Network structures of in-service teachers’ collective knowledge construction: An SNA analysis of multiliteracies online course

Countries: 
Published: 
2021

Source: Innovations in Education and Teaching International, 58:1, 47-58

(Reviewed by the Portal Team)

Using the notions of Knowledge Society Network (KSN) and Collaborative Knowledge Networks (CKNs) as analytical frameworks, this study investigates the relationships between network structures and potentials for teacher participants’ collaborative knowledge construction in a 12-week online course with the goal of creating participatory multiliteracies practices.
The research questions were:
(a) How do teacher participants contribute to the discussions of multiliteracies theories and practices?; and
(b) Which network model does this online multiliteracies professional education course represent?

Method

Research context and participants
The current study investigated the teacher participants’ contributions and network models in the 12-week online multiliteracies professional education course at a public research university in Ontario, Canada.
Most participants were in-service teachers who wanted to learn new instructional methods and cultivate expertise in multiliteracies for various grade levels.
During the six modules of the course, an instructor generated three open-ended discussion questions, and group facilitators built reading summaries.
The course had one instructor and 18 teacher participants as graduate students.
As one of them dropped the course in Module 3; thus, this paper reports on 17 teacher participants.
The teacher participants explored recurring educational issues affecting literacy curriculum and pedagogy and why multiliteracies is imperative to interact with culturally and linguistically diverse students using technology.
They moved fluidly amongst learning communities through peer-to-peer support, group collaboration, and whole-class discussion that in turn impacted on individual critical reflection.
Three or four facilitators were assigned by the instructor in each module.

Data collection and analysis
The data source was the online discourse among the instructor and teacher participants. 15 discussion threads from Module 2 to 6 were collected.
The participants’ posted notes were downloaded and archived.
To investigate characteristics of the participants’ interactive dynamics (Marin & Wellman, 2011), each model was examined to calculate the total number of teacher participants, the total number of contributions (combining the total number of postings with the total number of comments), and the average contributions per person.
SNA tools, UCINET 6.666 (Borgatti, Everett, & Freeman, 2002) and NetDraw 2.166 (Borgatti, 2002; Borgatti et al., 2002), were employed to quantitatively and visually examine the social network features of the modules over time at community and individual levels.

Findings and discussion
The current study investigated the interpersonal and participatory interaction of the teacher participants and their idea interaction in the online multiliteracies professional education course and which network model the course represented by conducting SNA.
Overall, the network of the course was classified under the Intensive Participant Interaction Networks, which means high participant interaction and relatively low idea interaction although the learning community has a sustained idea growth over time (Hong et al., 2010).
Also, it followed the Collaborative Interest Networks (CINs) in which the participants shared common interests and topics, while newly contributing participants emerged every week (DiMaggio et al., 2009).
Analysing the teacher participants’ social and idea interactions through SNA enabled to track the development of the network structures.
The teacher participants’ high interpersonal interactions and inclusiveness value along with the increasing density and slightly decreasing degree centralisation confirmed that the teacher participants’ sustainably shared responsibility for collaborative knowledge construction.
Similarly, as the core/periphery analysis result of shared problem space showed, new contributors appeared as their discussion progressed while active contributors sustainably maintain their high participation throughout the modules, which is in line with the idea that learners switch their positions in the learning community (Laat, Lally, & Lipponen, 2007).
What is more interesting is that the teacher participants who were not facilitators in each module maintained their active participation even after they resigned from the core position.
Meanwhile, the correlation between the modules was not found in idea interaction, which is not surprising because of the module-based course structure in this study.
Reflecting about the teacher participants increasing engagement in their learning with low idea interaction (DiMaggio et al., 2009; Hong et al., 2010), the future online multiliteracies course should be designed to facilitate the teacher participants’ design mode that continuously creates and re-constructs their innovative knowledge collaboratively (Bereiter & Scardamalia, 2014; Scardamalia & Bereiter, 2017).
In order to improve the collective knowledge and design-mode thinking of the teacher participants, the course should provide them as members of a participatory learning community with securely shared space to foster both active collaboration and dynamic idea exchange (Hong et al., 2010).

References
Bereiter, C., & Scardamalia, M. (2014). Knowledge building and knowledge creation: One concept, two hills to climb. In S. C. Tan, H. J. So, & J. Yeo (Eds.), Knowledge creation in education (pp. 35–52). Singapore: Springer.
Borgatti, S. P. (2002). NetDraw software for network visualization. Lexington, KY: Analytic Technologies.
Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet 6 for windows: Software for social network analysis. Harvard, MA: Analytic Technologies.
DiMaggio, M., Gloor, P., & Passiante, G. (2009). Collaborative innovation networks, virtual communities, and geographical clustering. International Journal of Innovation and Regional Development, 1, 387–404.
Hong, H.-Y., Scardamalia, M., & Zhang, J. (2010). Knowledge Society Network (KSN): Toward a dynamic, sustained network for building knowledge. Canadian Journal of Learning and Technology, 36. Retrieved from https://www.cjlt.ca/index.php/cjlt/article/view/26372
Laat, M., Lally, V., & Lipponen, L. (2007). Investigating patterns of interaction in networked learning and computer-supported collaborative learning: A role for social network analysis. ComputerSupported Collaborative Learning, 2, 87–103.
Marin, A., & Wellman, B. (2011). Social network analysis: An introduction. In J. Scott & P. J. Carrington (Eds.), The Sage handbook of social network analysis (pp. 11–25). London: Sage.
Scardamalia, M., & Bereiter, C. (2017). Two modes of thinking in knowledge building. Revista Catalana De Pedagogia, 11, 61–83. 

Updated: May. 18, 2021
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