Capturing regulatory patterns in online collaborative learning: A network analytic approach

doi:10.1007/s11412-021-09339-5

2021-03-04

PRE

DOI：10.1007/s11412-021-09339-5

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摘要

En 中文

Interest in understanding regulation in the context of collaborative learning has increased in the past decade. Existing studies have investigated how regulated learning evolves in collaborative learning by focusing on external behaviors, and how different types and strategies of regulation are effective in promoting collaborative learning. Due to the cyclical and dynamic characteristics of regulation, there is a need for new methods that can trace the dynamic emergence of regulatory processes in diver collaborative learning contexts, so as to provide some insight into effective learning design. In the context of 45 student teachers participating in multi-layered online collaborative activities, this study investigated their regulatory patterns during various stages of online collaborative learning activities over an eight-week semester via content analysis and epistemic network analysis (ENA). Quantitative analyses indicated that student teachers demonstrated active social aspects of regulation and had many regulatory behaviors in content monitoring in the designed online collaborative learning activities. Through identifying and comparing the regulatory patterns of the high-performing group and the low-performing group across the stages of learning activities, the results showed that the group demonstrating ample regulatory patterns in content monitoring, evaluating, and social emotional regulatory behavior performed better on the collective score of group product. Furthermore, the analysis elucidated how groups regulated their collaboration variously in different stages of online learning activities. Suggestions about regulated learning at both cognitive and social emotional aspects are provided to teachers and learning designers for designing and implementing online collaborative learning activities.

Keyword:

Regulation

Online collaborative learning

Student teachers

Epistemic network analysis