Theoretical Framework of Self-Regulation, Co-Regulation and Socially Shared Regulation
Hadwin, A. F., Järvelä., S., & Miller, M. (in press). Self-regulation, co-regulation, and shared regulation in collaborative learning environments. In D. H. Schunk, & J. A. Greene, (Eds.). Handbook of self-regulation of learning and performance (2nd ed.). New York: Routledge.
This chapter revisits and updates our earlier conceptualizations of social modes of regulation in collaboration (Hadwin et al., 2011) with the aim of: (a) summarizing relevant theoretical ideas, (b) grounding these constructs in their educational psychology foundations, (c) highlighting contemporary research evidence bearing on these ideas, (d) offering directions for future research, and (e) discussing implications for practice.
Järvelä, S., & Hadwin, A. F. (2015). Promoting and researching adaptive regulation: New frontiers for CSCL research. Computers in Human Behavior, 52, 559-561.
The last two decades have witnessed significant advances in collaborative learning, and in the range of collaborative tools and technologies available for collaborative work. Recent research has shown that succeeding in collaborative contexts requires the development and refinement of a range of regulatory skills and strategies for generating shared problem spaces, planning, monitoring, evaluating and adapting group processes. In other words, group members must develop skills for regulating themselves (self-regulation), each other (co-regulation), and together (socially shared regulation). Computer supported collaborative learning (CSCL) environments afford opportunities to guide, support, and research regulation. This special issue presents a new generation of CSCL tools and emerging empirical research focused on supporting and examining the role/s of regulation in collaboration. Collectively papers in this special issue: (a) identify specific targets of regulation, (b) introduce tools and technologies for supporting and researching regulation, (c) present empirical findings to show how regulation emerges or influences collaboration, and (d) identify and discuss conditions under which regulation emerges (or does not emerge).
Scripting and Visualization to Support Regulation
Selected Publications and Presentations:
Miller, M., Hadwin, A. F. (2015). Scripting and awareness tools for regulating collaborative learning: Changing the landscape of support in CSCL. Computers in Human Behaviour, 52, 573-588.
This conceptual paper addresses the need to design tools for supporting regulation in computer supported collaborative learning (CSCL). First, we extend previous work articulating the important role of self-regulation, co-regulation, and shared-regulation in successful collaboration (Hadwin, Järvelä, & Miller, 2011; Järvelä & Hadwin, 2013). Second, we draw on this theoretical framework to address the capacity of CSCL environments to support regulation of collaboration in the form of two types of tools:
(a) Scripting tools that structure and sequence collaborative interactions, and (b) group awareness tools that collect, aggregate and reflect information back to learners to facilitate collaboration. Finally, directions for future research of regulation of collaboration and CSCL regulation tools are discussed.
Järvelä, S., Kirschner, P. A., Hadwin, A., Järvenoja, Hl., Malmberg, J., Miller, M., & Laru, J. (2016). Socially shared regulation of learning in CSCL: Understanding and prompting individual and group level shared regulatory activities. International journal of computer-supported collaborative learning, 11 (3), 263-280. DOI: 10.1007/s11412-016-9238-2
The field of computer supported collaborative learning (CSCL) is progressing instrumentally and theoretically. Nevertheless, few studies examine the effectiveness and efficiency of CSCL with respect to cognitive, motivational, emotional, and social issues, despite the fact that the role of regulatory processes is critical for the quality of students’ engagement in collaborative learning settings. We review the four earlier lines in developing support in CSCL and show how there has been a lack of work to support individuals in groups to engage in, sustain, and productively regulate their own and the group’s collaborative processes. Our aim is to discuss how our conceptual work in socially shared regulation of learning (SSRL) contributes to effective and efficient CSCL, what tools are presently available, and what the implications of research on these tools are for future tool development.
Hadwin, A. F., Bakhtiar, A., & Miller, M., (2017). Challenges in an Online Collaboration: Comparing Shared Planning Supports. Paper presented at the Canadian Society for the Study of Education. Toronto, Canada.
The mark of successful regulation is strategic adaptation in response to challenging situations. In group work, the research to date points to at least five broad types of challenges experienced by groups across a variety of settings: motivational, socio- emotional, cognitive, metacognitive, and environmental (e.g., Blumenfeld, Marx, Soloway, & Krajcik, 1996; Järvenoja & Järvelä, 2009). Self-regulated learning theory posits that planning, especially having shared perceptions about the collaborative task, is critical in ameliorating group work challenges. Hence, the purpose of this study was to examine the effects of providing different types of planning supports in the form of awareness visualizations of group members’ task perceptions on reported challenges. Findings revealed dominant differences across support conditions. Individuals in the no visualization condition (a) rated planning as more problematic for their groups than individuals in either of the two visualization conditions, and (b) reported the degree of challenges in doing the task, checking progress, and engaging in group work to be strongly positively correlated with planning challenges, (c) reported more Time and Planning main challenges compared to Doing and Group work challenges, and (d) reported that planning strategies (adopted together as a team) were most effective for addressing planning challenges they encountered, followed by teamwork strategies which were less effective. In contrast, individuals belonging to groups who received one of the two planning visualization supports (a) reported both planning and teamwork strategies to be equally effective for addressing planning challenges, and (b) reported higher levels of success with their strategies than groups without a planning support. Findings attest to the need to support awareness of group processes for collaborative team planning.
Adaptive Regulation and Student Success
Selected Publications and Presentations:
Davis, S.K., Edwards, R., Hadwin, A.F., & Milford, T. (2017). Exploring Student Engagement to Understand a Trimodal Distribution of Student Achievement in an Undergraduate Learning to Learn Course. Paper presented at the Canadian Society for the Study of Education. Toronto, Canada.
Fredricks, Blumenfeld, and Paris (2004) outline three dimensions of student engagement (behavioural, emotional and cognitive) that could explain individual differences in course performance. Examining differences in student engagement allows us to further understand differences across student achievement. Prior research on the prediction of student achievement has divided students into two groups (pass or fail). Discovery of a trimodal distribution of final course grades in an undergraduate self-regulated learning course resulted in three performance groups: low, middle, and high. A logistic regression analysis using student engagement measures showed the low group demonstrated less behavioral engagement than the middle group and the middle group demonstrated less agentic engagement than the high group. These results support Reeve (2016)’s assertion that emotional engagement should be replaced with agentic engagement. These results suggest that students at different performance levels need to focus their efforts on different aspects of student engagement.
McCardle, L, Webster, E. A., Haffey, A., & Hadwin, A. F. (2015). Examining students’ self-set goals for self regulated learning: Goal properties and patterns, Studies in Higher Education, 1-17. http://dx.doi.org/10.1080/03075079.2015.1135117
Task-specific goals play a critical role in self-regulated learning, yet little research has examined students’ self-set goals for authentic study sessions. We propose high-quality goals that are useful for guiding task engagement and evaluating progress are specific about (a) time, (b) actions, (c) standards, and (d) content. In Study 1, we examined characteristics of students’ self-set goals. Five categories were created to describe students’ goals relative to the features of a high-quality goal. Students rarely included specific information regarding actions, standards, or content. In Study 2, we examined patterns of change in quality of self-set goals across a semester in which students were in a learning-to-learn course. Improvements in goal quality were either inconsistent or non-existent. Implications of vague goals for regulating learning are discussed.
Regulation of Motivation and Emotion
Selected Publications and Pressentations:
Bakhtiar, A., Webster, E. A., & Hadwin, A. F. (accepted). Regulation and socio-emotional interactions in a positive and a negative group climate.
Collaboration in an online environment can be a socially and emotionally demanding task. It requires group members to engage in a great deal of regulation, where favourable emotions need to be sustained for the group’s productive functioning. The purpose of this cross-case analysis was to examine the interplay of two groups’ regulatory processes, regulatory modes, and socio-emotional interactions that contribute to or are influenced by emotions and socio-emotional climate perceived in the group. Specifically, this study compared a group of 4 students unanimously reporting a positive climate to a group of 4 students unanimously reporting a negative climate after completing a 90-minute online text-based collaborative assignment. By drawing on two data channels (i.e., observed regulatory actions and socio-emotional interactions during collaboration and self-reported data about emotional beliefs and perceptions), four contrasting group features emerged: (a) incoming conditions served as a foundation for creating a positive collaborative experience, (b) regulation of emotions during initial planning, (c) negative emotions served as a constraint for shared adaptation in the face of a challenge, and (d) encouragement and motivational statements served as effective strategies for creating a positive climate. Implications for researching and supporting emotion regulation in collaborative learning are discussed.
Bakhtiar, A., Hadwin, A.F., Milford, T., & Fior, M. (2017). Self and team efficacy beliefs as predictors of collaborative task participation in a computer-supported collaborative learning environment. Paper presented at the Canadian Society for the Study of Education. Toronto, Canada.
Three efficacy beliefs may be important for examining task engagement in Computer Supported Collaborative Learning (CSCL) environments: self-efficacy, proxy efficacy, and collective efficacy (e.g., Wang & Hwang, 2012). In particular, regulating task engagement in CSCL requires learners to not only consider the degree to which they feel confident to collaborate on the task (self- efficacy), but also the degree to which they feel confident in other members’ ability (proxy efficacy) and the team’s ability as a whole (collective efficacy). In this study, we (a) focused our examination on self- and proxy efficacy and examined their influences on individual task participation during two online collaborative tasks, and (b) whether the links between efficacy and participation changed depending on the types of visualization supports provided during shared planning. Findings indicated that self-efficacy positively influence the amount of effort put towards the task, even beyond knowledge mastery. A path model described the importance of collaboration experience, not grades, in influencing self-efficacy and in turn individual participation in the next collaboration. Although high proxy efficacy is associated with reduced effort, this effect was removed with group experience. The differences in predictive strengths found between the two tasks, especially for proxy efficacy, showed the evolving nature of learners’ strategic approach from one task to another. Findings demonstrate the need to follow collaborating students across multiple learning sessions to understand the role each type of efficacy belief play in shaping task engagement in collaboration; therefore, develop a more effective CSCL tools to support that process.