Examining knowledge construction in three social interactive learning environments: a comparison of knowledge networks, social networks, and social knowledge networks

ABSTRACT

Knowledge construction can be facilitated through different types of social interactive environment. A knowledge network environment (KN) develops a knowledge-centered network visualizing the structure of collective knowledge. A social network environment (SN) creates a people-centered network visualizing the social relationships among people. Considering the connectedness of knowledge and people, this study moves the conversation forward by introducing an integrated environment of both knowledge- and people-centered network, a social knowledge network environment (SKN). The study aimed at comparing the three types of environments and investigated their effect on collaborative knowledge construction processes and individual learning outcomes. The quasi-experiment results indicated that these three environments differed in their effects on learners' knowledge construction levels, the sequential patterns, as well as individual learning outcomes. Learners in SN had a highest rate of social interaction than the learners in the other environments. KN learners had a higher rate of allocentric elaboration than SN learners. SKN learners showed a higher level of surface and elaborative application, reflection, and knowledge construction outcomes than KN and SN learners. The different roles of social interactions were further discussed across three environments.

KEYWORDS:

• Knowledge construction
• social interactive learning environments
• social networks
• knowledge networks
• social knowledge networks

Acknowledgements

The authors gratefully acknowledge the financial support provided by the National Natural Science Foundation of China (NSFC) Funded Project [grant number: 61907035] as well as the Fundamental Research Funds for the Central Universities, China [grant number: SWU2009208]. Also, the authors highly thank Professor Shengquan Yu and Advanced Innovation Center for Future Education at Beijing Normal University, China, for providing learning cell system (LCS) and supporting for this research.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China: [Grant Number 61907035]; Fundamental Research Funds for the Central Universities, China: [Grant Number SWU2009208].

Notes on contributors

JinJu Duan

Jinju Duan is a professor in School of Educational Technology, Faculty of Education at Southwest University in Chongqing, China. Her research interests include massive open online courses, interactive learning environments, and social learning.

Lin Lu

Lin Lu is a doctoral candidate in learning technologies in College of Education and Human Ecology at The Ohio State University, USA. Her efforts have been devoted to studying emerging issues in distance education such as learning analytics, self-regulated learning, and online collaborative learning.

Kui Xie

Kui Xie is Cyphert Distinguished Professor, Professor of Educational Psychology and Learning Technologies in Department of Educational Studies and Director of The Research Laboratory for Digital Learning at The Ohio State University, USA. His main research interests include teacher professional development, technology intervention and learning environment.