Hanxiang Du

Background Social annotation has emerged as a promising educational technology that fosters collaborative reading and discussion of digital resources among learners. While the positive impact of social annotation on students' learning process and performance is widely acknowledged, students' behavioural patterns in social annotation are underexplored. Objectives This study investigated patterns in students' use of annotation and response behaviours in social annotation activities. We also explored how students' performance in the behavioural, cognitive, emotional, and social dimensions varied based on their behavioural patterns. Methods We recruited 93 undergraduates who were enrolled in an elective course at a large North American University. Students were tasked with collaboratively annotating the class readings uploaded to Perusall, a social annotation platform, over 7 weeks. We used metaclustering to determine the optimal number of clusters pertaining to student behaviours. We compared the differences among clusters across multiple performance dimensions. Results and Conclusions Two distinct clusters were identified and defined as initiators and responders. We found that responders had significantly longer active reading time and exhibited greater social annotation effort compared to initiators. However, initiators received more peer acknowledgement, as evidenced by higher upvotes. No significant difference was found in cognitive insight between initiators and responders, but responders demonstrated significantly higher cognitive discrepancy. Additionally, there were no significant differences in positive and negative tones between initiators and responders; however, responders displayed higher levels of prosocial behaviours than initiators. This study has significant practical implications regarding promoting students' collaborative learning experience in social annotation.

Online learning communities are becoming increasingly popular as they are known to support collaborative dialogue and knowledge building. Previous studies have typically focused on small, closed learning communities from an individual, static, and aggregated perspective. This research aims to advance our understanding of open and large online learning networks by exploring and characterizing community level dynamics and communities of performance. To achieve this goal, we mined a large open online learning network of over 30,000 students and approximately one million posts. First, we analyzed overall community network development by building temporal social networks. Subsequently, we studied sub-community dynamics using community detection algorithms, and, following that, investigated the interaction between community dynamics and communities of performance using best colleague correlation and Kruskal-Wallis test. Results found that large open online learning communities begin with a very large network having numerous small sub-communities. These communities consist of students who are similar in performance with strong links. The overall network size gradually shrinks, as does the number of sub-communities, and these communities evolve over time for their membership formulation with students who are more different in performance with weaker ties. Theoretical, practical, and methodological implications are then discussed. This study pushed the online learning community research to examine large and open networks by taking a more community-based and dynamic view of investigation.