The impacts of the comprehensive learning analytics approach on learning performance in online collaborative learning - Zheng, Lanqin; Kinshuk; Fan, Yunchao; Long, Miaolang (Education and information technologies, 2023)
Online collaborative learning has been an effective pedagogy in the field of education. However, productive collaborative learning cannot occur spontaneously. Learners often have difficulties in collaborative knowledge building, group performance, coregulated behaviors, learning engagement, and social interaction. To promote productive collaborative learning, this study aims to propose and validate a comprehensive learning analytics approach in an online collaborative learning context. The comprehensive learning analytics can automatically construct knowledge graphs, analyze metacognitive learning engagement and social interaction and provide personalized feedback. A total of 90 college students participated in this study, and they were assigned to the experimental group and control group. The students in the experimental group conducted online collaborative learning with the comprehensive learning analytics approach, while the students in the control group conducted traditional online collaborative learning without any specific approach. The results indicated that the comprehensive learning analytics approach significantly improved collaborative knowledge building, group performance, coregulated behaviors, metacognitive learning engagement, and social interaction compared with traditional online collaborative learning. In this paper, the results of the study together with the implications are discussed.