Exploring a Personal Social Knowledge Network (PSKN) to aid the observation of connectivist interaction for high‐ and low‐performing learners in connectivist massive open online courses

Abstract

This study adds a new perspective to the observations about connectivist interaction behavior in cMOOCs by extending the notion of network building from the perspective of individuals. We explore the possibility of building a learning network named Personal Social Knowledge Network (PSKN) to support in the monitoring of learning performance and interaction in cMOOCs. The sample in this study included 284 preservice teachers and their learning lasted approximately 12 weeks. Data were primarily gathered by PSKN graphs. The results revealed a correlation between connectivist interaction measured by the PSKN (including density structure) and learning performance. The results also revealed differences in connectivist interaction behavior and patterns, indicated by PSKN (densities and structures), for high-and low-performing learners in cMOOCs. The high-performing learners show deeper knowledge interaction and social communication in addition to simple knowledge sharing and social communication. Additionally, as time passed and the PSKN of high-performing learners extended further, their interaction behavior became more complex and their role had gradually changed from "learning" to "teaching" as well as from knowledge acceptance to knowledge creation in cMOOCs.can explain 33.3% of the variance in learning performance, however, other variables, such as friend number and friend degree indicated weak contribution of prediction (see Table 5).Q2: What are the differences in connectivist interaction behaviors observed by PSKN (density and structure) between high-and low-performing learners in cMOOCs? Supporting informationAdditional Supporting Information may be found in the online version of this article.

A HyFlex-Flipped Class in Action Learning: A Connectivist MOOC for Creative Problem-Solving

“…Some research studies attempted to intervene in the MOOCs classes with strategies such as selfregulation, peer groups, and machine-learning algorithms to optimize the intervention based on the learners' information for the effective implementation of MOOCs. The results remain slightly but not significantly higher than the average completion rate of no additional intervention (Andersen et al, 2018;Duan et al, 2019;Heutte et al, 2021;Wang et al, 2017).…”

“…When applying the MOOC and AL into the flipped system, the LMS supports the flexibility of learning to be blended either online, onsite, or on demand, providing essential learning resources, and integrated thinking tools, that are ready to open classrooms for people at work and students to meet in an actual complex world situation. As in a related study by Boshoff and Fernandes (2012), the learning system offers a supportive environment for problem- Boshoff & Fernandes, 2020;Duan et al, 2019;Hsia et al, 2021;Robertson & Heckroodt, 2022).…”

“…The interaction, which digitally occurs via a computer network, can be very dynamic or may fail, or there may be a loss of control for in-class students. The Times Higher Education critiqued xMOOC based on its nature of content delivery and e-learning structure, stating that this type of learning lacked creativity and active knowledge (Duan et al, 2019;Nazari et al, 2021;Wang et al, 2017).…”

“…Social construction brought students to negotiated and flavored interaction until gradually, they could work across space boundaries to make a social connection. Students found they built recognition by negotiating the meaning of their experiences across the open distributed network (Andersen et al, 2018;Duan et al, 2019;Otto et al, 2021;Wang et al, 2017).…”

Analyze dynamique de l'évolution du réseau social dans le cMOOC basé sur le mode RSiena

“…More current research is attending to learning environments and resources, theory analysis, interaction patterns, characteristics of learners, and activity design. In recent years, the static characteristics and relationships of networks have received growing research attention, such as the structure of the whole social network (Wang et al, 2018), the correlation between individual network status as well as other variables like academic performance and concept generation (Duan et al, 2019;Xu & Chen, 2019). Despite the extensive literature examining SNA, one issue that remains less explored is the dynamic evolution mechanism of the network, which may lead to the interpretation of how connectivist learning happened.…”