Advances in Socially Shared Regulation in Computer Supported Collaborative Learning

• In Event: Advances in Self-Regulated Learning Research Using Learning Technologies: A Synthesis of the Empirical Evidence

Abstract

Objectives. Due to regulatory challenges in computer supported collaborative learning (CSCL) we have been working for developing tools, prompts and technologies for supporting socially shared regulation in collaborative learning. The guiding idea has been to promote students’ awareness of the targets that potentially could call for active regulation (see Järvelä et al., 2016). Various technologies have been used both for supporting collaboration, but also collecting data about temporal process of collaboration (Järvelä et al., 2019).
Theoretical framework. We have advanced a theory of regulated learning in social learning contexts, namely socially shared regulation (SSRL) and implemented multimodal methods to identify when, how and what makes regulation in collaborative learning functional (Järvelä et al., 2018). In the context of carrying out collaborative tasks, three types of regulation are thought to be required for achieving success: (a) self-regulated learning (SRL) in which group members take control of their own thinking, behaviour, motivation and emotion in the collaborative task, (b) coregulated learning (CoRL) in which group members provide transitional support, facilitating one another’s engagement in self-regulatory processes within the task and (c) socially shared regulation of learning (SSRL) in which group members work together to regulate their cognition, motivation and affect in a synchronised and productive manner (Hadwin et al., 2017).
Methods and Results. During the past decade we have been carrying out empirical research in primary and secondary school science with multimodal data collection (Järvelä et al., 2019) to study SSRL in CSCL. Science (i.e., physics, chemistry, biology) has been chosen as a topic since it enables inquiry learning tasks with authentic learning challenges embedded with multiple opportunities for processes of cognition, motivation, and emotion along with their regulation. We have primarily focused on understanding internal conditions, operations, and metacognitive monitoring (Malmberg et al., 2019) in combination with the task and collaborative learning context standards. The internal conditions (Winne, 2017) are comprised of factors internal to the student or group, such as prior knowledge, motivation, and emotional climate (Järvenoja et al., 2019). The operations encompass social and socio-emotional interaction and actualized cognitive and affective strategies in an interaction (Dindar et al., 2020; Järvelä et al., 2016). I will report our findings dealing with social and socioemotional conditions in CSCL (Järvelä et al., 2016; Isohätälä et al., 2019), metacognitive monitoring and cognitive conditions fueling regulation in collaborative learning (Haataja et al., 2018; Sobocinski et al., 2020) and emotions as conditions and as products for SSRL (Järvenoja et al., 2018).
Significance. Currently we are working for sound conceptual understanding and empirical verification of an interplay of cognition, motivation and emotion in collaborative learning. After that, metrics for regulation can be developed for future AI implications supporting regulation in learning. Our aim is to target to the trigger events that are markers for regulation of cognition, motivation and emotion within collaborative groups and determine the critical signatures of these events that predict success or failure in individual and collaborative learning.

Author

• Sanna Järvelä, University of Oulu