Subscribe Today
Home Articles Reader Opinion Editorial Book Reviews Discussion Writers Guide About TCRecord
transparent 13

Self-Regulation, Coregulation, and Socially Shared Regulation: Exploring Perspectives of Social in Self-Regulated Learning Theory

by Allyson Hadwin & Mika Oshige - 2011

Background/Context: Models of self-regulated learning (SRL) have increasingly acknowledged aspects of social context influence in its process; however, great diversity exists in the theoretical positioning of �social� in these models.

Purpose/Objective/Research Question/Focus of Study: The purpose of this review article is to introduce and contrast social aspects across three perspectives: self-regulated learning, coregulated learning, and socially shared regulation of learning.

Research Design: The kind of research design taken in this review paper is an analytic essay. The article contrasts self-regulated, coregulated, and socially shared regulation of learning in terms of theory, operational definition, and research approaches.

Data Collection and Analysis: Chapters and articles were collected through search engines (e.g., EBSCOhost, PsycINFO, PsycARTICLES, ERIC).

Findings/Results: Three different perspectives are summarized: self-regulation, coregulation, and socially shared regulation of learning.

Conclusions/Recommendations: In this article, we contrasted three different perspectives of social in each model, as well as research based on each model. In doing so, the article introduces a language for describing various bodies of work that strive to consider roles of individual and social context in the regulation of learning. We hope to provide a frame for considering multimethodological approaches to study SRL in the future research.

Contemporary models of learning recognize that learners are not passive recipients of knowledge. Rather than seeing learners as “empty vessels” waiting to be filled with knowledge, theorists and teachers acknowledge them as active participants in the learning process (Brown & Campione, 1996; Bruning, Schraw, & Ronning, 1999; Phillips, 1995). As a result, learners are viewed as constructors of knowledge rather than recipients of information. In other words, human knowledge—whether it be the bodies of public knowledge known as the various disciplines, or the cognitive structures of individual or learners—is constructed (Phillips).

Although this notion seems simple at first, it is central to many debates about learning and instruction. The degree to which students are credited with agency to direct their own knowledge building, integrate prior knowledge, and learn to act on or influence both the knowledge and the learning environment is central to teasing apart multiple uses of the term constructivism (cf. Detterman & Sternberg, 1996; Driver, Asoko, Leach, Mortimer, & Scott, 1994; Prawat, 1996). Some constructivists focus their attention on active individual minds, whereas others focus on the growth of knowledge in a learning community or culture (Phillips, 1995). Regardless of where they are situated along this continuum, contemporary constructivist theories recognize both the active participation by learners and the social nature of learning (Bruning et al., 1999).

Increasingly, learning is viewed as a situated activity (e.g. Brown et al., 1993; Bruning et al., 1999; Lave & Wenger, 1991). In other words, the way students come to understand theory, content, learning strategies, and themselves as learners is deeply rooted in the contexts in which they learn. Theories of situated cognition suggest that both what students learn and the ways in which they learn are situated both socially and situationally in these contexts (Salomon, 1993). Students learn how to strategically engage in reading, writing, studying, critical thinking, and problem-solving by testing out study tactics to complete classroom tasks and abstracting, evaluating and adapting those studying tactics for new learning contexts (Brown et al.).

Historically, models have portrayed self-regulated learning (SRL) as an individual, cognitive-constructive activity (e.g., Winne, 1997; Zimmerman, 1989) that integrates learning skill and will (McCombs & Marzano, 1990). Such models emphasize individual agency and individual differences associated with SRL, including self-efficacy, metacognition, goal-setting, and achievement (Schunk, 1990, 1994; Zimmerman, 1990). In addition, the notion that social context or environment is an important part of student’s SRL is evidenced in Zimmerman’s (1989) sociocognitive model of self-regulation: SRL involves personal perceptions and efficacy, as well as environmental conditions such as support from teachers and feedback on previous problems.

Although most models of SRL acknowledge aspects of social context, there is great diversity in where social is positioned in the model, from a peripheral contextual input for individual SRL to a socially shared process. Bruner (1996) has suggested that the field of educational psychology does not have a strong history of developing models or methodologies that explain cognition and context in relation to each other. Typically, and in the case of models of SRL, human thought and action are considered in ontological isolation from context and culture (Martin & Sugarman, 1996). Some contemporary views of SRL acknowledge external influences and the role of context as inputs to a self-regulatory system. However, there has been little attempt to bridge theories of SRL that move along the ontological continuum from regulation in the mind of an individual, through regulation as shared and distributed among individuals (cf., Meyer & Turner, 2002).

The role of social context in self-regulation has evolved over the last 20 years. Corno and Mandinach (2004) suggested that contemporary perspectives of learning and SRL reveal: (a) increased interest in explaining the role of social and contextual influences on SRL, and (b) shifts to models that place social context in the sociocultural center of SRL. As a result, emerging perspectives of SRL move beyond Zimmerman’s (1989) earlier conception of social context being a component in the triadic process, and toward social being at the core of SRL. As a result, a range of models of SRL have emerged. These models move along a continuum, from more individual constructivist perspectives to more social constructionist perspectives of learning (cf., Hadwin, 2000; Meyer & Turner, 2002).

This article examines models of SRL to investigate the role of social context, interactions, and influence in those models. Models were drawn from a broad continuum, from sociocognitive models (Zimmerman, 1989, 2000), to sociocultural models (Diaz, Neal, & Amaya-Williams, 1990; Gallimore & Tharpe, 1990; McCaslin & Hickey, 2001), through to social constructionist models of SRL (Jackson, Mackenzie, & Hobfoll, 2000; Yowell & Smylie, 1999). Specifically, this paper contrasts (a) the role of social influence, (b) the emerging language for describing self-regulated learning (self-regulation, coregulation, or socially shared regulation), and (c) empirical methods for researching social aspects of SRL at various points along a social continuum.



The term self-regulated learning emerged largely from a sociocognitive perspective. Self-regulated learning refers to strategic and metacognitive behavior, motivation, and cognition aimed toward a goal. “Students can be described as self-regulated to the degree that they are metacognitively, motivationally, and behaviorally active participants in their own learning process” (Zimmerman, 1989, p. 329). Sociocognitive models of SRL emphasize modeling and prompting as key instructional tools for promoting SRL. Social context is central in framing and influencing student self-regulation, and the underlying goal is to enhance the individual’s regulation of cognition, metacognition, behavior, and motivation. Social and self are viewed as distinct entities whereby social influences shape the development of SRL by defining conditions for tasks and providing standards, feedback, and modeling. From this perspective, self-regulation refers to an individual’s process, within which contextual and interpersonal feedback affect the acquisition, while an individual’s goals and efficacy influence the motivation (Jackson et al., 2000). The focus is on an individual as a regulator of a behavior (individual-oriented process).

From a sociocognitive perspective, learners actively interpret and reorganize ideas. Opportunities for learning are orchestrated by an instructor or other social influences. In other words, learners are active agents who strive to take control of their learning while being influenced by self and social factors (e.g., Schunk & Zimmerman, 1997). Self-regulated learning is socially influenced, beginning with observational learning (modeling, verbal description, social guidance, and feedback) and later by self-imitation and self-regulation. In other words, students learn through a process of modeling whereby they pattern thoughts, strategies, and behaviors to reflect those displayed by one or more models (Schunk, 1998).

Importantly, from a sociocognitive perspective, SRL is situation specific. As a result, students can vary dramatically in their self-regulatory competence and strategies from task to task and domain to domain. Both Schunk (2001) and Zimmerman (2000) emphasized the importance of social context and instruction in providing (a) modeling, (b) opportunities for guided practice, and (c) instrumental feedback. Through these social processes, students develop competence with the task, content, and context, thereby becoming self-regulated learners. Self-regulated learners rely on internal standards, self-reinforcement, self-regulatory processes, and self-efficacy beliefs. To elaborate:

There is extensive evidence (Rosenthal & Zimmerman, 1978) that adult models withdraw their support as observing youngsters display emulative accuracy. Reciprocally, these youngsters, seeing their increased proficiency, seek to perform on their own, such as when a young boy spurns further assistance from his mother when he feels he can tie his own shoelaces. At this point, the boy’s reliance on his mother as a social model becomes selective, and he will seek assistance from her mainly when he encounters obstacles, such as a novel type of shoelace. (Zimmerman, 2002, p. 5)

In Zimmerman’s example, the mother is a social influence on the child’s self-regulation. The mother is not viewed as self-regulating for the child; rather, she models how to tie the shoelace (the task) and provides feedback and instrumental support as the child learns to master the task himself. From this perspective, self-regulated learning is a developing process within the individual, who is assisted by task modeling and feedback provided by others.


Just as particular models and views of SRL are associated with the sociocognitive perspective, particular research methods and data collection tools have also been prominent in research about self-regulated learning.

Studies appear to support the social cognitive model’s claim that self-regulatory skills originate in others and are influenced by the context in which learning occurs (e.g., Cleary & Zimmerman, 2004; Kitsantas, Zimmerman, & Cleary, 2000). For example, building on Zimmerman’s (1989, 2000) model, Cleary and Zimmerman developed the Self-Regulation Empowerment Program (SREP) for school professionals to help students acquire self-regulation skills. In this program, students’ specific study strategies and motivation were first assessed by using semistructured and unstructured interviews. Second, students’ self-awareness and self-efficacy were increased by self-recording their strategies and discussing error attribution. Third, a tutor demonstrated and verbalized how he or she used a context-specific strategy. After the demonstration, students practiced the strategy presented. Finally, the acquired strategies were examined to see if they occurred in a cyclical, self-regulated way. Although the program has not been empirically examined, a case study with 12- year-old students confirmed the positive effect of this program, as well as the social cognitive model of SRL. In this study, self-report and interview data were collected about learners’ study strategies, motivation, and self-efficacy; social context was part of the intervention but not part of the unit of analysis.

Similarly, Kitsantas et al. (2000) examined the effects of modeling and feedback on the acquisition of dart-throwing techniques, self-efficacy, satisfaction with skills, interest level, and error attribution styles in 60 ninth-grade girls. The ninth graders were randomly assigned to one of six groups: coping modeling with and without feedback, mastery modeling with and without feedback, and no modeling with and without feedback. Coping modeling refers to models that showed gradual success in acquiring dart-throwing skills. Mastery modeling refers to models that presented perfect techniques only. Feedback was restricted to social appraisal, and no corrective feedback was given. Overall, the study found that participants in coping modeling acquired better dart skills, scored higher self-efficacy, displayed higher satisfaction levels, and showed higher interest levels than those in mastery modeling, who scored higher on all these scales than the no modeling group. Further, those in each modeling group with feedback scored higher on all the scales than the groups without feedback. In comparing error attribution styles, those in coping modeling tended to attribute their errors to wrong strategy choices, whereas those in mastery and no modeling groups tended to attribute them to inability and lack of practice. The study concluded that the findings validated the significance of social learning and self-beliefs in SRL and provided further evidence for the social cognitive model. In Kitsantas et al.’s study, the unit of analysis was again individual dart throwers. Dependent variables consisted of skill performance (dart-throwing skills), self-efficacy, satisfaction, and error attributions. Social context was varied as an independent variable in terms of modeling and feedback combinations, but it was not a focus for data collection and analysis.

Similarly, Azevedo, Cromley, and Seibert (2004) examined differences in student domain knowledge, mental models, and self-regulation under three different scaffolding or support conditions. Rather than examining coregulatory dialogue, they manipulated it to examine differences in the resulting outcome of self-regulated learning. Social context—in this case, scaffolding condition—was varied as an independent variable, and individual think-aloud protocols were coded for aspects of self-regulation. Consistent with sociocognitive perspectives of self-regulated learning, individual self-regulatory activity was the focus in this study, and data were aggregated across individual scores for analysis. Social context (scaffolding conditions) was manipulated to examine the outcomes on individuals’ mental models and self-regulatory engagement. In addition to the research among secondary and middle school students, studies conducted among different populations and in different contexts also seem to confirm the social cognitive model (e.g., Perry, 1998; Purdie & Hattie, 1996; Sanz de Acedo Lizarraga, Ugarte, Cardelle-Elawar, Iriarte, & Sanz de Acedo Baquedano, 2003). Perry observed writing and portfolio activities in Grade 2 and Grade 3 classrooms over 6 months and interviewed the students to examine young children’s SRL. The study found that students in the classrooms that allowed more self-control over writing and provided teacher assistance with the writing process chose more challenging topics and were more intrinsically motivated than students in the classrooms where teachers had control over the topics and provided only corrective evaluation about mechanical aspects of writing. Consistent with a sociocognitive perspective of self-regulated learning, Perry’s study compared individuals’ self-regulatory choices and motivations when social context varied across classrooms in terms of learner control, teacher support, and feedback. In contrast to other studies described in this section, Perry did not manipulate social context (classroom conditions). Instead, she examined qualities of classrooms through observations and interviews. Perry’s work makes a significant contribution to research on the sociocognitive aspects of SRL because both the individual and the social contexts were targets for data collection and analysis. Perry qualitatively examined evidence of self-regulated learning and the classroom context (classroom control, types of feedback, challenge, and peer support or interaction) that supported SRL. Nevertheless, we have classified this research as sociocognitive because it focused on how different instructional or social contexts influence student self-regulation of learning rather than on the dynamic interplay between social context and regulation.

Although space precludes reviewing the vast array of studies on self-regulated learning, those described previously illustrate two aspects of research about self-regulated learning. First, the unit of analysis is usually the individual. Data are often collected about aspects of SRL, such as individual performance, strategies, efficacy, behaviors, goal-setting, and self-evaluation. Second, social aspects of self-regulated learning are (a) absent from data collection and analysis, (b) examined as a separate data source from individual SRL, or (c) manipulated as an independent variable in the research design. The importance of social context and processes is considered to play a central role in individual SRL. However, the target of interventions and the unit of analysis typically focus on individual SRL and occasionally on social contextual features or factors. Sociocognitive research about SRL does not focus on the interaction between social influences and individual self-regulated learning.



Coregulation refers to a transitional process in a learner’s acquisition of self-regulated learning, within which learners and others share a common problem-solving plane, and SRL is gradually appropriated by the individual learner through interactions. Typically, coregulation involves a student and an other (usually a more capable other, such as a more advanced student, peer tutor, and so on) sharing in the regulation of the student’s learning. We use the term capable other quite loosely to acknowledge that it refers to a role rather than a particular person. For example, in a teacher-student dyad, both the teacher and the student bring diverse forms of expertise to bear on the problem at hand. During coregulatory activity, all participants assume expert and novice roles through varying aspects of the shared activity. In contrast to a sociocognitive perspective of SRL that emphasizes self-regulation developing within the individual assisted by external modeling and feedback, coregulation emphasizes social emergence and sharing of who actually does the regulation through a zone of proximal development. As in the previous example, rather than the mother modeling how to tie a shoelace, she takes on the regulation of her son’s shoelace tying by metacognitively monitoring and evaluating for her son. The mother might ask questions like: What do you know about how to connect those two laces? How do you know when you have completed the first step properly? What do you need to do now? In this way, the child focuses on task enactment while an external other supports his metacognitive engagement and regulatory control of learning. Cognitive demands of completing the task are eased by sharing the demands of metacognitively monitoring, evaluating, and regulating the task processes.

Although multiple perspectives of coregulation appear in the literature, they tend to be grounded in Vygotskian views of higher psychological processes being socially embedded or contextualized (Vygotsky, 1978), and Wertsch and Stone’s (1985) notion that these kinds of higher psychological processes are internalized through social interaction (McCaslin, 2009). For example, McCaslin and Hickey (2001) theorized that coregulation is a manifestation of emergent interaction within a zone of proximal development; teachers and students transition from coregulating learning toward self-regulation of learning.

McCaslin (2009) emphasized that coregulation occurs though activity, engagement, and mutual relationships in which individuals bring areas of expertise to novel learning tasks. Working within the zone of proximal development, participants are necessarily enriched or informed through interaction. From coregulation in the zone of proximal development emerge (a) self-regulation (adaptive learning, motivation, and identity), and (b) social and cultural enrichment. Therefore, social and cultural opportunities in schools afford and constrain opportunities for learning and are in turn shaped by coregulatory interaction and activity itself.

From this perspective, Hadwin, Wozney, and Pontin (2005) examined coregulation as an emergent process in interaction. Through dialogue and interaction, individuals learn to engage and control their own self-regulatory strategies, evaluations, and processes by observing, requesting, prompting, or experimenting with self-regulation with a supportive other. Research examined teacher–student dialogue to identify mechanisms used in coregulatory interactions and found that (1) teachers coregulate learning by requesting information, restating or paraphrasing students, requesting judgments of learning, modeling thinking, and providing prompts for thinking and reflecting, and (2) students coregulate learning through discourse acts such as requesting information, requesting judgments of learning, summarizing, modeling thinking, and requesting restatements. Consistent with Vygotskian perspectives, students gradually appropriated self-regulatory activity toward independent regulation, choosing relevant information for themselves and generating their own judgments of performance and learning. Interaction and coregulation are the processes that support learners as they begin to appropriate their own self-regulatory processes.

Recent research by McCaslin and colleagues (e.g., McCaslin & Burross, 2010, this issue) has explored coregulation in the larger sociocultural context of schools and classroom learning communities. Building on McCaslin’s (2009) model of coregulation, which suggests that personal, cultural, and social influences together coregulate identity as it emerges in school, this research examines the ways in which instructional opportunity and student engagement/activity operate as reciprocal entities affording, shaping, and coregulating student identity in the classroom. Rather than focusing exclusively on individual self-regulatory characteristics, McCaslin and Burross explore the dynamic interplay between personal influences (individual differences in readiness, ability, and potential that may also be valued and socially validated), social influences (opportunities and relationships that influence opportunities and experiences or provide practical opportunities/realities), and cultural influences (norms and challenges that contextualize opportunities and outcomes in schooling). Specifically, they focus on coregulation dynamics in classrooms where instructional practice and student activity or adaptive learning inform achievement and classroom communities. Here the notion of adaptive learning extends beyond individual self-regulation and instead on the community of practice—the way learning communities adapt and evolve as personal, social, and cultural influences come together.

Coregulation has also been used to describe developmental stages or progression in the self-regulation of young children. From a sociocultural perspective, SRL is a stage occurring as children are socialized into speech patterns and practices (Gallimore & Tharp, 1990). Coregulation is the temporary sharing or distributing of self-regulatory processes and thinking between a learner and a more capable other (peer or teacher), whereas the learner transitions toward self-regulatory practice. Therefore, self-regulation exists first in the practices of adults as they model activities. Over time, learners appropriate those activities and practices as they begin to understand how they support learning and how they are enacted (Gallimore & Tharp). From this perspective, the mark of student SRL is when the activity and practice appear in learners’ own performance and when those activities are internalized and automatized.

Diaz et al. (1990) support this view, suggesting that self-regulation is first seen as a social process because it appears first in shared practices. Later self-regulation becomes part of a child’s understanding, thereby appearing within individual practices. This work has been influenced largely by Vygotsky’s notion of internalization; self-regulatory functioning and control are transferred from the social to the individual psychological planes. As a result, this perspective emphasizes appropriation and joint problem-solving rather than modeling as core processes in the social exchange between learners and more capable others. That appropriation and joint problem-solving constitute coregulation; intersubjectivity is created, the problem situation is redefined, and, through common goals and task definitions, self-regulatory control gradually emerges. That control shifts from other, to mutual (or inter-, co-, together), to learner control over time and practice.

Not surprisingly, coregulation relies on two processes: scaffolding and intersubjectivity. Scaffolding becomes a primary mechanism for relinquishing control of SRL to students as competence and mastery emerge. It provides a means for teacher control and support to be gradually withdrawn or even redirected to better match student appropriation of self-regulatory activity. Importantly, the kind of scaffolding we refer to here is scaffolding of the self-regulatory cognitive and metacognitive processes rather than scaffolding content knowledge per se. Intersubjectivity involves sharing rationales and explanations of plans, goals, and activities in the common regulatory space.

Coregulation is an interactive process whereby regulatory activity and thinking are shared among participants and embedded in the interactions among person, context, and culture. Hadwin et al. (2005) described coregulation as a process that occurs as control moves from teacher regulation of student learning to student self-regulation of learning. During teacher direct-regulation of learning, the teacher (other) is doing or demonstrating self-regulated learning. During coregulation, the regulation is shared between self and other. Students and teachers take turns prompting and guiding one another to take over some of the regulatory activities such as monitoring progress or self-evaluation. During coregulation, student and teacher regulate together, sharing thinking and decision-making and developing a shared or intersubjective task space where each can bring expertise and control to the task. And finally, during student-direct regulation of learning, the student independently engages behaviors, actions, and thinking associated with self-regulated learning.

Examples of coregulation include the following:

1. Teacher and student discuss the language they might use to set some goals for this task and to explain strengths and weaknesses of those goals. How do you know if it is a good goal? As the student begins to establish goals that are task specific and for which progress can be monitored, discussion shifts to strategies for enacting goals.

2. Peers in a group take on different cognitive and metacognitive roles associated with self-regulated learning for an individual or collective task. Student 1 reminds students to stop and check how they are doing (monitoring and evaluating), Student 2 engages students in a discussion about the task parameters and purpose (task understanding), Student 3 engages students in a discussion about the task goal, and Student 4 asks students to share ideas and strategies for completing the task.

3. As a student and teacher collaborate to unpack the meaning of a task that was assigned in class and to construct an understanding of the task, the teacher’s own understanding of that same task begins to develop in sophistication so that the student and teacher have a shared language for talking about course tasks.


Hadwin et al. (2005) investigated changes in the ownership of regulatory behavior and phases of SRL in 10 graduate students over a year. The students were instructed to create a portfolio regarding their course and have four conferences with a teacher during the course. Discourse during the conferences was recorded, and the first and the last conferences were analyzed. The results showed a statistically significant decrease in teacher regulation and an increase in student regulation over time. The mean percentage of total coregulatory dialogue remained fairly stable from the first to last conference; however, there was a statistically significant decrease in discussions related to task understanding and an increase in discussions related to strategy execution from the first to the last conference. The study provided evidence of a shifting of control from the teacher directing student regulation of learning to the student appropriating self-regulatory control of his or her own learning. And, importantly, coregulatory dialogue, wherein the teacher prompted the student or the student prompted the teacher for assistance, supported the transition from teacher to self-regulation. In Hadwin et al.’s study, data about coregulation consisted primarily of teacher–student discourse.

Studies that adopt a sociocultural approach to the study of self-regulation (coregulation) tend to examine teacher–pupil interactions and teacher behaviors as a source of social learning systems (e.g., Flem, Moen, & Gudmundsdottir, 2004; Gallimore, Dalton, & Tharp, 1986). For example, Flem et al. conducted qualitative research in Norway on how a successful teacher in an inclusive classroom interacted with children with behavioral disorders. The study reported that the teacher provided scaffolding with her students to help them become self-regulated. She served as a good model, employed behavioral management techniques, gave feedback, and instructed students by questioning and providing cognitive structures in class. Further, Gallimore et al. investigated self-directed speech in learning new skills among 27 teachers. After training the teachers to use behavioral management techniques, modeling, and responsive questioning skills in class, the researchers interviewed them. Their qualitative analysis revealed that approximately half of the teachers spontaneously reported the use of self-directed speech, although more teachers might have used it. The researchers attributed this underreport to teachers’ negative image about self-talk, such as feelings of embarrassment. The self-directed speech was also reported to disappear as the skills became more automatic. The study concluded that when people learn new skills, regardless of their age, they go through other-regulation, self-regulation, and automatization.

In contrast, McCaslin and colleagues (e.g., McCaslin & Burross, 2010, this issue; McCaslin & Murdock, 1991) have examined coregulation interactions, tensions, and presses at a larger grain size as individual, classroom, parental, and cultural influences interact to afford and constrain learning and adaptation. By combining classroom observation and self-report data, McCaslin has examined the dynamic interplay among instructional opportunity, student activity, and teacher–student relationships (e.g., McCaslin et al., 2006; McCaslin & Burross). McCaslin and Burross identified four instructional opportunity factors—guided elaboration, direct instruction, review, and structured problem-solving—based on 710 ten-minute classroom observations. Student self-reports revealed five student activity factors: anxious and withdrawn, good worker, engaged learner, disengaged and distracting, and struggling and persistent.

Importantly, McCaslin and Burross (2010, this issue) used a range of analytical techniques with a targeted sensitivity to the interplay between classroom context and student activity. They examined the instructional opportunities present in classrooms, the ways in which students participated in and adapted to classroom demands, and how students performed on standardized tests. By systematically transitioning the focus of analysis between instructional influences and personal or individual influences, they examined relationships between social and personal. Findings indicated that students differentially adapt or respond to different classroom demands. In the study, children in Grades 3–5 from cultures and schools challenged by poverty, mobility, and low performance on standardized tests manifested positive adaptation to classroom tasks, particularly in the context of direct instruction.

Vygotskian approaches to self-regulation often focus on parent–child interaction and the role of private speech, as well as parent–child relationships as a social origin of SRL (e.g., Diaz et al.,1990; Winsler, Diaz, Atencio, McCarthy, & Chabay, 2000; Winsler, Diaz, McCarthy, Atencio, & Chabay, 1999). Diaz et al. reviewed studies of the relationships between parenting styles and characteristics of self-regulated behaviors. They discussed that authoritative parenting, which is marked by balanced combination of parental control and warmth, is related to children’s level of locus of control, autonomy, and private speech.

As part of a 3-year longitudinal study, Winsler et al. (1999) compared mother–child interaction, children’s private speech, and problem-solving performance of 18 preschool children at risk of behavior disorders with those of 22 typically developing preschoolers. First, children were asked to assemble puzzles individually and then with their mother. The children’s task performance and mother–child interactions were recorded and transcribed. Later, children’s speech during individual and collaborative sessions, maternal speech and help, and children’s attention were analyzed by a researcher. Children’s task performance and vocabulary level were also measured. Preliminary findings showed that the mothers of at-risk children displayed more adult regulation, more negative comments, less praise, and less withdrawal of physical control during collaborative puzzle-solving than those of comparison children. In terms of children’s use of private speech, at-risk children exhibited overt, task-related private speech more frequently than comparison group children, indicating that at-risk children had delayed internalization of private speech. Findings from this study supported the sociocultural perspective regarding the relations between parent–child interaction and children’s self-regulation. Further, the final report of the longitudinal study by Winsler et al. (2000) confirmed the trend that at-risk children displayed developmental delay in internalization of private speech.

Although studies that supported Diaz et al.’s (1990) Vygotskian paradigm largely focus on young children and use puzzle-solving as a task, Karasavvidis, Pieters, and Plomp (2000) examined teacher–student discourses in 3-hour geography tutorials in 10 Grade 10 students. Insisting that students’ utterances that display self-regulation are inseparable from the contexts in which they occur, they coded the data depending on whether students’ utterances followed teacher’s instructions and questions, or occurred independently. They found a significant shift from teacher regulation to student regulation during tutorial sessions. In this way, studies anchored in Vygotskian notions explore the impact of close personal interactions on children’s self-regulation skills as social aspects.

As illustrated in the range of studies described, research about coregulation focuses on interactions and transitions of power as the unit of analysis, rather than individual cognition, behavior, motivation, or metacognition. The individual is not absent from analysis, but neither is the social. From this perspective, understanding the appropriation of self-regulated learning means examining changes in distributed interaction and exchange. Not surprisingly, discourse analysis has played a central role in research about coregulation. Further, Salonen, Vauras, and Efklides (2005) suggested microgenetic designs as a possible methodology in investigating interpersonal interactions. This design captures recurring communicative patterns and moment-by-moment interactions and addresses the evolving nature of interpersonal relationships and relationships between individuals and their environments.



Socially shared-regulation refers to the processes by which multiple others regulate their collective activity. From this perspective, goals and standards are co-constructed, and the desired product is socially shared cognition. We focus on social as synergy among individuals. Jackson, et al. (2000) proposed that self-regulation is self-in-social-setting regulation in which individual actions are embedded in collective society. They argued that personal goals are inseparable from social goals and are achieved through social interaction. In essence, socially shared regulation is collective regulation in which the regulatory processes and products are shared.

From social constructionist perspectives of learning, two distinct categories emerge: (a) individual regulation targeted to the social good, and (b) collective regulation in which groups develop shared awareness of goals, progress, and tasks toward co-constructed regulatory processes, thereby regulating together as a collective processes.

Yowell and Smylie (1999) discussed the development of self-regulation from Bronfenbrenner’s (1994) ecological theory, drawing heavily from Dewey’s (1938/1963) and Vygotsky’s (1978) theories of development. Yowell and Smylie posited that many theories of self-regulation exclusively focus on individual persons’ processes. In contrast, they proposed that self-regulation is actually the product of relationships between and within interpersonal and environmental interactions, as well as individual processes. Although they defined self-regulation as a goal-directed process, they believe that the pursuit should be adaptable and enhance individual development and social change.

Yowell and Smylie (1999) structured the development of self-regulation in three levels: microsystem, mesosystem, and exosystem. In the microsystem, interpersonal interactions, such as student–teacher interactions, affect a person’s development. Interactions between a person and his or her immediate environment also influence and are influenced by the next level of environmental interactions in the mesosystem. At the microsystem level, self-regulation is developed through adult–child interactions. Scaffolding should be based on “intersubjectivity” (p. 474), or mutual understanding, between an adult and a child. Thus, in the development of self-regulation, “two experts and two novices” (p. 474) are involved; an adult may be an expert in learning strategies but a novice in the child’s world, and a child may be an expert in the children’s world but a novice in terms of academic skills. This shared understanding requires trusting and respecting relationships between the two, as opposed to asymmetrical authoritarian relationships.

In the mesosystem, multiple microsystems, such as friends and school, reciprocally interact, which simultaneously affects personal growth (Yowell & Smylie, 1999). For the development of self-regulation, adults must provide learners with a guided learning environment and tasks that connect them with other microsystems in personally and culturally meaningful ways. The development of self-regulation is embedded in this connection between academic concepts and daily concepts, which promotes personal and cultural growth. Thus, affording students volunteer opportunities in community settings contributes to individual and cultural development while enhancing self-regulation.

In the exosystem, which contains the person, the microsystem, and the mesosystem, sociocultural values and norms indirectly affect a person’s development. Yowell and Smylie (1999) claimed that these values and norms should be built on “social trust” (p. 483). Similar to interpersonal relationships, trusting relationships between individuals and society are fundamental in the pursuit of socially valued goals. As such, Yowell and Smylie view that self-regulation is embedded within and across multiple layers of nested social systems.

The second perspective that views social aspects of SRL as holistic interactions suggests communal regulation and has a greater focus on exosystem. Jackson et al. (2000) proposed that self-regulation is “self-in-social-setting regulation” (p. 276), in which individual actions are embedded in collective society. When people set goals and take actions toward them, their behaviors are not based on individual standards, but on socially accepted notions. Their behaviors are affected by the opinions, comments, and behaviors of other people within the same social network. They argued that personal goals are inseparable from social goals and are achieved through social interactions. Thus, people’s behaviors are influenced by communal regulation. Even when people self-regulate their affective modes, they also do so in a way that cultural standards allow them to do. People refer to other members in the network for guidance and confirmation of appropriate behavioral and emotional expressions in the cultural context where they are. In this way, Jackson et al.’s notion of communal regulation recognizes how cultural context plays a central part in the development of self-regulation.


Studies about socially shared regulation examine individual regulatory processes as part of socially constructed knowledge. The research often occurs in technology-based learning environments where social exchange and co-construction can be more easily traced (e.g., Hurme & Järvelä, 2005; Järvelä, Lehtinen, & Salonen, 2000; Leinonen, Järvelä, & Lipponen, 2003; Iiskala, Vauras, & Lehtinen, 2004; Salovaara & Järvelä, 2003; Vauras, Iiskala, Kajamies, Kinnunen, & Lehtinen, 2003).

For example, a study by Leinonen et al. (2003) explored individuals’ contributions to socially constructed negotiation in a computer-supported collaborative learning environment (Knowledge Forum). The study investigated 20 ninth graders’ activities in the Knowledge Forum and their awareness of the co-construction of knowledge. The data were collected from students’ notes in the networked communication tool and a questionnaire that targeted students’ purpose and perspectives on their participation in the networked discussion. The qualitative analyses identified four different types of knowledge co-construction: (a) an active co-construction, (b) a nonactive construction, (c) a comment receiver, and (d) an isolate. An active co-constructor wrote notes and comments to others the most frequently and actively collaborated with others. A nonactive constructor wrote notes and comments the least frequently, while accessing others’ notes. A comment receiver played a central role in the knowledge co-construction. An isolate received very few comments from others and no reciprocal interactions. Further, students’ perception of participation to the knowledge co-construction was reported to parallel their activities in the computer tool. Research such as this examines the roles that individuals play (and contribute) in a social community. In essence, members of a community take on roles that assist the community in monitoring and regulating the building of a collaborative knowledge and products. In this research, the unit of analysis is an individual in a social setting, such that data about interaction and exchange are central to determining the role of any one individual in that social system.

In addition to the studies on individual regulation for socially shared goals, socially shared regulation is examined through studies on group regulation in which the group members codevelop collective regulatory process. For example, Hurme and Järvelä (2005) explored metacognitive processes in solving mathematical problems using the Knowledge Forum for sixteen 13-year-old students. The students worked in pairs and were instructed to use the Knowledge Forum to exchange their thinking and problem-solving processes with other pairs in class. The data were collected from the pairs’ notes in the networked discussion tool. The overall content analyses demonstrated that networked discussion of mathematical knowledge and questions occurred, and trivial notes, which consisted of discussions unrelated to problem-solving tasks, such as complimenting someone’s diagram, were also present. The analysis of the problem-solving procedures illustrated that the students shared their understandings of mathematical concepts, procedures for problem-solving, and regulation of the problem-solving process. Further, in exploring if the students could articulate their metacognitive processes, the qualitative analysis showed that the pairs discussed metacognitive knowledge (e.g., procedural knowledge like how to solve a problem in another way, task knowledge like how to interpret the task presented), as well as metacognitive judgment and monitoring (e.g., querying solving procedures and correctness of the answer to the other pairs).

Similarly, Iiskala et al. (2004) examined how metacognition is manifested in social interaction during collaborative solving of mathematical word problems. Their analysis specifically targeted social-level awareness, monitoring, and regulatory processes during paired problem-solving. Data were videotaped and transcribed to capture both verbal and nonverbal communication. From this perspective, metacognition can only be understood in the course of joint problem-solving. In collaborative work, self-regulatory processes and metacognition operate at an interindividual level involving awareness, monitoring, and regulation. Iiskala et al. referred to this process as socially shared metacognition.

Vauras et al. (2003) conducted a case study of a high-achieving pair of 10-year-old girls on how shared-regulation and motivation occurred in solving mathematical problems. The pair’s interactions during mathematical problem-solving presented as a computer game were videotaped over 16 sessions, and the pairs were interviewed to explore their experiences of collaborative problem-solving. The study reported that as soon as the girls started the game, they demonstrated self-regulated mathematical problem-solving; however, the regulation was a “reciprocal regulation” (p. 27) in which the pair took turns in regulatory activities. The analysis also showed that the girls were open to negotiating their thinking, identifying this aspect as a salient feature in successful collaborative learning. Further, the interview revealed that the girls enjoyed the collaborative process and appreciated each other’s competency.

Research about socially shared regulation focuses on collective interactions and collaboration as the unit of analysis, rather than individual cognition or transfer of knowledge through interpersonal interactions. The individual is present in the analysis as part of a collective entity. Thus, socially shared regulation is examined in a group of shared regulators, and individual regulation is always studied in relation to others and to the group regulation. From this view, socially shared regulated learning means examining collective processes within group’s interactions and negotiations of meaning. Similar to research about coregulation, analyzing the process of interpersonal negotiations is the main methodology. Accordingly, discourse analysis is used in the investigation, and computer-supported collaborative learning environments have become commonplace for facilitating the co-construction of knowledge and examining processes associated with shared regulation. Further, Aguilera (2001) proposed the use of the Procedure for the Assessment of Thinking in Interaction (PAT). The PAT was developed based on the notion that “thinking is cultural and is construed in interaction” (p. 285), aiming at analyzing thinking processes and abilities in solving social problems with others.


The purpose of this article has been to introduce a variety of ways that social aspects of self-regulated learning have informed theory and research. By contrasting perspectives of social, we have introduced a language for describing very different bodies of work that strive to consider roles of individual and social contexts in the regulation of learning. Table 1 summarizes each of three main perspectives described in this article: self-regulated learning, coregulated learning, and shared regulation of learning.

Table 1. Comparison of Different Perspectives of Social and Self-Regulated Learning


Self-regulated learning

Coregulated learning

Socially shared regulation


The process of becoming a strategic learner by actively monitoring and regulating metacognitive, motivational, and behavioral aspects of one’s own learning.

Transitional processes in a learner’s acquisition of SRL, during which members of a community share a common problem-solving plane, and SRL is gradually appropriated in response to and directed toward social and cultural contexts.

Processes by which multiple others regulate their collective activity. From this perspective, goals and standards are co-constructed, and the desired product is socially shared cognition.

Focus of data collected and analyzed

Individual focus on dependent variables



-strategies and skills

-metacognitive awareness

-self-reported behavior

Social focus on instructional context and sometimes manipulated as independent variable

Discourse or dynamic interaction

Interplay among individual, classroom, parental, and cultural influences

Discourse and dynamic exchange

Individual roles and contributions but always in the context of others

Evolution of idea units and regulatory activities

Data collected


Performance measures

Mental models

Interview data


Think-aloud protocols


Frequency and content of interactions

Observations of shared behaviors and sociocultural dynamics


Observed interaction (verbal and nonverbal)

Individual roles and contributions to group

Group products

Analytical techniques

Correlation of individual factors/measures

Content analysis

Comparative methods (e.g., case study, ANOVA, etc.)

Discourse analysis

Content analysis

Correlational analyses Class-level factors/measures

Discourse analysis

Network analysis

Self-regulated learning refers to the process of becoming a strategic learner by actively monitoring and regulating metacognitive, motivational, and behavioral aspects of one’s own learning. For the most part, the focus of this body of research has been on the individual, with consideration of the social context being either examined separately or manipulated as an independent variable. Work in this area has relied heavily on self-reports, think-aloud protocols, interviews, and various measures of resulting knowledge and understanding. Research strives to examine the processes and products of individual aspects of self-regulated learning under specific social and contextual conditions. Social processes that are emphasized include feedback, learner control of task and challenge, modeling, and different levels of scaffolded support.

Coregulated learning refers to the transitional process in a learner’s acquisition of SRL, during which experts and learners share a common problem-solving plane, and SRL is gradually appropriated by the individual learner as part of interactions. The focus of research in this area has been on aspects of interaction, speech, and discourse, often centering on issues of scaffolding and interdependence. Focus has also been given to the dynamic interplay among personal, social, and cultural influences, what McCaslin (2009) referred to as the press and tension among potential (personal), practicable (social such as classroom opportunities), and probable (cultural norms and challenges). As a result, data primarily consist of either traces of interaction in the form of discourse and language, or relationships among influences such as activities, engagement, regulations, and structures. Research about coregulated learning strives to examine the ways in which social practices interact with individual engagement and regulatory processes. Social support in the form of scaffolding tends to take on some of the self-regulatory processes or burdens rather than merely instructing or prompting students to engage in those processes.

Shared regulation of learning refers to the processes by which multiple others regulate their collective activity. From this perspective, goals and standards are co-constructed, and the desired product is socially shared cognition. We considered dividing shared regulation into two specific categories: (a) individual regulation for the social good, and (b) regulating as a collective entity. Similar to coregulated learning, discourse and traces of interaction are primary sources of data in the study of shared regulation. However, unlike research about coregulation, research about shared regulation tends to examine contributions, roles, the evolution of ideas, and the ways in which groups collectively set goals and monitor, evaluate, and regulate their shared social space. Examining socially shared regulation requires a shift toward new forms of instructional tools, data collection, and data analysis that acknowledge individuals as part of social entities and shared tasks. For example, Carroll, Neale, Isenhour, Rosson, and McCrickard (2003) are among a select group of researchers beginning to experiment with tools specifically designed to support students in synchronizing task-oriented collaborative activity through notification and awareness tools. We propose that these tools provide enormous potential for (a) helping students define tasks and collaborative goals, as well as monitor and regulate collective task completion, and (b) collecting data about the ways in which students monitor and regulate activities in these collaborative spaces to prevent them from derailing or being supported by only one group member.

Bringing social interaction and exchange into the foreground of data analysis means adopting and enhancing analytical techniques such as network analysis. Such techniques afford opportunities to examine not only the frequency of interactions but also the ways in which idea units travel among group members to result in co-constructed representations (cf. Iiskala et al., 2004).

Importantly, a goal of this article has not been to espouse one perspective over another but to acknowledge the important contribution that each line of inquiry makes to our understanding of self and social aspects in the regulation of learning. In describing each perspective, we have tried to clarify a language for acknowledging these different perspectives and the weight they give to social and individual aspects of learning at theoretical and empirical levels. We concur with Boekaerts and Corno (2005) that even within a construct such as self-regulated learning, there are no simple or uniform definitions. Every model of self-regulated learning emphasizes a different aspect of self-regulation, from volition to motivation to cognition (Pintrich, 2000). Including coregulated and socially shared regulated learning merely acknowledges that different aspects of self-regulation stretch beyond the individual and into the social realm.

By acknowledging the range of perspectives and empirical approaches in the study of self and social in the regulation of learning, we hope to initiate a dialogue about new ways of researching SRL. Specifically, we hope to provide a frame for considering multimethodological approaches to the study of self-regulated learning in which shifting definitions implies changing measurement (Boekaerts & Corno, 2005). In her dissertation, Hadwin (2000) demonstrated that by studying the same complex learning context from these different theoretical and methodological perspectives, we can significantly enhance our understanding of learners as social beings who monitor and regulate learning across a range of social levels. Since that time, research about self-regulated learning, coregulated learning, and socially shared regulated learning has flourished. We propose two challenges for future research in this area: (1) refine and develop analytical techniques for examining shared regulation in a social space, and (2) experiment with research designs and methodologies that allow the unit of analysis to shift along a continuum from individual, acknowledging the different ways that self and social inform the regulation of learning.


Aguilera, A. (2001). Shared thinking: Concept and assessment. European Journal of Psychology of Education, 16, 283–298.

Azevedo, R., Cromley, J. G., & Seibert, D. (2004). Does adaptive scaffolding facilitate students’ ability to regulate their learning with hypermedia? Contemporary Educational Psychology, 29, 344–370.

Boekaerts, M., & Corno, L. (2005). Self-regulation in the classroom: A perspective on assessment and intervention. Applied Psychology: An International Review, 54, 199–231.

Brown, A. L., Ash, D., Rutherford, M., Nakagawa, K., Gordon, A., & Campione, J. C. (1993). Distributed expertise in the classroom. In G. Salomon (Ed.), Distributed cognitions: Psychological and educational considerations (pp. 188–228). New York: Cambridge University Press.

Brown, A. L., & Campione, J. C. (1996). Psychological theory and design of innovative learning environments: On procedures, principles, and systems. In L. Schauble & R. Glaser (Eds.), Innovations in learning: New environments for education (pp. 289–325). Mahwah, NJ: Erlbaum.

Bronfenbrenner, U. (1994). Ecological models of human development. In T. Husen & T. N. Postlethwaite (Eds.), International encyclopedia of education (pp. 1643–1647). Oxford, England: Pergamon.

Bruner, J. (1996). The culture of education. Cambridge, MA: Harvard University Press.

Bruning, R. H., Schraw, G. J., & Ronning, R. R. (1999). Cognitive psychology and instruction. Englewood Cliffs, NJ: Prentice-Hall.

Carroll, J. M., Neale, D. C., Isenhour, P. L., Rosson, M. B., & McCrickard, D. S. (2003). Notification and awareness: Synchronizing task-oriented collaborative activity. International Journal of Human-Computer Studies, 58, 605–632.

Cleary, T. J., & Zimmerman, B. J. (2004) Self-regulation empowerment program: A school-based program to enhance self-regulated and self-motivated cycles of student learning. Psychology in the School, 41, 537–550.

Corno, L., & Mandinach, E. B. (2004). What we have learned about student engagement in the past twenty years. In D. M. McInerney & S. Van Etten (Eds.), Big theories revisited: Vol. 4. Research on sociocultural influences on motivation and learning (pp. 299–328). Greenwich, CT: Information Age.

Detterman, D. K., Sternberg, R. J. (1996). Transfer on trial: Intelligence, cognition, and instruction. Norwood, NJ: Ablex.

Dewey, J. (1963). Experience and education. New York: Collier Macmillan. (Original work published 1938)

Diaz, R. M., Neal, C. J., & Amaya-Williams, M. (1990). The social origins of self-regulation. In L. C. Moll (Ed.), Vygotsky and education: Instructional implications and applications of sociohistorical psychology (pp. 127–154). New York: Cambridge University Press.

Driver, R., Asoko, H., Leach, J., Mortimer, E., & Scott, P. (1994). Constructing scientific knowledge in the classroom. Educational Researcher, 23(7), 5–12.

Flem, A., Moen, T., & Gudmundsdottir, G. (2004). Towards inclusive schools: A study of inclusive education in practice. European Journal of Special Needs Education, 19, 85–98.

Gallimore, R., Dalton, S., & Tharp, R. G. (1986). Self-regulation and interactive teaching: The effects of teaching conditions on teachers’ cognitive activity. Elementary School Journal, 86, 613–631.

Gallimore, R., & Tharp, R. (1990). Teaching mind in society: Teaching, schooling, and literate discourse. In L. C. Moll (Ed.), Vygotsky and education: Instructional implications and applications of sociohistorical psychology (pp. 175–205). New York: Cambridge University Press.

Hadwin, A. F. (2000). Building a case for self-regulating as a socially constructed phenomenon. Unpublished doctoral dissertation, Simon Fraser University, Burnaby, British Columbia, Canada.

Hadwin, A. F., Wozney, L., & Pontin, O. (2005). Scaffolding the appropriation of self-regulatory activity: A social constructivist analysis of changes in student-teacher discourse about a graduate student portfolio. Special Issue of Instructional Science, 33, 413–450.

Hurme, T., & Järvelä, S. (2005). Students’ activity in computer-supported collaborative problem solving in mathematics. International Journal of Computers for Mathematical Learning, 10(1), 49–73.

Jackson, T., Mackenzie, J., & Hobfoll S. E. (2000). Communal aspects of self-regulation. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 275–300). San Diego, CA: Academic Press.

Järvelä, S., Lehtinen, E., & Salonen, P. (2000). Socio-emotional orientation as a mediating variable in the teaching-learning interaction: Implications for instructional design. Scandinavian Journal of Educational Research, 44, 293–306.

Karasavvidis, I., Pieters, J. M., & Plomp, T. (2000). Investigating how secondary school students learn to solve correlational problems: Quantitative and qualitative discourse approaches to the development of self-regulation. Learning and Instruction, 10, 267–292.

Kitsantas, A., Zimmerman, B. J., & Cleary, T. (2000). The role of observation and emulation in the development of athletic self-regulation. Journal of Educational Psychology, 92, 811–817.

Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. New York: Cambridge University Press.

Leinonen, P., Järvelä, S., & Lipponen, L. (2003). Individual students’ interpretations of their contribution to the computer-mediated discussions. Journal of Interactive Learning Research, 14, 99–122.

Iiskala, T., Vauras, M., & Lehtinen, E. (2004). Socially-shared metacognition in peer learning? Hellenic Journal of Psychology, 1(2), 147–178.

Martin, J., & Sugarman, J. (1996). Bridging social constructionism and cognitive constructivism: A psychology of human possibility and constraint. Journal of Mind and Behavior, 17, 291–320.

McCaslin, M. (2009). Co-Regulation of student motivation and emergent identity. Educational Psychologist, 44, 137–146.

McCaslin, M., & Burross, H. L. (2010). Research on individual differences within a sociocultural perspective: Coregulation and adaptive learning. Teachers College Record, 113(6).

McCaslin, M., Good, T. L., Nichols, S., Zhang, J., Wiley, C. R. H., Bozack, A. R., et al. (2006). Comprehensive school reform: An observational study of teaching in Grades 3 to 5. Elementary School Journal, 106, 313–331.

McCaslin, M., & Hickey, D. T. (2001). Self-regulated learning and academic achievement: A Vygotskian view. In B. Zimmerman & D. Schunk (Eds.), Self-regulated learning and academic achievement: Theory, research, and practice (2nd ed., pp. 227–252). Mahwah, NJ: Erlbaum.

McCaslin, M. M., & Murdock, T. B. (1991). The emergent interaction of home and school in the development of students’ adaptive learning. In M. Maehr & P. Pintrich (Eds.), Advances in motivation

and achievement (Vol. 7, pp. 213–260). Greenwich, CT: JAI Press.

McCombs, B. L., & Marzano, R. J. (1990). Putting the self in self-regulated learning: The self as agent in integrating will and skill. Educational Psychologist, 25, 51–69.

Meyer, D. K., & Turner, J. C. (2002). Using instructional discourse analysis to study the scaffolding of student self-regulation. Educational Psychologist, 37, 17–25.

Patrick, H. (1997). Social self-regulation: Exploring the relations between children’s social relationships, academic self-regulation, and school performance. Educational Psychologist, 32, 209–220.

Perry, N. E. (1998). Young children’s self-regulated learning and contexts that support it. Journal of Educational Psychology, 90, 715–729.

Phillips, D. C. (1995). The good, the bad, and the ugly: The many faces of constructivism. Educational Researcher, 24(7), 5–12.

Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation: Theory, research and applications (pp. 451–502). San Diego, CA: Academic Press.

Prawat, R. S. (1996). Constructivisms, modern and postmodern. Educational Psychologist, 31, 215–225.

Purdie, N., & Hattie, J. (1996). Cultural differences in the use of strategies for self-regulated learning. American Educational Research Journal, 33, 845–871.

Salomon, G. (1993). No distribution without individual’s cognition. In G. Salomon (Ed.), Distributed cognitions: Psychological and educational considerations (pp. 111–138). New York: Cambridge University Press.

Salonen, P., Vauras, M., & Efklides, A. (2005). Social interaction: What can it tell us about metacognition and coregulation in learning? European Psychologist, 10, 199–208.

Salovaara, H., & Järvelä, S. (2003). Students’ strategic actions in computer-supported collaborative learning. Learning Environments Research, 6, 267–285.

Sanz de Acedo Lizarraga, M. L., Ugarte, M. D., Cardelle-Elawar, M., Iriarte, M. D., & Sanz de Acedo Baquedano, M. T. (2003). Enhancement of self-regulation, assertiveness, and empathy. Learning and Instruction, 13, 423–439.

Schunk, D. H. (1990). Goal setting and self-efficacy during self-regulated learning. Educational Psychologist, 25, 71–86.

Schunk, D. H. (1994). Self-regulation of self-efficacy and attributions in academic settings. In D. H. Schunk & B. J. Zimmerman (Eds.), Self-regulation of learning and performance: Issues and educational implications (pp. 75–100). Hillsdale, NJ: Erlbaum.

Schunk, D. H. (1998). Teaching elementary students to self-regulate practice of mathematical skills with modeling. In D. H. Schunk & B. J. Zimmerman (Eds.), Self-regulated learning: From teaching to self-reflective practice (pp. 137–159). New York: Guilford Press.

Schunk, D. H. (2001). Social cognitive theory and self-regulated learning. In B. J. Zimmerman & D. H. Schunk (Eds.), Self-regulated learning and academic achievement: Theoretical perspectives (pp. 125–152). Mahwah, NJ: Erlbaum.

Schunk, D. H., & Zimmerman, B. J. (1997). Social origins of self-regulatory competence. Educational Psychologist, 32, 195–208.

Vauras, M., Iiskala, T., Kajamies, A., Kinnunen, R., & Lehtinen, E. (2003). Shared-regulation and motivation of collaborating peers: A case analysis. Psychologia: An International Journal of Psychology in the Orient, 46(1), 19–37.

Vygotsky, L. S. (1978). Mind in society. Cambridge, MA: Harvard University Press.

Wertsch, J., & Stone, C. (1985). The concept of internalization in Vygotsky’s account of the genesis of higher mental functions. In J. Wertsch (Ed.), Culture, communication, and cognition: Vygotskian perspectives (pp. 162–182). New York: Cambridge University Press.

Winne, P. H. (1997). Experimenting to bootstrap self-regulated learning. Journal of Educational Psychology, 89, 379–410.

Winsler, A., Diaz, R. M., Atencio, D. J., McCarthy, E. M., & Chabay, L. A. (2000). Verbal self-regulation over time in preschool children at risk for attention and behavior problems. Journal of Child Psychology and Psychiatry, 41, 875–886.

Winsler, A., Diaz, R. M., McCarthy, E. M., Atencio, D. J., & Chabay, L. A. (1999). Mother-child interaction, private speech, and task performance in preschool children with behavior problems. Journal of Child Psychology and Psychiatry, 40, 891–904.

Yowell, C. M., & Smylie, M. A. (1999). Self-regulation in democratic communities. Elementary School Journal, 99, 469–490.

Zimmerman, B J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81, 329–339.

Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational Psychologist, 25, 3–17.

Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation: Theory, research and applications (pp. 13–39). San Diego, CA: Academic Press.

Zimmerman, B. J. (2002). Achieving self-regulation. In F. Pajares & T. Urdan (Eds.), Academic motivation of adolescents (pp. 1–28). Greenwich, CT: Information Age.

Cite This Article as: Teachers College Record Volume 113 Number 2, 2011, p. 240-264
https://www.tcrecord.org ID Number: 15976, Date Accessed: 10/27/2021 9:56:12 AM

Purchase Reprint Rights for this article or review
Article Tools

Related Media

Related Articles

Related Discussion
Post a Comment | Read All

About the Author
  • Allyson Hadwin
    University of Victoria
    E-mail Author
    ALLYSON HADWIN is an associate professor at the University of Victoria and is a codirector of the Technology Integration and Evaluation (TIE) research lab. Her research focuses on the social aspects of self-regulated learning, as well as the ways that technologies can support self-regulation, shared regulation, and coregulation. Dr. Hadwin uses multiple methodologies to explore the dynamic and social nature of self-regulation as it evolves over time and through interaction with others.
  • Mika Oshige
    University of Victoria
    MIKA OSHIGE is an M.A. candidate at the University of Victoria. Her M.A. thesis study explores the first phase of self-regulated learning (task understanding) with relation to academic performance in postsecondary students.
Member Center
In Print
This Month's Issue