Refers to interaction patterns, knowledge construction, or regulatory actions without connection to regulatory constructs or targets.
Facilitative and directive other regulation. Other regulation aimed at guiding versus controlling the group’s regulatory processes. Regulatory act/ action in a co-regulatory trajectory.
Rogat 8c Linnenbrink-Garcia (2011)
Co-regulated learning. Interpersonal interaction geared towards monitoring and managing each other’s learning.
Garrison 8c Akyol (2015); Zheng 8c Huang (2016);
Ucan 8c Webb (2015) Group regulation. Group coordinates their efforts and
resources in effective ways to achieve common goals.
Kwon, Liu, 8c Johnson (2014) Internal and external regulation. Self-regulation vs.
regulation by the external setting like scaffolding or scripting.
Romero 8c Lambropoulos (2011)
Team vs. task regulation. Distinguishing regulation of the collaboration from regulation of the task.
Duffy et al. (2015); Saab, Joolingen, 8c Hout-Wolters (2011); Janssen, Erkens, Kirschner, 8c
Kanselaar(2012) Co-regulation (CRL). Joint influence of self-
regulated and other regulating agents on students’learning.
Tsai (2015)
The Emergence of Overarching Terms
Terms such as social regulation and interpersonal regulation have emerged as umbrella terms broadly referring to social forms of regulation primarily as a means for distinguishing it from regulation at the individual level (e.g., self-regulation). Adopting these terms emphasizes the importance of group-level regulatory activities such as planning, monitoring, and evaluating in collaborative work. However, the nuanced use of these terms to distinguish CoRL and SSRL from SRL is often misinterpreted to imply self-regulated learning is not a social mode of regulation. In contrast, we have always positioned self-regulated learning as a social process influenced by and influencing social context.
From a situated perspective, individual agency arises as part of a rich social milieu feeding self-regulated learning and growing from it. Conditions have internal and external properties. Products generated during regulation, such as shifts in emotional state, adopting a particular strategy, or exerting effort to reach a goal, become self conditions for individuals, and contextual conditions for collaborators (see Figure 6.1). In this way, self-regulation is inextricably social. Therefore the category social regulation refers broadly to all modes of regulation (including self-regulated learning) within collaborative learning contexts, social situations, and groups. Importantly, social regulation is not synonymous with socially shared regulation but may subsume SSRL among other things.
The Confusion Over Co-Regulation
Over the past five years, new research approaches, data sources, coding schemes, and analytic approaches have empirically documented modes of regulation and distinguished shared regulation from other modes of regulation (cf. Panadero & Järvelä, 2015). However, reviewing the literature reveals a state of confusion with respect to co- regulation in particular. The term co-regulation has been used to refer broadly to every mode of regulation during collaborative learning (e.g., DiDonato, 2013). It has also been used synonymously with social regulation (e.g., Volet, Summers et al., 2009) to refer to “constant monitoring and regulation of joint activity, which cannot be reduced to mere individual activity” (Vauras et al., 2003, p. 35). Further, CoRL is often misrepresented as an asymmetrical interpersonal interaction (e.g., Ucan & Webb, 2015) whereby group members regulate each other (e.g., Garrison & Akyol, 2015; Volet, Summers et al., 2009) often through prompting and coordinating actions (DiDonato, 2013).
However, we draw heavily from McCaslin’s (2004) initial socio-cultural conceptualization of co-regulation as the process whereby social environment supports the emergence of regulation, recognizing support is distributed amongst people (rather than one more capable other), task, tools, and environment. This view reconciles tensions in the field by acknowledging (a) the transitional nature of co-regulation in supporting or sometimes constraining the emergence of regulation, (b) the role of co-regulation in supporting the emergence of both self-regulated learning and shared regulation, and (c) the distributed nature of co-regulatory support across people and context afford opportunities for joint regulation to emerge. In this way CoRL plays a mediational role for SRL and/or SSRL. Viewing CoRL in this mediational way acknowledges the scaffolding role CoRL can play in spawning more proficient self-regulated learning as well as [socially] shared regulation.
New Terms for Regulatory Sources and Targets
Coding discourse and triangulating across data sources has led to new labels for regulatory sources and targets operating within the three modes of regulation. For example, other regulation has been used to refer to a regulatory act/action in a co-regulatory trajectory whereby regulation is directed or facilitated by others (e.g., peer, teacher, etc.). Recently, Rogat and Adams-Wiggins (2015) compared other regulation that controls (directive-other regulation) versus guides (facilitative-other regulation), finding facilitative-other regulation contributes to more balanced participation and regulatory contributions amongst group members.
From our perspective, other regulation is merely an affordance or constraint for self-regulated and/or shared regulation of learning. It is a coding node in discourse analysis, until it takes on a co-regulatory role by changing or shaping self- or shared regulation. Directive-other regulation can be characterized as a constraint for self- and shared regulation. Co-regulation is born when other regulation occurs and is acted on in terms of individual or shared regulatory planning, monitoring, evaluating, or strategic action targeting behavior, motivation, affect, or cognition.
What Does Shared Mean?
The term shared holds multiple meanings in the literature ranging from: (a) sameness, such as when individual group members hold similar or common goals, plans, and evaluations of the joint work, to (b) co-constructed,
such as when group members jointly negotiate shared goals and plans, and share in the monitoring and evaluating.
However, “sameness” (holding the same goal or evaluation), does not imply shared. Shared regulation is co- constructed; it is a mutually reactive, interdependent, and transactive process related to planning, monitoring, evaluating, and controlling learning processes. Negotiated emergent agreement is the goal, rather than implicit or passive agreement occurring when an individual acquiesces or just happens to hold the same idea. Transactivity occurs when reasoning builds on, relates to, and refers to reasoning shared by other group members (Berkowitz
& Gibbs, 1983; Teasley, 1997). Transactivity has been associated with successful construction of metacognitive knowledge in shared regulation, particularly when augmented with support in the form of reciprocal peer tutoring (De Backer et al., 2015).
Challenge 2: Regulated Learning Involves Psychological Constructs
The 2011 chapter boldly claimed research is not about any social mode of regulated learning if it is not anchored in specific psychological constructs including: (a) regulatory processes (monitoring, evaluating, and controlling), and (b) regulatory constructs or targets (motivation, cognition, behavior, and emotion). Since the 2011 chapter, promising advances have emerged in research about regulation in collaboration. In particular, it is becoming increasingly common to research multiple targets and/or processes of regulation within a study. For example, Lajoie et al. (2015) examined the role of socio-emotional processes in both metacognition and co-regulation used by medical students learning to deliver bad news. They specifically coded for: (a) meta-cognitive processes—
orientation, planning, executing, monitoring, evaluating, and elaborating; (b) positive expressing emotions; and (c) negative socio-emotional interactions. Although one might argue coding of co-regulation in this study emphasized cognitive knowledge construction primarily, the findings advance research about regulation by considering the dynamic relationships between emotions and metacognition in a distributed online problem-based learning environment.
Similarly, Ucan and Webb (2015) examined the roles of both metacognitive and emotion regulation in the emergence and maintenance of multiple modes of regulation (self-, co-, and shared) during seventh grade science inquiry collaborations. Finally, Järvelä, Järvenoja, Malmberg, Isohätälä, and Sobocinski (2016) examined groups’
cognitive and socio-emotional interaction with respect to three phases of regulation (fore-thought, performance, and reflection), illuminating differences in phases of regulation between cognitive and socio-emotional segments of discourse.
At the same time, three problematic trends exist in the research. First, there is a tendency to limit operational definitions of regulation to “cognitive” episodes alone, implying metacognitive knowledge and processes apply exclusively to domain and task knowledge construction. For example, Khosa and Volet (2014) investigated productive group engagement in cognitive activity and metacognitive regulation by coding: (a) high- and low- quality metacognitive regulation (planning, monitoring, and evaluating) in knowledge construction (talking about the domain knowledge) and knowledge production (talking about the task) episodes, and (b) the social nature and function of metacognitive activity (solo or collective). De Backer et al. (2015) investigated how socially shared metacognitive regulation correlates with both collaborative learners’ content processing strategies and the level of transactivity in their discussions. They analyzed students’ content processing strategies (i.e., questioning and explaining), as well as cognitively oriented and metacognitively oriented transactive discussions.
In contrast, metacognitive planning, monitoring, and evaluating should figure prominently as regulatory processes in motivation, emotion, behavior, and cognition. Modeling motivational and socio-emotional states as both conditions and products in learning (cf. Winne & Hadwin, 2008) acknowledges the salience of metacognitive monitoring, evaluation, and adaptation for motivational, affective, and even behavioral knowledge and beliefs.
From this perspective beliefs, thoughts, and perceptions are cognitive products (and conditions) in learning regardless of whether they focus directly on task or domain knowledge.
Restricting analysis to cognitive or content episodes exclusively tends to conflate knowledge construction and regulation because only knowledge construction episodes are examined for evidence of metacognitive processes (monitoring, evaluating, controlling) or regulatory modes (self-, co-, and shared regulation). It obscures evidence of metacognitive planning, monitoring, and control of motivation, emotions, or strategic behavior. Finally, and perhaps most importantly, focusing on domain and task segments alone precludes the possibility of interrelationships in regulation across facets (motivation, cognition, emotion, and behavior). For example, shared regulation of task production may arise in response to heightened task anxiety for one group member. Similarly, groups may collaboratively generate a strategy such as making sure everyone shares one idea (controlling behavior), in response to a cognitive evaluation of insufficient course concepts in a group response.
A second potential problem emerging in the contemporary research relates to delimiting regulatory action to specific regulatory constructs or targets (motivation, cognition, emotion, behavior). For example, Kwon et al.
(2014) coded interactions for: (a) group regulatory behaviors—discussions involving coordinating members’ joint efforts toward common goals; or (b) socio-emotional behaviors—discussions expressing or encouraging emotions. This approach to analysis precludes the possibility of groups regulating socio-emotional factors or conditions. In contrast, regulatory acts should be considered responses to situated challenges (e.g., time, efficiency, difficulty). What is important and different in our conceptualization is the implied interaction between motivation, emotion, metacognition, and strategic behavior in successful learning.
Similarly, a new trend in the research distinguishes between (a) task regulation defined as regulating the cognitive activities during learning, and (b) team regulation defined as coordinating the collaboration between students, such as checking others’ opinions. From our perspective, teasing apart team and task regulation: (a) obscures the dynamic interplay between team and task in self-, co-, and shared regulation, and (b) reduces regulation to a change-behavior devoid of critical metacognitive processes (planning, monitoring, evaluating) and psychological targets (motivation, behavior, emotion, and behavior).
Challenge 3: Challenges Provoke Opportunities for Regulation
The mark of successful regulation is strategic adaptation in response to a challenging situation or problem (Winne
& Hadwin, 2008). Given the surprising lack of research examining social aspects of learning at key points when challenge is encountered through to when it is resolved, challenge episodes were proposed in the 2011 chapter as critical for segmenting and analyzing data and discourse. Overall, this area has received minimal uptake in the field. For the most part, research about regulation has examined it across full collaborative episodes (e.g., Grau
& Whitebread, 2012; Rogat & Adams-Wiggins, 2015; Ucan & Webb, 2015), or at timed intervals (Iiskala et al., 2015; Molenaar & Chiu, 2014) over the course of collaboration, rather than using challenge episodes for segmenting and narrowing observations to periods in which a regulatory response is warranted.
Researchers across our programs of research have collected data about anticipated and perceived challenges and challenge indicators with a goal of identifying specific targeted episodes to observe regulatory responses (Miller
& Hadwin, 2015a; Panadero et al., 2015). For example, Malmberg, Järvelä, Järvenoja, and Panadero (2015) used the Virtual Collaborative Research Institute (VCRI) learning environment along with regulation tools prompting (a) identification of challenges hindering collaboration, and (b) planning SSRL strategies to overcome those challenges. Process mining findings indicated: (a) shifts from regulating external challenges toward regulating the cognitive and motivational aspects of collaboration depending on the phase of the course, and (b) temporal variety in challenges and regulation strategies across the time.
Despite limited empirical progress with respect to researching regulation within challenge episodes, at least three promising lines of inquiry provide foundation for the field to continue work in this area. First, groups experiencing positive socio-emotional interactions also engage in more regulatory processes such as planning, monitoring, and behavior than groups who experience negative socio-emotional reactions (Rogat & Linnenbrink-Garcia, 2011).
Findings may indicate active engagement in regulatory processes mitigates socio-emotional challenges, but given inferences are drawn from in-depth case studies of limited groups, further investigation is warranted.
Second, research has begun to identify types of events stimulating regulation and metacognitive processes. For example, Ucan and Webb (2015) found expressing misconceptions or lack of understanding of domain content stimulates co-regulatory processes, whereas expressing uncertainty about a shared idea, seeking consensus, and experiencing contradictory views tended to stimulate shared regulation. Findings such as this focus on the adaptive nature of regulation arising in the context of simulating events.
Finally, research evidence to date points to at least five broad types of challenges experienced by groups across a variety of settings (Bakhtiar, 2015): (1) Motivational challenges tend to center around differing personal priorities such as competing goals, or differing participation levels. Typically these challenges result in declines in effort, engagement, or participation (e.g., Järvelä & Järvenoja, 2011). (2) Socio-emotional challenges refer to challenges in achieving positive climate such as relational problems associated with achieving psychological safety, communicating effectively, and navigating power relationships (Näykki, Järvelä, Kirschner, & Järvenoja, 2014).
(3) Cognitive challenges refer to difficulties in achieving shared mental models of the task and domain, or choosing effective solution paths and strategies (Barron, 2003). (4) Metacognitive challenges relate to difficulties monitoring, evaluating, and reflecting on group processes, products, and progress (Janssen et al., 2012). (5) Environmental challenges relate to external conditions surrounding collaborative work such as technology, task complexity and duration, resources, and group composition (Hommes et al., 2013). We posit the occurrence of these challenges demands varying modes of regulatory action and warrants future investigation.
Challenge 4: Regulation as Change Over Time
The 2011 chapter posited that regulation implies adaptation over time; to adequately research regulation, data should be sampled over time both within and across episodes. This area of research has shown tremendous growth over the past few years. In addition to noting increases in regulation over time (e.g., DiDonato, 2013), research has begun to explore patterns in emerging regulation over time. More recently, data mining techniques have been used to examine sequential patterns in regulation over time: (1) Lajoie et al. (2015) examined changes in metacognitive activity across two problem-based learning (PBL) online sessions. They found growth and progression on adaptive adjustments in the PBL group’s thinking, based on continuous metacognitive monitoring.
They also found a strong connection between co-regulatory actions activating discussion and metacognitive planning, revealing a co-occurrence of metacognitive, co-regulatory, and social-emotional interactions. (2) Schoor and Bannert (2012) explored logfile sequences of social regulatory processes during a computer-supported collaborative learning (CSCL) task. Although they found clear parallels between high-and low-achieving dyads in a double loop of working on the task, monitoring, and coordinating, closer examination indicated the lower- achieving group displayed faster change between categories, despite having similar patterns of regulation. (3) Järvelä et al. (2016) examined temporal sequences for self- and socially shared regulation during CSCL and found: (a) shifts in the types of self-regulation and socially shared regulation of learning as work progressed, and (b) a tendency of individual self-regulatory processes to be salient early in the collaborative process.
Understanding socially shared regulation of learning requires an understanding of the learning context and the evolution of social and regulatory processes over time. Continued advancements in observational data collection techniques as well as analytical methods and tools are necessary for furthering research about the sequential and temporal aspects of regulation. However, pursuing these methods requires care to avoid the reduction of regulation (self-, co-, or shared regulation) to action alone. Regulation is more than what people do and how they do it. Understanding regulation means knowing something about internal perceptions and intent. Social interactions, sequences, and patterns need to be contextualized in larger episodes of activity with attention to individual and collective goals, plans, and reflection to delineate meta-cognitively driven regulatory processes versus extemporaneous patterns of interaction.
Challenge 5: Researching the Co-Emergence of SRL, CoRL, and SSRL
Over the past five years, the field has progressed past naive notions of learning as solely individual or solely collaborative and must now take up the challenge of understanding how these three modes of regulation (self-, co-, and shared) contribute together to successful collaborative learning. For example, Malmberg, Järvelä, and Järvenoja (2016) investigated how temporal sequences of regulated learning events, such as regulation types (self- , co-, and shared) and regulation processes (e.g., planning, monitoring) emerge during different stages of the collaborative learning process. Qualitative content analysis and sequential analysis of videotaped sessions indicated task execution promoted socially shared planning despite co-regulated learning occurring most frequently. Such research indicates different modes of regulation may support one another in relation to task completion.
Panadero et al. (2015) specifically examined the relationship between self-regulated learning, shared regulation of learning, and group performance in the context of a collaborative essay writing task during a multi-media learning course for pre-service teachers. Findings indicated that groups with better individual self-regulators reported higher levels of group regulation in terms of the collective number of shared goals and strategies, and the activation of strategies to regulate challenges. Despite over reliance on self-report measures of self-regulated learning administered once only, findings from this study establish a relationship between individual self- regulation and aspects of socially shared regulation.
Similarly, Grau and Whitebread (2012) examined the relationship between primary children’s self-regulated learning (planning, monitoring, control or regulation, and reflection) and social aspects of regulation including:
(a) directing the regulation of others (which they labeled co-regulation), and (b) participating in joint regulation of the task (which they labeled socially shared regulation). In addition to observing increases in self-regulation over the semester, they found significant positive correlations between shared regulation and references to relevant knowledge. Importantly, this research attempted to overcome the dichotomy between individual agency and group activity by examining the interrelationships between self-regulated learning and interpersonal elements of regulation characteristic of either co-regulation or socially shared regulation.
Finally, DiDonato (2013) examined self-reported co-regulation as a possible moderator of changes or improvements in self-reported self-regulated learning during collaborative work. The difference between pre-post SRL scores served as a Level-1 individual outcome variable and group co-regulated learning score (midway through collaboration) served as the Level-2 variable in hierarchical linear modeling. Findings indicated self- reported self-regulated learning increases from the beginning to the end of a collaborative interdisciplinary middle school project, but self-reported co-regulation moderated the relationship between SRL and time. In other words, groups with higher co-regulation scores were also more likely to have individuals whose SRL scores increased from the beginning to the end of the nine-week collaboration period. In-depth video analysis of one group indicated the presence of other regulation rotating amongst group members. Further, when one student took the planning lead, producing a well-defined project idea and providing elaborated explanations about why this was a good plan, it seemed to provide a shared platform for individual and collaborative regulation and task completion.
Together these studies demonstrate the importance of drawing on multiple analytical methods to examine the ways multiple modes of regulation operate in support of one another during collaborative learning tasks. Together these studies have taken important steps toward overcoming the dichotomy between modes of regulation and instead examining the interplay between them. Limited findings to date suggest co-regulation may moderate increases in self-regulatory processes over the course of collaboration (e.g., DiDonato, 2013), while proficiency in self-regulation may set the stage for the emergence of shared regulation (e.g., Panadero et al., 2015).