Increasing Students' Social Engagement During Covid-19 With Net.Create: Collaborative Social Network Analysis To Map Historical Pandemics During A Pandemic

INFORMATION AND LEARNING SCIENCES(2020)

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摘要
Purpose - The authors explored shifts in social interactions, content engagement and history learning as students who were studying one pandemic simultaneously experienced another. This paper aims to understand how the Net.Create network visualization tool would support students as they tried to understand the many complex interactions in a historical text in a remote learning environment and how sustained knowledge building using Net.Create would shape student attitudes toward remote learning, collaboration and engagement.Design/methodology/approach - This paper explores changes in engagement and learning in a survey-level history course on the black death after a shift to remote learning during the COVID-19 pandemic. The authors used activity theory to focus the adaptation of Net.Create, a web-based collaborative social-network-analysis tool and to understand how it supported group-based remote learning. The authors describe how the redesigned activities sustained engagement with historical content and report coded student network entries, reading responses and surveys to illustrate changes in engagement and learning.Findings - The results suggest that students benefit from personal connections to historical content and their peers. Net.Create supported both through collaborative knowledge-building activities and reflection on how their quarantine experiences compared to the historical content they read. It is possible to avoid student frustrations with traditional "group work" even in a remote environment by supporting collaborative learning using Net.Create and a mix of individual and group contributions.Originality/value - This is the first use of a collaborative network visualization tool to support large classroom interaction and engagement with history content at the undergraduate level.
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关键词
Engagement, Online learning, Network analysis, Representations, Covid-19, Computer-supported collaborative learning, History learning
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