An immediate analysis of the interaction topic approach to promoting group performance, knowledge convergence, cognitive engagement, and coregulation in online collaborative learning

Online collaborative learning (OCL) has been a mainstream pedagogy in the field of higher education. However, learners often produce off-topic information and engage less during online collaborative learning compared to other approaches. In addition, learners often cannot converge in knowledge, and they often do not know how to coregulate with peers. To cope with these problems, this study proposed an immediate analysis of interaction topics (IAIT) approach through deep learning technologies. The purpose of this study is to examine the effects of the IAIT approach on group performance, knowledge convergence, coregulation, and cognitive engagement in online collaborative learning. In total, 60 undergraduate students participated in this quasi-experimental study. They were assigned to either the experimental or the control groups. The students in the experimental groups conducted online collaborative learning with the IAIT approach, and the students in the control groups conducted online collaborative learning only without any particular approach. The whole study lasted for three months. Both qualitative and quantitative methods were adopted to analyze data. The results indicated that the IAIT approach significantly promoted group performance, knowledge convergence, coregulated behaviors, and cognitive engagement. The IAIT approach did not increase learners’ cognitive load. The results, together with the implications for teachers, practitioners and researchers, are also discussed in depth.