How Learning Process Data Can Inform Regulation in Collaborative Learning Practice

Abstract

Abstract

We have been working to understand when, how, and what makes regulation in collaborative learning functional aiming to understand the process of collaboration so that we could better inform learners and teachers in practice. Multimodal learning data collection and learning process analytics have guided our work. Seamless and accurate integration of multimodal learning data for measuring regulation in collaborative learning can be seen as a future direction of multimodal learning analytics (MMLA). Collaborative groups can be considered complex systems. The cognitive, emotional, motivational, and behavioural states of the group and its’ members are related to each other and in constant flux. Therefore, research has shown that regulation is a crucial process for making the maladaptive process of collaborative learning more adaptive. Regulation in collaborative learning involves groups taking metacognitive control of the task together through negotiated, iterative fine-tuning of internal and external conditions as needed. Metacognitive monitoring is always an internal mental process, but it can be externalized via visible interactions with other group members in collaborative situations. When aiming to support the adaptive collaborative learning process, we highlight two aspects: metacognitive awareness and participation in cognitive and socio-emotional interaction. In this chapter we present our recent empirical progress in these two aspects and discuss how learning process data and MMLA can be used to unravel regulation in collaborative learning and practical implications for collaborative learning.