
asynchronous nature of learning in DE settings means that the provision of such
scaffolding and support is challenging. This is especially true because although in an
ideal situation students would engage in proactive regulation and would seek goals
which promote knowledge and skill development, in many situations students self-
regulate in a reactive manner and implement strategies only in the face of feedback
which informs them that their performance is deficient. Thus, just as it is important
for distance education research to acknowledge and explore social-cognitive
mechanisms for the acquisition and improvement of SRL skills, it is also important
that DE students have the tools they need in order to accurately monitor and calibrate
their level of performance. In particular, as we discuss, planning and self-monitoring
appear to be critical mediating factors when interacting with material in computer
based learning environments. Exercises in the consideration of support for these
processes are therefore not inconsequential.
In the sections that follow, we first emphasize how executing metacognitive
processes during these activities represents phases of self-regulated learning and
that students must effectively regulate their own learning in order to profitably
interact with the material. Second, we identify areas in which instructional design
features are, or could be, utilized to support learners during learning episodes. To do
this we draw from research which has tried to take into account the effect of
individual difference variables in learners’ SRL skills and areas of weakness, as
well as features of the task itself. We highlight the role of self-monitoring through
the concept of calibration and the constructs of executive functions, and conclude
108 L. Bol, J. K. Garner
123
with a call for research into the effectiveness of support mechanisms for diverse
groups of learners.
Learning from electronically enhanced text
Students in DE courses are often required to read and learn from a substantial
amount of informational text. This text can range from viewing a PDF file on a
screen to interacting with hypermedia text in which links to further content are
embedded, or viewing a combination of informational text and graphics, anima-
tions, or movie files. We use the term electronically enhanced text to mean screen-
presented text which may or may not include a combination of hyperlinks and
embedded graphics, animations and other digital multimedia objects. The potential
for learning that these media offer is great. But the critical role of self-regulated
learning techniques when learning from electronically enhanced text has also been
recognized. According to Greene and Azevedo (2009) ‘‘hypermedia environments,
with their flexibility in presenting multiple representations, have been suggested as
ideal learning tools for fostering sophisticated mental models of complex systems.’’
However, they go on to say that
Students must possess effective SRL skills to benefit from such environments
because navigating and learning from multiple representations requires large
amounts of learner control…students who do not regulate their learning often
become overwhelmed by the many options presented to them in hypermedia
environments, thus making the presence of relevant information in multiple
representations a hindrance rather than an advantage (p. 19).
Theoretical accounts of self-regulated learning predate the research that
specifically deals with learning from electronically enhanced text. Many of these
studies investigated SRL in the context of learning from various forms of
informational text presented in paper form. Through these studies, researchers
identified particular self-regulated learning strategies that were important factors in
predicting learning outcomes (Pressley 2000; Pressley and Ghatala 1990). For
example, when interacting with informational text, skilled readers were found to be
more likely to engage in metacognitive strategies such as goal setting (Pearson and
Fielding 1991; Pressley 2000). A purpose for and desired outcome of reading allows
the reader to engage in more effective self-monitoring. Skilled readers also consider
what they already know about a topic prior to reading. During reading they monitor
their progress, and activate strategies designed to promote comprehension of the
information in the text as well as integration of information with background
knowledge. At the conclusion of a learning episode, skilled readers may reflect on
the effectiveness of their learning as well as the effectiveness of the strategies and
procedures used. In short, effective learning from informational text involves goal
setting, prior knowledge activation, comprehension monitoring, task- and subject-
specific strategy selection, and reflection on both outcome and process.
With the exception of certain task and medium features which incur unique
learning-related behaviors, such as the hyperlink functionality that introduces
Self-regulation and distance education 109
123
decisions about clicking to view additional content, research pertaining to learning
from traditional forms of informational text and converges with research on learning
from electronically enhanced material, in the sense that similar SRL behaviors
characterize successful students in both settings. The work of Azevedo and
colleagues (e.g. Azevedo et al. 2004) has revealed that students with effective self-
regulated learning skills are more likely to experience gains in the content and
complexity of their knowledge structures than students with poor self-regulated
learning skills. Importantly, these gains are not simply due to differences in prior
knowledge or the use of micro-level strategies such as note-taking or re-reading
areas of the material (Greene et al. 2010). Rather, gains are in large part due to
differences in the macro-level metacognitive processes such as planning and self-
monitoring that characterize the first two phases of Zimmerman’s SRL model.
Specifically, according to Azevedo et al. (2004), the most effective students spend a
higher proportion of their time engaging in tasks relating to planning, self-
monitoring, and cognitive strategy use. They create sub-goals during the learning
task, retrieve relevant prior knowledge, and monitor their progress towards goals.
They are effective in planning their time, which is a critical factor when learning
episodes are of a fixed length.
The role of self-monitoring during the second, task performance phase of SRL
has received detailed attention. More successful students tend to spend proportion-
ately more time and effort monitoring the relationship between new and prior
knowledge, rather than simply trying to understand the new information without this
consideration. This finding is related to the both the purpose and the content of
thoughts that can be categorized as serving self-monitoring processes. For example,
Greene and Azevedo (2009) found that the presence of self-monitoring activities
such as self-questioning and judgments of learning were more powerful predictors
of knowledge growth from pre-test to posttest than planning or activities related to
managing task difficulty (such as help-seeking). The precursors to these self-
questioning activities also seem to be important. From close examination, Greene
and Azevedo (2007) identified several behaviors which differentiated more from
less successful students as they interact with electronically enhanced text. These
included management of the process of meaning construction, such as when
drawing inferences from the text or coordinating between sources of information
(e.g. between graphics and text); management of the interaction between oneself
and the material, such as when monitoring the adequacy of current understanding in
order to rate the adequacy and utility of the information being provided; and
management of one’s own knowledge state, such as when establishing judgments of
learning and monitoring the feeling of knowing.
In summary, it seems that individual differences in the tendency to engage in
macro-level planning and self-monitoring activities account for differences in
performance outcomes following interactions with electronically enhanced text.
One important question that follows is how to best support students’ self-monitoring
behaviors, particularly in DE courses where learner-teacher interaction is likely to
be asynchronous. As a matter of further relevance to this issue, we consider students
who have difficulties with the self-regulation, and how these difficulties may
influence interactions with instructional content.
110 L. Bol, J. K. Garner
123
Support for self-regulatory processes
In this section we consider for whom SRL support might be beneficial, and the
nature of the support that could be provided. We also consider more generally, ways
in which support for SRL processes could be incorporated into DE courses.
Students with weak self-regulated learning skills
Students with weak SRL skills are an obvious priority for targeted support. In
particular, since metacognitive planning and self-monitoring behaviors are associ-
ated with knowledge gains, one approach might be to focus support on these
processes explicitly. Students with weak SRL skills, according to Greene and
Azevedo (2007,2009) are unlikely to spend time planning their learning and more
likely to simply recycle the goals they do adopt in working memory. Thus, they may
benefit from prompts to engage in planning and to consider the goal of their learning
episode. Prompting a written or typed record of this goal may relieve working
memory burden and reduce overall cognitive load. Similarly, these students are
likely to spend more time focusing on the meaning of the instructional text and less
time comparing and monitoring the status of their prior knowledge in relation to the
meaning of the text. Prompt questions that encourage students to reflect on the
congruence of the new information with prior knowledge, or which require an
answer that integrates the new information into existing frameworks that have been
previously covered in the course, may facilitate more meaningful reflection.
As would be predicted by Zimmerman’s social-cognitive perspective on the
formation of SRL strategies and habits, in addition to supporting these processes
through prompts within the instructional sequence, it may be possible to intervene
and successfully train students to use specific self-regulatory strategies (Azevedo
and Cromley 2004). While at first glance it may seem that a training study would not
be relevant to DE settings, this study can be used as an illustration of the potential
benefits following even a short intervention. Here, the training employed consisted
of a brief 30 min instructional period in which each participant received a detailed
explanation of the phases of self-regulated learning with examples from the
hypermedia environment they were going to use. This guidance was provided by an
instructor prior to the experimental learning episode. Even with such a brief
intervention, students in the intervention condition were more likely to plan their
learning using the more effective strategy of activating prior knowledge rather than
the less effective behavior of recycling the goal in working memory. Differences
were also apparent in self-monitoring. Students in the intervention condition were
more likely to monitor their comprehension using feeling of knowing (FOK) and
judgment of learning (JOL) processes than students in the control condition, who
did not receive any training in SRL. These students were instead more likely to use
information adequacy judgments, in which relative emphasis was given to
the suitability of the information in terms of serving the learning goal, rather than
the relationship between the new information and prior knowledge. Students in the
intervention condition were also more likely to use effective generative strategies
during their learning episode, and were found to engage in note-taking,
Self-regulation and distance education 111
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summarizing, and coordinating informational sources. This was in contrast to
students in the control condition were more likely to engage in informational
searches.
This study raises the possibility that older students taking DE courses may benefit
from even a brief overview of self-regulated learning and examples of ways in
which they can use metacognitive strategies to facilitate their interactions with
electronically enhanced text. The conclusion is made, however, with the caveat that
the typical length of experimental learning episode is short (1 h or less) and limited
to one interaction with the material. Further research is needed to see whether or not
the effects of this type of short intervention would persist over time, whether or not
students of younger ages would gain any benefit from it, and whether or not this
brief intervention has efficacy in DE settings.
Students with poor calibration skills
Students may be weaker in particular areas of self-regulated learning compared to
others and may need further targeted support aimed at improving these skills. One
such area is calibration. Calibration is defined generally as the degree to which a
person’s perception of performance corresponds with his or her actual performance
(Keren 1991). The degree of correspondence is determined by a person’s judgment
of his or her performance compared against an objectively determined measure of
that performance (Hacker et al. 2008b). Thus, calibration is considered to be
metacognitive monitoring process. Monitoring provides information at the meta-
cognitive level about the status of one’s knowledge or strategies at a cognitive level
(Nelson 1996). Based on this information, metacognitive control can be exerted to
regulate one’s knowledge or strategies. In Zimmerman’s model (2000), self-
evaluation ‘‘refers to comparing self-monitored information with a standard or
goal’’ (p. 21). In numerous studies, calibration accuracy is measured by comparing
students’ judgments of their performance on an exam or individual item with their
actual performance (e.g., Bol and Hacker 2001; Bol et al. 2005; Maki et al. 2005).
These kinds of judgment may be prospective (predictions), aligning with the
forethought phase or retrospective (postdictions), aligning with the reflection phase.
Calibration is also important in the performance or volitional phase when a learner
continuously monitors and adjusts his focus contingent on feelings of knowing
(FOK’s) or judgments of learning (JOL’s). Therefore, it may be argued that
effective self-regulated learning is enhanced by accurate calibration in each of the
cyclical phases.
The poor calibration skills of the lowest achieving students have been well
documented in literature (Bol and Hacker 2001; Bol et al. 2010; Hacker et al.
2008b; Juslin et al. 2000; Maki et al. 2005). Typically, students with poor
calibration skills are inaccurate and overconfident when compared to their higher
achieving peers who are much more accurate and often underconfident (Hacker
et al. 2000,2008a,b). Lower-achieving students seem to anchor their calibration
judgments on optimistic yet inaccurate beliefs about their own abilities. Students
who are overconfident may have a false sense of how well they have mastered the
material. They may believe they are prepared when in fact they are at risk for
112 L. Bol, J. K. Garner
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failure. Or students could intentionally inflate their overconfidence during a learning
episode as a self-handicapping strategy, excusing or attributing their poor
performance to external causes (Winne 2004; Zimmerman 2000). Underconfidence
also can be detrimental to academic performance because students may fail to
disengage from studying and misallocate study time if they assume the material is
not yet mastered. Students with poor calibration skills may not take the remedial
steps necessary to improve their efforts (Hacker et al. 2008a,b).
The detrimental effects of poor calibration skills beg the question of whether and
how they can be improved. Attempts to improve calibration accuracy in classroom
settings have been met with mixed success. Contrary to expectations, repeated
calibration practice does not seem to enhance accuracy. In one illustrative
experiment (Bol et al. 2005), students in traditional and distance course formats,
took a series of five on-line quizzes. In the practice condition, students were
required to predict and postdict their quiz scores. Taking the quizzes on-line
provided students with immediate feedback on their scores and calibration accuracy.
Students in the control condition did not predict or postdict their performance on the
quizzes. The dependent variables were calibration accuracy and scores on the final
exam for both groups. The results revealed that calibration practice on the quizzes
neither enhanced calibration accuracy or exam performance. Calibration tends to be
stable, suggesting that feedback and practice alone are insufficient for improving
calibration accuracy. The findings that reflection and instruction on monitoring and
calibration were found to be effective were more promising (Nietfield et al. 2006).
In this experiment, weekly reflection coupled with feedback improved calibration
accuracy and performance on exams and the course project. Another study showed
that external rewards or incentives for more accurate calibration enhanced
postdiction accuracy among lower-achieving students. More recently, group
calibration practice and the provision of guidelines have been shown to improve
calibration accuracy and achievement among high school students (Bol et al. 2010).
The study by Azevedo and Cromley (2004) described earlier provided at least
indirect support for the effectiveness of SRL training for improving calibration.
They found that students in the intervention condition were more likely to monitor
their comprehension using feeling of knowing and judgment of learning, processes
underlying calibration, than students in the control condition, who did not receive
any training in SRL.
Students with executive function deficits
Distance education courses by their nature attract a diverse group of learners and
have historically attracted students who may be non-traditional in terms of their age
and learning histories; some may have literacy difficulties or learning disabilities
and others may be re-entering formal education after a decade or more. Few studies
have specifically examined learning with hypermedia and electronically enhanced
learning materials in these populations, so this is an area in which future research
will enhance our understanding of how a potentially significant proportion of these
students learn using these materials. For now, we focus on executive function
difficulties, in part because of their under-represented nature in this literature but
Self-regulation and distance education 113
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also because of the relevance of executive function constructs to reading
comprehension (Locascio et al. 2010) and self-regulated learning processes (Garner
2009; Manganello 1995; Peterson et al. 2006).
The term ‘‘executive functions’’ refers to a fairly broad group of neurocognitive
processes that support higher level cognitive and metacognitive functions. These
include planning, organization, self-monitoring and goal-directed behavior (Esling-
er 1996). At the neurocognitive level, executive functions give rise to control over
attention and working memory, and facilitate freedom from distractibility (Locascio
et al. 2010). Executive functions can be conceptualized on a continuum in much the
same way as other cognitive processes; that is to say, it is where some individuals
have strengths and others weaknesses. Individuals with effective executive
functions are likely to operate with broadly effective self-regulatory processes in
all areas of life, flexibly regulate goal-directed actions, and organize pieces of
information as well as their interaction with that information. Individuals with
executive function difficulties struggle with planning, self-organization and self-
monitoring, and may find it hard to screen out or otherwise not respond to
distracting stimuli. Deficits in executive functions are associated with a number of
risk factors including mild to moderate traumatic brain injury and a position on the
autism and Asperger’s spectrum, but for many individuals the source of weaknesses
in planning, organizational skills and attentional control may not be identifiable.
Like working memory capacity or cognitive ability, a range of executive function
performance exists even in the typical population (Garner and Tocker 2011). Yet,
while the deficit remains at the subclinical level the difficulties can nonetheless
interfere with everyday life (Garner and Tocker 2011; Manganello 1995; Peterson
et al. 2006; Wilson et al. 1996).
Executive functions have been widely investigated in the clinical psychological
literature, in part because neuropsychological and organic disorders tend to have an
obvious executive dysfunction component. More recently, however, educational
researchers have started to consider the prevalence and potential consequences of
subtle variations in executive functions on the cognitive, metacognitive and
interpersonal regulatory processes in which students engage (McCloskey et al.
2009; Metzler 2007; Garner 2009, Garner and Bol 2011; Peterson et al. 2006). An
executive functions-related component of attention deficit disorder and reading
disabilities have been revealed by recent research studies, but even in typical and
unimpaired samples there is evidence to suggest that variation in executive
functions has consequences for self-regulated learning processes and academic
achievement. Specifically, Garner (2009) found that executive functions accounted
for variance in metacognitive, cognitive and affective aspects of SRL, and both
Peterson et al. (2006) and Garner and Tocker (2011) found that college students
with sub-clinical risk factors for executive dysfunction were at risk of poor self-
regulatory processes and academic distress.
Thus, in conceptualizing the relations between executive functions and self-
regulated learning, we position executive functions as neurocognitive processes that
promote self-regulation at both the basic cognitive (e.g. attentional control) and
metacognitive (e.g. planning and self-monitoring) levels. Because of their
foundational role in cognitive and metacognitive regulation, difficulties with
114 L. Bol, J. K. Garner
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executive functions may impact the SRL cycle as it is employed during interactions
with electronically enhanced text. These difficulties may manifest themselves in
three ways. The first is at the macro-level of self-regulated learning processes, in
terms of the choices the learner makes when opportunities for strategic behavior
arise. The second is through reading comprehension processes, and the third is
through navigation behavior.
As previously noted, macro-level SRL processes include planning, self-
monitoring and self-evaluation. Poor executive functions may result in difficulties
recognizing the need for goal setting and strategic planning. Cognitive rigidity and
cognitive impulsivity—both included under the umbrella term of executive
dysfunction (Wilson et al. 1996)—may result in a learner beginning an interaction
with the learning materials without a clear goal in mind and being unable to switch
strategies when changes to the learning process are called for. Without specific
prompting and support, self-evaluation may not result in the necessary improve-
ments to SRL behaviors.
Also as noted previously, a large proportion of student-content interaction
involving electronically enhanced text entails reading. Before considering the
specific issue inherent to the presence of electronic enhancements such as
hyperlinks, it is worth considering that individuals with executive function
difficulties can exhibit difficulties with comprehension processes that are separable
from other specific reading disabilities (Locascio et al. 2010; Sesma et al. 2009).
That is, in spite of fluent word recognition processes, learners with executive
function difficulties can have difficulty constructing an overall understanding of the
‘big picture’ concepts contained within the material. Whether this difficulty arises
from a lack of control over attention and working memory processes, a failure to
organize importance pieces of information, or some other difficulty is not known.
Yet, the implications are that these learners may benefit from explicit support in
constructing a global representation of the text.
To further complicate matters, electronically enhanced text contains features
such as hyperlinks and search functions which make non-linear navigation not only
easy to do but visually and cognitively appealing. However, when reading materials
that contain hyperlinks, global reading comprehension processes can be negatively
impacted by the over utilization of these embedded links. This is the case even in
typical learners who do not have documented difficulties with either reading
comprehension or attentional control (Greene and Azevedo 2007). The use of
hyperlinks have also been questioned as a factor in making it more difficult for the
reader to build coherent representations of text (Zumbach 2006) and Greene and
Azevedo (2007) noted that excessive movement between areas within electronically
enhanced learning materials was associated with poor learning outcomes. Thus,
students must be more engaged in using reading strategies than when reading
traditional text (Schrader et al. 2008), but they must also be engaged in the exercise
of restraint in following links and navigating in a manner that does not suit the
overall goal of the learning episode. To do this requires impulse control, which is a
broadly recognized executive function (Spinella 2005). However, since impulse
control, control over attention and working memory, and the maintenance of
freedom from the influence of distracting stimuli (both intrinsic and external to the
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learning material) are problematic for students with executive function difficulties,
it may be hard for them to inhibit the desire to click on hyperlinks and other ‘‘hot’’
areas of graphics or to not view other pages on a website simply because they are
listed in the page outline. Since broad organization of a large number of pieces of
information is also a challenge for these students, comprehension outcomes may
ultimately be quite poor.
Supports for student learning with electronically enhanced material
When considering how to support learners’ interaction with electronically enhanced
text from the perspective of executive functions and metacognitive aspects of self-
regulated learning, it may be helpful to think about students’ needs in terms of the
four types of scaffolds identified by Azevedo et al. (2004). Azevedo and colleagues
identified conceptual scaffolds, which support the learner in selecting relevant
knowledge and information; metacognitive scaffolds, which facilitate students’
management and self-regulation of task accomplishment; procedural scaffolds,
which support students’ completion of tasks or use of learning resources; and
strategic scaffolds, which help students to be aware of strategic choices and
alternatives for successfully completing different learning tasks. Of these, strategic
and metacognitive scaffolds appear to offer the most relevance for learners with
regulation difficulties.
Students with weak executive function and self-regulated learning skills have
difficulties with planning, self-monitoring, self-reflection and organization of
material. Hence, metacognitive and strategic scaffolds may be of particular benefit.
Examples include Meyer et al.’s (2010) ePEARL online electronic portfolio system.
This unique software platform integrates electronic storage of work products with
features that prompt the creation of goals at the beginning of a work cycle, and the
ability to view and integrate feedback into subsequent versions of the work product.
Students can therefore retain and easily access concrete representations of their SRL
processes. The system has been found to improve middle school students’
metacognitive management of their learning, but the potential as an intervention
tool for students with specific self-regulatory and executive difficulties has not been
examined, and it is not clear how ePEARL could support the wide-scale
presentation of instructional material for students in DE courses.
Strategic scaffolds may be of particular use to students with self-regulated
learning and executive function difficulties because of the difficulty that these
students may have in selecting appropriate and alternative strategies when problems
are encountered. For example, if learning materials were to be presented through the
medium of an electronic learning platform, and if that platform could support
flexible problem solving behaviors by presenting a sequence of steps through which
students can navigate in order to consider more and less likely solutions to a
problem, then students may be able to interact more effectively and strategically
with the information even while they are relative novices in the subject area. For
instance, students who are learning within a scientific domain where the process of
hypothesis generation is key (e.g. medicine), prompts for the consideration of
116 L. Bol, J. K. Garner
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multiple instead of singular pieces of information, and several rather than one
potential diagnosis, might be helpful (Lajoie et al. 1998).
Students with poor calibration skills may particularly benefit from metacognitive
scaffolds and other supports that might be incorporated into on-line learning
environments. More specifically, Winne (2004) identified four strategies to promote
calibration of knowledge and learning processes in software design. The first
strategy was to guide learners to delay metacognitive monitoring. This strategy is
based on a phenomenon labeled the delayed JOL effect (Thiede and Dunlosky 1994)
that shows improved judgments after a learning delay similar to improved
achievement associated with distributed sessions over time. For example, learners
might be first asked to highlight a text and at a later time evaluate the highlighted
content relative to how well it is understood, how easily is can be retrieved, and how
it relates to the learning objective. A second strategy is the provision of forms and
timed alerts that guide students to summarize content. Thiede and Anderson (2003)
found that summarizing information after a delay improved calibration accuracy.
Furthermore, other research (Wood et al. 1995) suggests that the summaries were
more effective when forms and guidelines were provided. ‘‘Adding such forms as
software tools and pairing them with delay effect Thiede and Anderson reported
should enhance calibration and improve a skill for learning’’ (Winne 2004, p. 481).
It is likely that this finding was a byproduct of increased knowledge due to the
summarize technique. As students gain more knowledge their calibration accuracy
improves (Krugar and Dunning 1999). Helping learners review the ‘‘right’’
information is the third strategy. Students have a tendency to select ‘‘almost
learned’’ (high JOL) or more interesting content for restudy. If students were to rate
test items on JOL and interest they could be provided feedback indicating that
selection of content for restudy based on interest and minimal challenge may not be
the best choices. Finally, the development of more effective practice tests would
provide students with records of their performance on past tests as well as items (or
tasks) on those tests. This longitudinal data may improve students’ calibration over
time, shifting the basis of calibration judgments from past judgments to actual
performance. Calibration tends to be largely inaccurate and biased towards
overconfidence among lower achieving students because they anchor their
judgments on past predictions in an effort to protect their self-worth (Bol et al.
2005; Hacker et al. 2008a,b). Overall, Winne (2004) argued for the development of
electronically enhanced learner interfaces which not only present learning material
but which but also store information which students have indicated is important and
prompt more effective calibration judgments as an essential component of self-
regulated learning.
In addition to the Winne’s (2004) sound suggestions for enhancing calibration
skills in software design, the studies described earlier might also bear promise in
fostering calibration accuracy. Calibration practice coupled with reflection could be
efficiently built into course design. Although some studies did not reveal
metacognitive benefits associated with practice tests and feedback alone (Bol and
Hacker 2001; Bol et al. 2005), Nietfield et al. (2006) findings suggested that
requiring students to make calibration judgments coupled with reflections on their
accuracy about their accuracy and how performance might be improved was
Self-regulation and distance education 117
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effective in enhancing accuracy and performance. The intervention consisted of
exercises that asked students to assess their learning for the current session as well
as their study preparation, respond to and provide confidence ratings on review
items and reflect on the accuracy of their confidence ratings. In addition to weekly
feedback, the students were given feedback and interpretation of their calibration
accuracy the week following exams. Similarly, the findings by Hacker et al. (2008a,
b) revealed that providing students with information about their calibration
accuracy, having them reflect on this accuracy, and suggesting ways in which it
could be improved increased calibration accuracy for lower achieving students but
only when incentives (extra credit points) for increased calibration accuracy were
awarded. More recently, a study with high school students suggested that providing
students with guidelines designed to improve their predictions for their exam
performance improved calibration accuracy and test performance. This was
particularly true when calibration was practiced in group settings (Bol et al.
2010a,b). In the forethought phase, instructional designers might prompt students to
make predictions for particular topic areas or skills, feedback might be ongoing as
students engage in a learning episode, and strengths and weaknesses identified.
Guidelines for reflection (either individually or perhaps in group contexts through
discussion boards) could spark a revision in goals or plans to guide further study.
After a learning task students might receive feedback on performance and
calibration, engage in further reflection and perhaps, be rewarded for increasing the
accuracy of metacognitive judgments anchored on past performance. The computer
software facilitates this record keeping as well as the provision of delayed
calibration prompts, guidelines for reflection, rewards, and options for group
calibration and study.
At a more basic but equally important level from an instructional design
perspective, individuals with executive function difficulties can often have trouble
with working memory and attentional control, (McCabe et al. 2010; Scope et al.
2010). Attentional focusing is imperative during the performance or volitional
stage of SRL (Zimmerman 2000). Thus, since it is known that individuals with
poor selective attention capabilities pay attention to information that is more
intense or visually appealing than other aspects of information, and that as such it
is more difficult for them to maintain a goal in working memory, careful use of
hyperlinks and other visual aspects of electronically enhanced text must take
place in order to reduce the chances of becoming distracted, off-task or losing
track of recently assimilated information (Radosh and Gittelman 1981). To date,
very little research has been conducted with samples of students with self-
regulated learning and executive function difficulties to examine the specific
features of hypermedia and electronically enhanced learning materials on
students’ navigation and reading practices, judgments of learning, and actual
learning outcomes.
Tentative recommendations can be drawn from considering the needs of learners
with cognitive disabilities that include individuals with attention and executive
function-related difficulties. For example, Mariger (2006) explains that learners
who have difficulty with attention and working memory control benefit from
limited hyperlinks and an uncluttered visual display. Kanta (2001) presents a list of
118 L. Bol, J. K. Garner
123
modifications to web pages that can assist the learning who has a disability,
including providing auditory feedback, presenting the key information at the
beginning of the page, and using screen layouts which provide the option to view
only one area at a time. While intuitively significant, the recommendations do not
appear to be drawn from empirical studies and arguably these very general
principles would aid those without disabilities as well. In contrast, Zentall (2005)
provides an excellent summary of recommendations for the design of instructional
materials for individuals with attention-related difficulties. From his survey of
empirically based resources, we relate several key findings which have direct
consequences for the design of electronically enhanced learning materials and
which relate well to the processes of attentional focusing and self-instruction which
fall under the performance phase of Zimmerman’s model of self-regulated
learning. The first category of modification relates to the removal of extraneous or
unnecessary visual stimulation from the task, what Zentall called ‘‘irrelevant cues.’’
This principle, which is also aligned with multimedia principles of learning (Mayer
2001), calls for the removal of details not central to the concept being conveyed by
the materials and the removal of colored or visual stimuli on the screen which alert
the reader to this non-essential information. Secondly, Zentall recommends the
addition of features such as color to highlight essential information or elements.
The caveat in this area of course, is one familiar to instructional designers: the
provision of a sufficient number of cues and signals for important information
without the creation of visual distractions. The third area of recommendation
concerns the promotion of students’ self-instruction skills. Self-instruction
according to Zimmerman (2000) involves overt or covert self-articulation of the
steps involved in achieving an academic goal. Miranda et al. (2002) found that for
younger students with attentional difficulties, teaching specific self-instruction for
task completion (e.g. ‘‘I need to follow my plan’’) improved performance. Related
work by Reid et al. (2005) showed that the performance of children with
attentional difficulties could be improved by the provision of cue to self-monitor
progress. Thus in a manner similar to Winne’s (2004) call for the inclusion of
progress monitoring steps for all students within computer based learning
environments, it seems that a broad range of students could be helped by support
for basic self-regulated learning process such as planning, goal setting, self-
monitoring and self-reflection.
Summary and conclusions
This paper highlights challenges in supporting self-regulation in on-line or DE
contexts. We responded to Abrami’s et al.’s (2011) call to apply self-regulation
theory and research to help guide improvements in the next generation of distance
and on-line learning. Using Zimmerman’s cyclical model of self-regulation (2000),
we focused on selected design strategies that have shown promise in fostering
more effective self-regulation skills among learners. However, given the breadth of
our charge, a more narrow scope was required. Therefore, we primarily focused on
the role of self-regulation in student-content interactions and identified specific
Self-regulation and distance education 119
123
areas of difficulty for students working with electronically enhanced text.
Furthermore, we identified populations for whom SRL support might be
particularly beneficial and suggested ways to support SRL processes for these
learners. Students with weak general SRL skills, poor calibration skills, and
executive function deficits might be particularly at risk in DE courses that are
largely autonomous or self-directed in nature. One common support found in the
literature is the use of scaffolds to support self-regulated learning in on-line
learning environments (Azevedo et al. 2004). Scaffolds may be employed coupled
with electronic portfolios to guide planning, self-monitoring, self-reflection, and
organization (e.g., ePEARL developed by Meyer et al. (2010). As techniques to
improve calibration skills in particular and self-regulation more generally, Winne
(2004) suggests a delay between the learning episode and metacognitive
monitoring, the provision of timed alerts and forms that guide content summa-
rization, directing students to review appropriate information based on past
performance, and the development of more effective practice tests followed by
performance feedback. A review of calibration studies conducted in classroom
contexts points to the use of sustained, guided, and in-depth reflection across trials,
perhaps in conjunction with incentives and group practice, to promote more
accurate calibration, SRL skill and performance (Hacker et al. 2008a,b; Nietfield
et al. 2006; Bol et al. 2010a,b), Students with attention deficits linked to executive
dysfunction may profit from the removal of extraneous, unnecessary visual
stimulation, the provision of features to highlight essential information, and the
promotion of self-instruction skills involving self-articulation of the steps needed to
accomplish academic goals (Zentall 2005).
Incorporating evidence-based approaches like self-regulation theory into distance
education may help meet the challenge of improving student learning and
satisfaction in these contexts. Nevertheless, researchers need to bolster the evidence
base to evaluate and tailor these approaches to meet the needs of their students. This
line of inquiry should be expanded to include a broader range of students,
particularly students with poor SRL skills or nontraditional students who enroll in
DE courses. Researchers also need to account for potential interactions that may
occur when students are strong domain experts but have weak SRL skills or vice
versa. Extending episodes of learning beyond short-term experimental studies
would further advance our understanding. More in-depth, longer term studies might
focus on a protracted course of study and how students effectively employ SRL in
the face of multiple distractions and demands on their time. Furthermore, research is
sparse on studies investigating the role of SRL in other types of interaction that
occur in on-line and distance education settings. Exploring ways to best integrate
SRL into student–teacher and student–student interactions has been emphasized less
than in student-content interactions. All three types of interaction are important and
demand effective use of SRL. Anglin et al. (2010) contend that high reliability
interventions must be applied to distance education, with practice following
research. We strongly concur and recognize that though initial research to
understand SRL in distance education contexts has been promising, much more
work remains.
120 L. Bol, J. K. Garner
123
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Author Biographies
Dr. Linda Bol is a Professor in Educational Foundations and Leadership with a program emphasis in
educational psychology and research. Dr. Bol obtained her doctorate from the University of California,
Berkeley and teaches graduate courses in theories of learning, research methods, program evaluation, and
classroom assessment. She has maintained an active research agenda in cognitive psychology as it relates
to classroom learning. For example, she has also published studies on the cognitive demand associated
with teachers’ assessment practices and ways to promote students’ self-regulated learning. Most recently,
she has investigated how students’ metacognitive judgments predict study strategies, explanatory style
and achievement. Furthermore, Bol has conducted numerous evaluation studies of educational programs
aimed at promoting achievement of at-risk youth.
Joanna Garner is a Research Assistant Professor in the Center for Educational Partnerships at Old
Dominion University, Norfolk, VA. Prior to her appointment she was an Assistant Professor of
Psychology at Penn State University, Berks College. She received her Ph.D. in Educational Psychology
from Penn State University in 2003. A native of the United Kingdom, Dr. Garner earned B.Sc. (Hons) and
M.Phil. degrees in Psychology from the University of Surrey, UK. Her research interests focus on
psychological variables that impact student learning, including the interplay between self-regulated
learning and executive functions, and the application of principles of cognitive psychology to multi-media
learning and instruction.
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