GOOD FILE

1. Instructional Science (2005) 33: 559–565 Ó Springer 2005
DOI 10.1007/s11251-005-1280-9
A perspective on state-of-the-art research on self-regulated learning
PHILIP H. WINNE
Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada
(E-mail: winne@sfu.ca)
Theory and research on self-regulated learning (SRL) are growing
rapidly but the field is still within grasp. A search of PsycINFO (2005
June 25 08:30) using the phrase ‘‘self-regulated learning’’ yielded 463
hits when the search examined all text. There were 207 hits when the
search focused just on titles. Using the Google Scholar search engine,
the exact phrase ‘‘self-regulated learning’’ yielded about 471 hits (2005
June 25 08:31; Google does not report an exact number of hits). The
papers in this special issue focus on a particular sector of this field,
scaffolding self-regulated learning and metacognition in either face-to-
face tutoring sessions or computer-based learning environments. Was
this a wise choice?
In 1976, when there was a single article (Mlott et al. 1976) in the
literature that used the phrase ‘‘self-regulated learning’’ (PsycINFO,
2005 June 25 08:32 searched using dates 1900 to 1976), Wood et al.
(1976) introduced the term scaffolding. By this, they meant ‘‘a form
of assistance that enables the child or novice to solve a problem, car-
ry out a task, or achieve a goal that would be beyond his or her
unassisted efforts’’(p. 90). A nascent question was born: Do learners
need scaffolding to self-regulate learning? The answer is: Yes and no.
When there were 103 publications on self-regulated learning in
1995 (PsycINFO, 2005 June 25 08:36 searched using dates 1900 to
1995), I argued self-regulating learning is ubiquitous (Winne, 1995a,
1995b). This is logically entailed on adopting any of several current
and widespread stances, two among them being particularly promi-
nent: Learners are agents. Learners construct knowledge. Whether
scaffolding is available or not, these paradigmatic stances necessitate
that learners can and do self-regulate learning. Empirically, it is
impossible to prove every learner is constantly engaged in SRL be-
cause data to validate this claim can not be collected for each learner
at every instant whenever they learn. Studies show, however, that
SRL is common across learners and tasks (e.g., see Boekaerts

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et al. 2000). On these facts, learners do not need scaffolding to engage
in SRL.
If SRL is ubiquitous, why aren’t all learners better at learning than
they are? Learners vary with respect to qualities of SRL and parame-
ters that affect SRL (e.g., Howard-Rose & Winne, 1993; Winne, 1997,
in press). Some learners therefore are at absolute or relative disadvan-
tage. That is, although they self-regulate learning, they do so at times
and/or in ways that are less than optimal. The obvious hypothesis is:
Can learners with particular profiles of individual differences that del-
eteriously affect SRL benefit from scaffolding? A follow-up question
is: What kinds of scaffolding are appropriate for which learners en-
gaged in which tasks? On these points, the researchers publishing in
this special issue pursue crucial issues. Their focus is important to
advancing theory and research on SRL.
SRL is a learner’s design experiment
With and without scaffolding, learners engage in design experiments
wherein they follow Miller et al. (1960) fundamental cycle of test-
operate-test-exit. That is, in striving to optimize learning relative to
their goals, learners modulate how they cognitively operate on infor-
mation to accomplish tasks. They choose among options for opera-
tions based on their individual analysis of task conditions (the first T
in TOTE). Then, they test (the second T in TOTE) whether those
operations satifice (Simon, 1953). If they do – if the goal is achieved
sufficiently, they move on (exit, the E in TOTE). Otherwise, they test
the now updated state of conditions, choose operations, and try again
(see Winne, 2001).
Where do these studies lead?
The articles in this special issue, with varying nuance, present several
important general results. First, learners’ self-managed, design experi-
ments in self-regulating learning that they carry out in authentic
learning environments often do not meet our (instructors’) standards.
That is, learners do not achieve as highly as we intend. Second, there
are scaffolds that make positive contributions to learners’ SRL and
thereby enhance outcomes. Third, scaffolds can stand in various rela-
tionships to computer-based learning environments and face-to-face
tutoring – as an informational part of the environment and as a social

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supplement to it. Fourth, as evidenced in the studies by Azevedo et al.
(2005) and by Choi et al. (2005), some theoretically useful scaffolds
are not successful.
Several specific findings reported in this issue merit recapitulation.
Dabbagh and Kitsantas’ (2005) study shows that learners report they
differentiate features of their instructional environments becasue they
match particular web-based pedagogical tools to specific intentions
about engaging in learning. Azevedo’s et al. (2005) and Puntambekar
and Stylianou’s (2005) studies document that specific forms of scaf-
folding, including dynamic and intelligent regulation offered by a hu-
man tutor in Azevedo’s et al. (2005) study, can positively affect
learners’ achievement. Choi’s et al. (2005) and Hadwin, Wozney, and
Pontin (2005) studies reveal that ordinary collaboration is insufficient
to transform learners’ ‘‘everyday’’ experiments in self-regulation into
productive SRL. Students need support and, as Hadwin et al. (2005)
show, there is a developmental trajectory across kinds of scaffolds
and balances among them at particular points along that trajectory.
Taken together, what can be suggested on the basis of these sum-
mary findings as well as the specific findings in each article? I offer
four main conclusions. First, regardless of which particular paradig-
matic belief system entails SRL, there is empirical evidence that learn-
ers intend to regulate learning and that SRL influences outcomes.
This is an important matter because, by Occam’s razor, learning sci-
ence should strive to eliminate from its catalog any variables that are
epiphenomena. SRL should remain a prominent entry in the catalog
of matters to be investigated by learning science. Second, several of
the studies offer empirical evidence that, left to their own devices,
learners’ SRL is suboptimal. Scaffolds can intervene to accomplish
what Wood et al. (1976) defined them to be, namely, effective aids.
Third, SRL has properties of a skill in that it is necessary to provide
guided practice with feedback over time to transform it from a less
effective to a more effective activity. The span of time appears to be
moderated by the degree of adaptive expertise embedded in the scaf-
folding – e.g., contrast a human expert to a peer or oneself – and the
sort of information yielded by the second test of Miller’s et al. (1960)
TOTE cycle, that is, metacognitive monitoring. Fourth, a variety of
forms of data – process data, outcome data, self-report data, and
time tags – are required to paint a full portrait of SRL.
In parallel to researchers’ programs of studies about SRL, lifelong
learners pursue a program of successive design experiments to
improve learning. The findings summarized above correspondingly
have parallel implications for learners about probing how to learn

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and how to pull themselves up by the bootstraps to learn better
(Winne, 1997, in press). First, many learners may need to bring SRL
into mindful focus, as the field has done since 1976 when the first
article used the phrase self-regulated learning (Mlott et al., 1976). Sec-
ond, SRL matters when learning is suboptimal. Learners must be able
to recognize this state. Equally as important, to initiate and persist in
their design experiments, learners must subscribe to a system of epis-
temological and motivational beliefs that classifies failure as an occa-
sion to be informed, a condition that is controllable, and a stimulus
to spend effort to achieve better. Third, learners must have access to
kinds of information that can transform the ways they learn. In par-
ticular, they need process feedback (Butler & Winne, 1995) that in-
forms them about the cognitive operations they apply alongside
knowledge of results that indicates the extent to which the products
of those cognitive operations meet standards for success. Fourth, be-
cause the portrait of SRL is complex and one that changes over time,
learners also probably need methods for keeping track of all these
data and for mining them to recognize patterns. Like researchers,
they need tools to do their research on how to enhance SRL.
Implications for future research on SRL
Given the parallels just sketched between researchers’ programs of
studies and lifelong learners’ own research programs, what might be
key items on the agenda for future research on SRL? Guided by the
model of SRL Hadwin and I proposed (Hadwin & Winne, 1998;
Winne, 2001), I suggest several that I believe are key. My choices are
based on intuition (i.e., my biases). I expect my list is readily cri-
tiqued, and it should be.
Bootstrapping SRL by carrying out a longitudinal series of design
experiments is a succession of problem solving exercises of large mag-
nitude. What scaffolds and tools would help learners manage this
over a long term activity? We can look to our own programs of
research for some heuristics?
Our research proceeds by generating hypotheses that are logically
entailed by models. Learners with stronger programs of SRL-focused
design experiments will likely have better models of learning. What
are those models? How do learners perceive relations among their
models of learning and the real world of features in instructional
designs? A first topic for research is the models learners have and the
ways in which learners perceive those models can change. There are

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broad and rich literatures on mental models, epistemological stances,
and theories of mind. I recommend mining these and testing the
transferability of findings reported in those literatures in the context
of learners bootstrapping more effective SRL. Dabbagh’s et al. (2005)
and Hadwin’s et al. (2005) studies take a step toward this issue. Punt-
ambekar and Stylianou’s (2005) notion of metanavagational support
might also be a fruitful approach to explore.
In programs of research that we carry out, we have elaborate
methods for recording, analyzing, reporting, and synthesizing data
over time and from multiple inputs. How can scaffolds and tools do
the same for learners with respect to their successive attempts to ap-
ply and improve SRL? In particular, how can learners obtain process
feedback about topics that Azevedo’s et al. (2005) human tutors con-
sider in providing adaptive guidance to learners? Colleagues and I
(Winne et al. in press) are completing a software application that will
afford some of this functionality. How might learners collaborate to
do this in contexts like those in Choi’s et al. (2005) and Hadwin’s
et al. studies?
Third, there remain fundamental issues to be investigated about
how learners carry out metacognitive monitoring and how to increase
the accuracy of metacognitive monitoring (e.g., Jamieson-Noel &
Winne, 2003; Winne & Jamieson-Noel, 2002, 2003). Without good
data about how learning proceeds, progress on bettering SRL will be
slow. What tools and scaffolds can be invented to improve metacogni-
tive monitoring?
Coda
In all this work, it is important to not lose sight of a key element of
SRL. Learners are agents who construct knowledge in the changing
milieu framed by knowledge, beliefs, motivational dispositions and
other propositions ‘‘in’’ their minds plus information they access in
their environment, whether this be from solo studies or participation
in social contexts. We must come to understand better what learners
understand and how they forge these understandings. The former is
an ontological question that can be informed by empirical work. The
latter requires a strong partnership between learners and researchers
to expose the ways minds work as they learn and as they learn how
to learn.

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Acknowledgments
Support for this research was provided by grants to Philip H. Winne
from the Social Sciences and Humanities Research Council of Canada
(410-2002–1787 and 512-2003–1012), the Canada Research Chair pro-
gram, and Simon Fraser University.
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