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What Have We Learned About the Social Context-Student Engagement Link?

by Monique Boekaerts - 2011

The author explores how each author contributes to our understanding of the social context--self-regulation link. She also describes how the articles collectively enhance our insights into the social embeddedness of regulation strategies in the classroom and lists some of the challenges that remain.

The special issue, edited by Allyson Hadwin and Sanna Järvelä, needs to be applauded. They asked several research groups from Europe and North America to explore the link between social aspects of learning and self-regulation. In fact, these different research groups contributed to several symposia, organized by Hadwin and Järvelä, at conferences of the American Educational Research Association (AERA) and the European Association of Learning and Instruction (EARLI). The main question that they raised was: What does “social” imply in theories of self-regulation, and what do we know about the effect of social aspects of the learning environment on students’ strategy use? It is important to realize in this respect that researchers have accrued a great deal of information about the effect of social factors on self-regulation in individual learning situations but that we know next to nothing about the effects of social factors on strategy use in collaborative learning. These are still uncharted waters, and we urgently need studies that explore how various aspects of the social context interact with and jointly affect students’ motivation to engage in collaborative learning situations as well as their strategy use.

The special issue consists of five papers that collectively attempt to clarify the constructs that are commonly used to describe the social aspects of self-regulation. I will begin the discussion with a brief summary of the theoretical approaches and the empirical results reported by the five research groups. I will explore how each article contributes to our understanding of the social context–self-regulation link. I will also describe how the articles collectively enhance our insights into the social embeddeness of regulation strategies in the classroom. Finally, I will address some of the challenges faced by researcher who wants to travel these uncharted waters, focusing mainly on issues that are stimulated by the five articles in this special issue, but I will also diverge to issues that go beyond the ideas expressed by the five research groups.


Table 1 describes the five different studies, highlighting the main constructs that each study brought to the foreground, the types of self-regulatory strategies that were studied, and how each research group conceptualized the social context. In addition, some information is provided on the students who took part in the respective studies, the methodology of the respective studies, and the most important insights that were gained. As can be viewed in Table 1, all research groups want to understand the quality of the students’ strategy use in real-life learning situations. They all acknowledge that students’ involvement and participation is socially situated and that agents who are actually or virtually present during the learning process affect student learning either favorably or unfavorably. Where the different research groups diverge is in the way they have conceptualized the social context and in the effects that they assume various aspects of the social context have on student engagement and strategy use.

Table 1. An Overview of the Special Issue


Age Groups

Main Constructs

Conceptualization of Social Context

Strategies Studied

Data Collection & Analysis Techniques

Major Insights

Hadwin and Oshige

Review of the literature

* Specific social and contextual conditions foster SRL.

* Coregulation: Social support in the form of scaffolding.

* How do groups organize the shared social space?

Great diversity in where social aspects of the LE are positioned in the models of SRL, from a peripheral contextual input for individual SRL to a socially shared process.

Coregulation: Shared monitoring and control.

* How do students respond to social support?

Shared regulation:

* How do students regulate for the social good?

* How do they regulate as a collective activity?

Primary data:

* Content and discourse analysis

* Traces of interaction

* Network analysis

Introduced a language for discussing three main forms of regulation.

Kaplan, Lichtinger, & Margulis

High school (Ninth grade)

N = 211

Goal orientation: Students’ purpose for engagement. It is dynamically constructed in the learning situation and influences strategy use.

Focus is on different affordances of the social context.

* Types of tasks.

* Perceived instructional practices in traditional vs. authentic environments.

*Regulating attention

*Monitoring content

*Eliciting context

*Planning ahead

*Success and value encouragement



* Writing an essay

* Self-report questionnaire

* Smallest space analysis

Purpose for writing and selection of strategies closely intertwined in good writers but not in poor writers.


Review of his own studies;

college students & high school students

Regulation of motivation is a process through which students influence, control, and manage the level and nature of motivation (engagement and willingness to work hard).

* Focus on how students deal with different obstacles in the LE.

* Four interdependent forms of social influence are critical for the development of metamotivation: direct instruction, modeling, scaffolding, and sociocultural processes.

Strategies enacted to increase, sustain, and modify one’s level of motivation, for example:

*Interest enhancement

*Social reinforcement

*Task restructuring


*Raising self-efficacy

*Dealing with distracters

* Scenarios of different instructional contexts

* Qualitative analysis of students’ encountered verbal reports on obstacles and the motivational regulation strategies they used

* Students who had more adaptive motivational beliefs tended to report greater use of regulation of motivation strategies.

* Students adapted their motivation regulation strategies to the context.

*The strategies influenced the behavior of others present in the LE.

Järvelä & Järvenoja

College students

N = 16

Socially constructed regulation of motivation during CL in relation to situation-specific social challenges.

Focus is on teamwork: How are specific social challenges related to the socially constructed regulation of motivation?

Strategies enacted to increase, sustain, and modify the level of group motivation:

*Task structuring

*Social reinforcing

*Efficacy management

*Interest enhancement

*Socially shared goal-oriented talk


* Three consecutive group assignments

* Questionnaire social challenges

* Videotapes

* Group interviews

*Discourse analysis

* Teamwork is the main challenge. It is combined with other challenges during different stages of the group project.

* Students co-construct different regulation-of-motivation strategies in different stages of the project.

McCaslin & Burross

Primary school students in Grades 3–5

N = 439

The way students participate in the classroom informs on their emergent identity as the learners they are and want to be.

Teachers coregulate students’ participation and engagement.

Focus is on different instructional opportunities that the teacher creates to coregulate students’ learning.

Students’ self-monitoring report:

* Type of engagement

* Somatic complaints

*Observation of instructional opportunities

*Self-monitoring reports

*Correlations and principal component analysis

Students’ self-monitoring reports inform on their engagement in the classroom.

Linking instructional profiles (coregulation) with students’ self-monitoring reports inform on how and why students adapt to instructional demands.

CL = collaborative learning. LE = learning environment. SRL = self-regulated learning.


Table 1 shows that Hadwin and Oshige (2011) gave an overview of the literature on the effect of social aspects on self-regulation. They explored the critical role that individual and social factors play in diverse self-regulation models. They put the studies that investigated the social nature of learning along a continuum ranging from studies in which the individual student was the unit of analysis to studies in which the focus was on socially shared regulation. Hadwin and Oshige argued that it is crucial to distinguish between situations in which students themselves are supposed to be the sole director of the learning process, and situations in which the learning process is externally regulated or coregulated. Moreover, these two situations should be set apart from situations in which the management of the learning process is deliberately and purposely constructed jointly with other learners.

When individual students are supposed to be the sole regulators of their own learning process, they are expected to be active agents themselves who strive to monitor and take control of their learning, while being influenced by their own thoughts and feelings and by what other persons want them to do. Such an expectation is often based on the erroneous assumption that most students have access to the necessary and sufficient declarative, procedural, and conditional knowledge to be metacognitively aware of what needs to be done and have access to adequate strategies to steer and direct their learning. Numerous researchers pointed out that in the initial stages of the learning process, students still need the teacher or a more advanced peer to help with the regulation of the cognitive, motivational, and behavioral aspects of the learning process (see also Alexander, 2003; Boekaerts, 1997).

Hadwin and Oshige described the coregulation process as the temporary sharing or distributing of self-regulatory processes and thinking between a learner and a more capable other (peer, parent, or teacher) while the learner is in transit toward becoming a self-regulator in that domain. They emphasized that during the period of transit, self-regulatory processes need to be scaffolded—rather than scaffolding content knowledge per se. At that stage, learners begin to appropriate relevant scripts and practices and become aware of how they can support their own learning process.

This article informed the discussion on the importance of making a clear distinction between the sharing involved in coregulation and in collaborative learning. When working in a small group on a collaborative task, the individual student’s feelings, cognitions, and actions are embedded in a communal learning process. Multiple learners regulate their own learning process as well as the collective activity. This involves a multitude of actions, such as co-constructing the goals and standards in order to create synergy, developing collective metacognitive awareness and shared understanding of the problem at hand, collective monitoring, and jointly regulating and evaluating the shared social space. Hadwin and Oshige argued that these simultaneously occurring processes can best be investigated in technology-based learning environments, where social exchange and co-construction can be more easily traced. Content and discourse analyses as well as descriptions of the traces that students leave behind when they work together on a problem are the primary data that need to be explored to understand how individual students profit from or are impeded by the shared regulation process.

Hadwin and Oshige’s article is a well-informed introduction to the special issue. It provides the reader with an advance organizer, setting the scene for reflections on the complex learning and regulation patterns that emerge when students are invited to work collaboratively on a complex task. A major insight that these researchers brought is that communal aspects of learning need to be studied more intensively because these aspects may be a key to further advances in examining self-regulation and coregulation processes. I will come back to this issue after discussing the four other papers.


As can be viewed from Table 1, Kaplan, Lichtinger, and Margulis (2011) explored the relation between a traditional motivation measure (goal orientation) and students’ strategy use in relation to a concrete writing assignment. Kaplan et al. asked high school students (ninth grade) to write an essay about the topic “What is true friendship?” and to complete a posttask questionnaire about their goal orientation, self-efficacy for the writing task, and the strategies they used to self-regulate the task. He argued that different goal orientations create different purposes for engagement and that this calls for the selection of different strategies. Kaplan et al. also predicted that the perceived purpose for engagement depends on the perceived affordances of the educational context (i.e., the types of tasks the students have to do and the perceived instructional practices). His main message is that students’ purpose for engagement is dynamically and purposefully constructed in the learning situation; it is based on three components, namely perceived relevance of the learning situation, relevant self-perceptions, and perceived action possibilities for engagement.

Kaplan et al. used smallest space analyses to study the hypothesized integration of motivation variables (goal orientation) and strategy use. He found that writing strategies may or may not be located within regions that also include goal orientation (mastery or performance orientation). He considered closeness in space as evidence that the students construed the writing strategies as an integral part of their purpose for engagement. Kaplan et al. investigated whether there were differences in the purpose and strategy construal processes between students with different ability levels and students who had studied in two different learning environments, namely a traditional versus an authentic learning environment. He made four groups by crossing writing ability level with type of school. In both schools, low achievers seemed to have construed their strategies separately from their purpose of engagement, this in contrast to high achievers. Kaplan concluded that students who are capable of good writing are more likely to perceive strategies that are effective for writing as part of the purpose of engaging in a writing activity.

What have we learned from this study? Even though it is necessary to set up additional studies to replicate the data, I would like to conclude that the analysis technique that Kaplan et al. used in their study reveals a great deal more about the relation between motivation variables and the (self) regulation strategies that the students actually use to steer and direct the writing process than more traditional methods have exposed in the past. Smallest space analysis can shed bright light on very important issues, such as whether purpose for engagement and selecting relevant learning or motivation regulation strategies are integrated or separate processes in different groups of learners for different school subjects.


As can be seen in Table 1, Wolters (2011) defined student engagement in terms of the activities that students undertake to regulate their motivation. He reviewed the literature on the regulation of motivation within an academic context and pointed out that this construct has not been part of most models of self-regulated learning, mainly because most models primarily focused on the cognitive aspects of the self-regulation process. Wolters argued that students build up motivational beliefs in relation to a domain and use this knowledge to orient themselves on new learning tasks. Stored motivational beliefs are critical for the learning process because they influence the way students think about the current learning task, thus affecting the choices they make with respect to the learning strategies they will use, including monitoring and control processes strategies that are part of metacognition. Drawing on the extensive literature on metacognition, he identified three facets of metamotivation that correspond with the three facets of metacognition: (1) metalevel knowledge or understanding necessary to regulate motivation (e.g., beliefs about particular activities and whether they are boring or motivating), (2) monitoring one’s level or state of motivation (students’ ability to observe and gather information on their own motivation), and (3) active efforts to intervene and control one’s own motivation (motivation regulation strategies that are part of task engagement).

Wolters’s main contribution is that he identified different types of strategies that students use to put their motivational beliefs to use. His studies show that students adapt their regulatory strategies in function of the context in which they learn. Wolters also described four interdependent forms of social influence that are critical for the development of metalevel knowledge about the regulation of motivation, namely direct instruction, modeling, scaffolding, and sociocultural processes. He cautioned that the effect of these social influences on students’ self-regulation is not unidirectional. The motivation regulation strategies that students use in the classroom modify the context, influencing the behavior of others present in that environment.

An asset of this study is that Wolters developed a language and a tool for describing the self-regulatory strategies that students use to deal with distracters, difficulty, boredom, low interest, and other aspects of the learning environment that require them to take actions to increase or sustain their level of motivation or to find alternative ways to motivate themselves. This tool permits researchers to describe accurately what students with different ability levels and students who study in diverse learning environments do to sustain their motivation in order to actively engage and participate in learning activities.


As can be viewed from Table 1, Järvelä and Järvenoja (2011) also studied the regulation of motivation, but they went one step further than Wolters (2011): They explicitly focused on the interaction between social challenges and the way in which group members regulate the group’s level of motivation. In line with Hadwin and Oshige’s (2011) theorizing, Järvelä and Järvenoja argued that in a collaborative learning context, an individual’s engagement and participation in the learning process is coregulated by the other students in the group. This implies that an individual student’s engagement level, and the motivational beliefs on which it is based, are continually shaped and reshaped as the collaborative learning process unfolds.

Järvelä and Järvenoja investigated how college students jointly regulate their motivation while working collaboratively. They argued that in socially organized learning situations, such as cooperatively working on a math assignment, students need to be skilled in perspective taking. This means that they share and compare knowledge, make an action plan that they all agree on, monitor the group’s progress, and reflect jointly on the different solution steps as well as on the final solution.

Taking the regulation of motivation strategies described by Wolters as a starting point, Järvelä and Järvenoja explored how college students manage challenging collaborative learning situations. They gave to the students three consecutive group assignments and a questionnaire to identify the social challenges that they had encountered en route. Five main categories of social challenges were identified, of which personal priorities (differences in goals, priorities, expectations) was the most salient one, followed by work and communication problems, lack of team work commitment, and finding common ground to collaborate. Analyses of videotapes revealed that college students made most use of task structuring (e.g., reducing off-task behavior by structuring the task and the learning environment) and social reinforcement (e.g., positively supporting each other’s suggestions) to increase, sustain, and modify the level of group motivation during collaborative work on the assignments. The students also employed efficacy management strategies, interest-enhancement strategies, and socially shared goal-oriented talk as means to increase or sustain the level of group motivation.

These researchers clearly demonstrated that college students make use of different motivation regulation strategies during collaborative work and that different strategies may serve a different purpose. A cross-data summary from the three sources (questionnaire, videotapes, and interviews) informed us that students used a combination of different motivation regulation strategies in the various stages of the project. The questionnaire data revealed that in the beginning of the project (during task 1, shared reconstruction process of the main issues in the scientific articles), students mostly encountered personal priority and teamwork challenges and made most use of social reinforcement. The interviews informed us that the students were aware that social reinforcing boosted teamwork. For example, students explained that thinking out loud was safe in the group, that sharing ideas and confidence about the task was supportive of their own learning. During task 2 (apply theoretical insights to a case study), making teamwork work was the main challenge. The video data showed mainly social reinforcement strategies combined with task structuring. The interview data brought in that the students were metacognitively aware that they needed to regroup, monitor carefully what was going on in the group, and frequently go back to the instructions so that the focus and structure of the task were clear to all the group members. During task 3 (making a poster), the main challenges were teamwork and collaboration. Students were metacognitively aware that they were dealing with complex constructs and that they had to meet the challenge of creating common ground while negotiating multiple perspectives. Task structuring was the principal motivation regulation strategy that they used in this stage of the project.

We learned from this study that successive combinations of motivation regulation strategies are co-constructed in the learning situation, taking the most salient challenges into account.


Examination of Table 1 shows that McCaslin and Burross (2011) focused on the students’ emergent identity. They argued that learning always takes place in a social context and that students differentially adapt to diverse instructional demands and supports. Observing these adaptations in the classroom has great potential for gaining insight into how students perceive the demands and expectations of schooling. In a coregulation perspective, the way that students participate in the classroom and the quality of their engagement in learning are strong indicators of their emergent identity as the learners they are and want to be. McCaslin and Burross are particularly interested in the way that teachers coregulate students’ participation and engagement. They described different sources that influence coregulation, including personal, social, and cultural sources, as well as the relationships and tensions between these sources. They argued that interactions between these sources give rise to personally desirable and meaningful outcomes of schooling, culturally valued outcomes, and socially validated experiences.

In their study, they focused on primary school students in Grades 3–5 who live in poverty. These students are at risk because their participation and engagement in the classroom may not be validated socially and culturally. McCaslin and Burross documented the instructional opportunities available to these students, and the data provided some evidence that the teachers’ instructional opportunity profile (i.e., the amount of direct instruction, guided elaboration, reviews to test student retention, and structured problem-solving) was associated with students’ self-monitored behavior in the classroom. The students’ self-monitoring reports supplied information on what they were doing (e.g., I was working, I was listening, I was doing my job, and I was looking around) and on their somatic complaints (e.g., I had a dry mouth, my stomach was upset, I was tired).

It was hypothesized that students classified as “good workers,” “engaged learners,” and “struggling and persistent students” adapt favorably to instructional opportunities provided by the teacher and that the students in the “anxious and withdrawn” and the “disengaged and distracted” groups adapt unfavorably to instructional opportunities. Group membership reflected differences in the quality and salience of adaptation to instructional opportunities, but the correlations between the different engagement groups and the provided instructional conditions were rather low, with only one reaching significance at the .05 level. Interestingly, the students’ self-monitoring profiles correlated modestly with their scores on a story-writing task and their performance on end-of-the-year standardized achievement tests.

McCaslin and Burross’s study is important because it shows us a way to study differences in how and why students engage in learning and in how they adapt to different instructional demands and conditions. These authors pointed out that individual differences in participation and adaptation are based on many sources of influence, such as a student’s potential for learning, the actual opportunities for learning that are created in the classroom, and the cultural beliefs, expectations, and challenges that the students are faced with. Obviously it is important to replicate these results before we can draw firm conclusions.


The editors of the special issue asked the five contributing groups to address the question, What does “social” imply in theories of self-regulation, and what do we know about the effects of social aspects of the learning environment on students’ strategy use? Jointly the studies show that strategy use is a process that is energized by many aspects of the social and cultural context of learning. In this section, I will consider areas of convergence, discontinuities, and aspects overlooked in the respective papers.


Examining Table 1 informs us that the social context is defined differently by the different contributors. Hadwin and Oshige (2011) contrasted studies that focused on the effects of peripheral contextual input on self-regulation with studies that examined socially shared learning experiences. Kaplan et al. (2011) and McCaslin and Burross (2011) narrowed the social context to the different affordances or instructional opportunities that are provided by diverse teaching practices. Both of these research groups concentrated on individual students as the main targets for data collection and analysis, and they manipulated the social context as an independent variable in the research design.

Wolters (2011) and Järvelä and Järvenoja (2011) conceptualized the social context in terms of the different obstacles to motivation that students may encounter in the classroom. Wolters examined how individual students deal with these obstacles, and Järvelä and Järvenoja looked at how the group regulated its level of motivation with respect to social obstacles.

Table 1 shows that the five research groups also differ in the way they conceptualized strategy use. Even though all contributors discussed strategy use in terms of the intensity and quality of student engagement in classroom activities, they diverged on the way they operationalized these constructs. Hadwin and Oshige (2011) mainly discussed the monitoring and control processes (metacognition) that are necessary to steer and direct the learning process. They exemplified that monitoring and control takes on a different form when students are self-regulating, coregulating, and participating in collaborative learning. These researchers also noted that students must be aware of interpersonal exchanges and relations, respond to multiple social cues, and regulate their actions for the social good. Regrettably, Hadwin and Oshige did not discuss how learners construct and co-construct the regulation of affect and motivation.

Kaplan et al. (2011) measured specific metacognitive strategies as well as specific motivation regulation strategies. Wolters (2011) concentrated on individual motivation regulation strategies, and Järvelä and Järvenoja (2011) focused on group-based regulation of motivation. Finally, McCaslin and Burross (2011) used students’ self-monitored engagement as the primary indicator of strategy use. They did not assess presence or absence of specific self-regulation strategies, relying exclusively on students’ spontaneous recordings of mainly on-task and off-task behavior and psychosomatic complaints.

It should be pointed out that in some of the studies reported in the special issue, information on students’ strategy use was collected exclusively by means of self-reports. As Winne (2005) pointed out, self-reports are notoriously inaccurate and will result in biased correlations between measures of how learning is carried out and the results of those approaches on learning. It is recommended, therefore, that researchers select complementary assessment methods, such as observations, interviews, stimulated recall, thinking-alouds, and collecting traces to accrue more detailed information on students’ strategy use. Triangulation will produce a more complete and accurate account of the strategies that were actually used by different types of students (e.g., high achievers vs. low achievers; students who come from educated and poor backgrounds) who have enjoyed education in diverse learning settings. We need to know what combinations of strategies are used by students who adapt favorably to the instructional opportunities that the teacher provides and which cues in the learning environment they actually use to select these strategies.


The studies by Kaplan et al. (2011) and McCaslin and Burross (2011) both explored the effect of diverse instructional opportunities on students’ engagement in the classroom. Kaplan et al. suggested that mastery-oriented students, who had worked in authentic classrooms, considered self-praise (i.e., using tangible and verbal rewards to reinforce learning activities perceived as successful) rather than self-evaluation (i.e., using correctness and adequacy of a solution as a means to judge a successful learning outcome) as a strategy that served a mastery goal. The mastery-oriented students, who had studied in a traditional learning environment, held the opposite view, and the performance-oriented students, who had studied in an authentic learning environment, shared their view. Kaplan et al. concluded that the perceived purpose for engagement is tightly interwoven with strategy use and is the product of the learning environment within which students have learned these strategies.

McCaslin and Burross (2011) similarly argued that the way that students participate in the classroom and the quality of their engagement in learning are strong indicators of their emergent identity as learners (i.e., what type of learners they currently are and want to be in the future). These authors’ main message is that successful learners (good workers, engaged learners) adapt favorably to the instructional opportunities provided by the teacher and that anxious and withdrawn students, as well as distracted and disengaged students, do not profit from these opportunities.

My position is that the former types of students are able to take on a complementary role to what the teacher is doing, whereas the latter groups show behavior that does not seem to be instigated by what the teacher requests them to do. The question is, then, Why do these unsuccessful students fail to take note of what the teacher wants them to do? An answer to this question can only be formulated when we have insight into the students’ conceptions of what good learning and teaching mean, and in the interpersonal and interdependence beliefs that they can bring to bear on the instructional context.

Having access to conditional knowledge and using this knowledge. To participate in classroom activities, students must be aware of many things, including their own and the teacher’s (i.e., the coregulator’s) metacognitive experiences. They must also be able to respond to instructional and social cues and regulate their actions accordingly. For example, consider a situation in which the teacher is lecturing. All students may know that they need to listen when the teacher explains something, but only the successful students may be aware that they ought to listen actively and constructively. Similarly, during review, all students might realize that they need to provide an answer, but only the successful students might be aware that they need to search their memory for several bits of declarative knowledge and that they have to formulate the answer in such a way that the procedural rule is displayed. Analogously, when teachers ask their students to practice a new skill, only the successful students might be aware of why they need to practice that skill and what exactly they have to do.

In these situations, being aware means having access to metacognitive and metamotivational knowledge, which specifies when, where, and how specific strategies can be successfully used. In addition, it implies that the students have access to metaknowledge on interpersonal cooperation and interdependence. Metainterpersonal knowledge informs them on what it is that the teacher wants them to do and why, and on what supporting activities can or cannot be expected from the teacher. I suggest that the successful students in the McCaslin and Burross’s (2011) study expected the teacher to coregulate their performance, meaning that he or she monitors their adherence to the instructions and their progress in learning, but also provides adequate instructional and emotional support when necessary. By contrast, the less successful students may have experienced metacognitive, metamotivational, and metainterpersonal deficiencies. They might not have had a clue as to why they needed to do the things the teacher asked them to do, what they had to do, and how they had to do it. As such, they were not able to follow up on the teacher’s instruction.

A similar argument could be formulated for the good and poor writers in the Kaplan et al. (2011) study. In both the traditional and authentic school environment, poor writers had not yet integrated purpose and the selection of appropriate strategies. An explanation that Kaplan et al. put forward is that poor writers may not perceive these strategies as a means to realize the writing goal. An alternative interpretation that Kaplan et al. suggested is that the poor writers had formed a rather mechanical relationship between the purpose of writing and writing strategies, in the sense that they may mindlessly activate (writing) strategies that may not fit the writing goal (i.e., they suffer from metacognitive deficiency).

I suggest that unsuccessful students do not and that successful students do perform the specific learning functions that the teacher wants them to perform. That is probably the reason why the disengaged and distracted learners in the McCaslin and Burross (2011) study reported mainly off-task behavior (e.g., I was looking around, I was talking, and I was tired) and that the anxious and withdrawn students reported only psychosomatic complaints. Interestingly, the struggling and persistent group seemed to be aware that they were getting help and that providing and receiving social support are major aspects of the learning process. In other words, these students seemed to have access to metainterpersonal and metamotivational knowledge, but they still had metacognitive deficiencies.

Being aware of the different learning functions that need to be fulfilled. Shuell (1988) adequately explained that for students to learn effectively, several specific learning functions should be performed either by the teacher or by the learners themselves. These functions refer to cognitive, metacognitive, and motivational strategies, such as drawing attention to key words in the text, attaching value, gaining confidence, meaningful interpretation of the text, monitoring reading comprehension, integrating information in long-term memory schemata, assessing, and attributing the result. Each of these learning functions can be carried out either by the teacher (external regulation) or the students themselves (self-regulation), or they can be shared (coregulation), For example, when the teacher wants his students to pay attention to verb forms in the present continuous in the text, he might draw the students’ attention to the exact locations where the verb forms can be found in the text; he might also highlight these verbs in color or ask the students to find them in the text. These different forms of drawing the learner’s attention to the specific grammatical constructions are functionally equivalent in their ability to accomplish the relevant psychological function (draw attention to. . . ).

It is important that teachers are confident that their students will be responsive to the instructions (i.e., have access to relevant metainterpersonal knowledge to decode the instructions). More concretely, students should be able to detect, from what the teacher says, does, or requests, who assumes primary responsibility for the different aspects of the learning process. Judging from what the good workers and the engaged students in the McCaslin & Burross (2011) study wrote in the self-reports, I would suggest that they were aware of the learning functions that they needed to perform (e.g., I was doing my part, I was working, I was listening, I was into it).

Knowledge of the social rules and regulations that apply in the classroom, of relevant social scripts, as well as knowledge of different interdependence patterns is essential to profit from coregulation and collaborative learning. Students need to have access to this type of knowledge —which I have coined metainterpersonal knowledge or interpersonal beliefs, for lack of better terms—to follow up instructions and participate in various cooperative learning activities. It is therefore essential that researchers measure the students’ metainterpersonal knowledge alongside their metacognitive and metamotivational knowledge to explore why some students do and others do not adapt easily to specific instructional affordances and opportunities.


We have seen in this special issue that research on social and self aspects in self-regulation has branched out to include coregulation and socially shared regulation. It is my judgment that the greatest challenge for the coming decade is to consolidate the conceptual knowledge about group learning that has been accrued so far in educational psychology, social psychology, work psychology, and lifelong learning. We need to develop a greater sense of systematization and, above all, we need greater clarification of the meanings of the most central social constructs. We also need to better address the process aspects of self-regulation, coregulation, and socially shared regulation and put longitudinal studies of self-regulation, coregulation and socially shared regulation on the research agenda. In the last few sections of this article, I will address these issues in turn.


It is a fact that most of the constructs that we have used to study self-regulated learning have been designed for individual learning. For example, it is generally accepted that students who have a personal sense of purpose and mastery and believe that they are self-efficacious will be more successful in school compared with students who are rather passive, disengaged, anxious, and avoidant. Current research findings clearly support this Western view. Dunahoo, Hobfoll, Monnier, Hulsizer, and Johnson (1998) argued, however, that individuals can also have a collective sense of agency. They showed that students who scored high on communal mastery preferred group problem-solving to address challenging situations rather than working alone or in competition with others. These students valued social support (acquiring and providing support), and they considered the outcomes of their actions as based on being a member of the group. Hobfoll, Schröder, Wells, and Malek (2002) also reported that students who had adopted a socially integrated style valued communal mastery more than self-mastery. These students were inclined to make more mindful choices and acted less hurriedly compared with students who had a self-mastery goal orientation. They also expressed more concern with others, had more close links to a supportive social network, and reported that satisfaction could be derived from group membership.

It is important to look carefully at the social constructs that have been studied in other areas of psychology and borrow the assessment instruments that have already been validated rather than start from scratch. In my opinion, it is crucial that we assess—rather than assume—whether students perceive themselves as effective agents in achieving the learning goals and dealing with social challenges or whether they consider themselves effective students by virtue of being a team member.


We also need to take up the process aspects of self-regulation. For example, in the studies of Kaplan et al. (2011) and McCaslin and Burross (2011), the way that students adapt to different instructional opportunities was investigated. Although this is most certainly a relevant issue when studying engagement profiles, these studies did not address the processes by which specific engagement profiles are formed and the processes that affect these profiles over time. Kaplan et al. assessed the students’ thought processes about engagement rather than the actual operation of the process mechanisms. In McCaslin and Burross’s (2011) study, students’ perceptions of their own engagement were used as the principal indicators of adaptation to the instructional affordances created by the teacher. Evidently, more research is needed before conclusions can be drawn about the causes or correlates of real-time strategy use—rather than their proxy assessment via self-reports or self-monitoring of on-task and off-task behavior. What we need to study is how different engagement profiles are shaped and reshaped from the students’ interpretation of instructional opportunities during ongoing interactions with specific instructional practices. In other words, we need to fill the gap between broad cognitive construct, such as goal orientations, metacognitive knowledge, and emergent identities of the learner on the one hand, and actual behavior (i.e., strategy use) on the other.

A way to do this is by identifying units or modules in which broad cognitive constructs are linked to specific strategic actions. For example, Kaplan et al. (2011) identified purpose-strategies action orientations, and Wolters (2011) and Järvelä and Järvenoja (2011) identified strategies that deal with obstacles. Identifying such units allows researchers to explore how these units function, and how they interact with each other and with higher order goals. If we want to describe the architecture of the motivation system as it pertains to learning, we need to specify how specific motivation units are shaped, accessed, used, and reshaped over time.

Salonen, Vauras, and Efklides (2005) also argued that we should describe recurrent, interbehavioral patterns and interpersonal moment-by-moment coordinations. The micro genetic designs described by Lavelli, Pantoja, Hsu, Messinger, and Fogel (2005) might reveal the fine-grained changes in the context of social interactions while they are occurring, as well as dynamic and relational invariance.


Another crucial challenge, related to what I have just discussed, that should be on the agenda of future research in this area is longitudinal studies of the regulatory processes involved in coregulation and socially shared regulation. Hadwin and Oshige (2011) and Järvelä and Järvenoja (2011) argued that a multitude of actions occur in the shared social space when students work collaboratively. I agree with these researchers that the most pressing practical questions associated with individual and group learning do not concern how students use self-regulation strategies on a single occasion or in a single lesson period. Instead, these questions should focus on how contextual factors, including the presence of coactors in the learning process, interact and affect the use of co-constructed regulation patterns. Järvelä and Järvenoja provided us with a limited set of social challenges and some examples of what actually happens when students are dealing with these challenges. They also provided us with a language to reflect on and discuss these complex processes. As such, they lifted up a tip of the veil that still covers the complexity of group learning. However, we need to go even further and examine how contextual factors interact with each other and influence the development and maintenance of these (self) regulatory patterns over time. Järvelä and Järvenoja made a start by exploring how group members develop a shared understanding of a project they have to work on for several weeks and how they co-construct the goals and standards for that group learning activity.


Collectively, the articles in the special issue enhance our insights into the social embeddeness of regulation strategies in the classroom. The selection of strategy use comes about from the continuing interchange between students’ metalevel knowledge of cognition, motivation, and interpersonal behavior on the one hand, and the behavior of the coregulators present in the learning environment on the other. It is clear from the articles in the special issue and from my own analysis of the field that new models and methodologies are on the horizon and that much work needs to be done to create new theoretical understandings of the multiple, reciprocal connections between diverse features of the social context and the quality of students’ strategy use in the classroom.


Alexander, P.A. (2003). The development of expertise: The journal from acclimation to proficiency. Educational Researcher, 32(8), 10–14.

Boekaerts, M. (1997). Self-regulated learning: A new concept embraced by researchers, policy makers, educators, teachers, and students. Learning and Instruction, 7, 161–186.

Dunahoo, C. L., Hobfoll, S. E., Monnier, J., Hulsizer, M. R., & Johnson, R. (1998). There’s more than rugged individualism in coping. Part 1: Even the Lone Ranger had Tonto. Anxiety, Stress, and Coping, 11, 137–165.

Hadwin, A., & Oshige, M. (2011). Self-regulation, coregulation, and socially shared regulation: Exploring perspectives of social in self-regulated learning theory. Teachers College Record,


Hobfoll, S. E., Schröder, K. E. E., Wells, M., & Malek, M. (2002). Communal versus individualistic construction of sense of mastery in facing life challenges. Journal of Social and Clinical Psychology, 21, 362–399.

Kaplan, A., Lichtinger, E., & Margulis, M. (2011). The situated dynamics of purposes of engagement and self-regulation strategies: A mixed-methods case study of writing. Teachers College Record, 113(6).

Lavelli, M., Pantoja, A. P. F., Hsu, H., Messinger, D., & Fogel, A. (2005) Using microgenetic designs to study change processes. In D. G. Teti (Ed.), Handbook of research methods in developmental psychology (pp. 1–50) Oxford, England: Blackwell.

McCaslin, M., & Burross, H. L. (2011). Research on individual differences within a sociocultural perspective: Coregulation and adaptive learning. Teachers College Record, 113(6).

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.

Shuell, T. J. (1988). The role of the student in learning from instruction. Contemporary Educational Psychology, 13, 276–295.

Winne, P. H. (2005). Key issues in modeling and applying research on self-regulated learning. Applied Psychology: An International Review, 54, 232–238.

Wolters, C. A. (2011). Regulation of motivation: Contextual and social aspects. Teachers College Record, 113(6).

Cite This Article as: Teachers College Record Volume 113 Number 2, 2011, p. 375-393
https://www.tcrecord.org ID Number: 15981, Date Accessed: 12/3/2021 2:05:08 AM

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About the Author
  • Monique Boekaerts
    E-mail Author
    MONIQUE BOEKAERTS currently holds the chair of learning and instruction at Leiden University. She has played a leading role in the development of the motivation construct, designing the first situation-specific measurement instruments to assess motivation in the classroom. Boekaerts has built up a national and international reputation for the application of her theories of motivation and self-regulation in educational practice. Her booklet Motivation to Learn was translated into many languages and has been widely disseminated to policy makers and teachers. Recent publications include: Boekaerts, M., & Rozendaal, J. S. (2008). Expertise development in the college classroom: new insights. In D.C. Berliner & H. Kupermintz (Eds.), Fostering change in institutions, environments, and people (pp. 95118). New York: Routledge; Boekaerts, M. (2009). Goal directed behavior in the classroom. In K. Wentzel & A. Wigfield (Eds.), Handbook of motivation at school (pp. 105122). New York and London: Routledge; and Boekaerts, M. (in press). Motivation and self-regulation: Two close friends. In T. Urdan, S. Karabenick, & F. Pajares (Eds.), Advances in motivation achievement (Vol. 16). Bingley, England: Emerald Group.
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