For effective computer supported collaborative learning (CSCL), socially shared regulation of learning (SSRL) is necessary. To this end, this article extends the idea first posited by Järvelä and Hadwin (Educ Psychol 48(1):25-39, 2013) that successful collaboration in CSCL contexts requires targeted support for promoting individual selfregulatory skills and strategies, peer support, facilitation of self-regulatory competence within the group, and SSRL. These (meta)cognitive, social, motivational, and emotional aspects related to being/becoming aware of how one learns alone and with others are for the most part neglected in traditional CSCL support. Based upon a review of theoretical and empirical studies on the potential of and challenges to collaboration, three design principles for supporting SSRL are introduced: (1) increasing learner awareness of their own and others' learning processes, (2) supporting externalization of one's own and others' learning process and helping to share and interact, and (3) prompting acquisition and activation of regulatory processes. Finally, an illustrative example is presented for how these principles are applied in a technological tool for supporting SSRL.

Linking learning behavior analytics and learning science concepts: designing a learning analytics dashboard for feedback to support learning regulation. Computers in Human Behavior.

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Socially shared regulation of learning refers to processes by which group members regulate their collective activity. Successful individuals regulate their motivational, cognitive, and metacognitive engagement. Our hypothesis is that successful groups also share in regulating group processes. Following our earlier conceptual and empirical work on the social aspect of motivating and regulating learning (Hadwin & Järvelä, 2011; Järvenoja & Järvelä, 2009; Järvelä, Volet, & Järvenoja, 2010), our research questions are as follows: (a) What challenges do individuals and groups report experiencing during collaborative group work? (b) How do students collectively regulate these challenges at the time, and in future collaborations? (c) How do collaborative learning outcomes compare between groups with varying degrees of emerging shared regulation? We present an empirical study in which 18 graduate students worked in collaborative teams of 3–4 over an 8-week period. The nStudy (Winne, Hadwin, & Beaudoin, 2010) software was used for collaborative planning and work, as well as face-to-face and online collaboration between team members. Data included individual and collaborative statements about collaborative challenges, collaborative statements about contextual and future regulation strategies, collaborative learning performance, and log file traces of students’ contributions to collaborative chat discussions and planning activities. Findings indicated that the students expressed multiple challenges resulting in 3 kinds of regulation over time profiles: strong, progressive, and weak shared regulation. We also conclude that successful collaboration not only requires self-regulation but also allows each team member to support fellow team members to successfully regulate their learning and the team to come together to collectively regulate learning.