Diversity of interactions within connectivist learning context: insights from flow of collective attention

Abstract In connectivist learning environments, understanding the diverse interaction patterns of learners is essential for the effective design and implementation of online learning strategies. While traditional research has primarily focused on network analysis of peer-to-peer interactions, this study expands the scope by incorporating the often-overlooked yet pedagogically significant interactions that occur through content. By leveraging the open and flow network model of collective attention, the study offers a more robust and stable framework for understanding learner engagement, particularly in contexts where individual activity varies or learners disengage, which typically disrupts the structure of social networks. Using a cMOOC as a case study, the research identifies five distinct learner profiles: “Browsers”, “Likers”, “All-rounders”, “Commenters”, and “Sharers”, each exhibiting unique engagement patterns with resources such as Weekly Reports , Blogs , Materials , Cases , Forum Posts , Events , and the Problem-solving Hub . The prominence of “Browsers” as legitimate peripheral participants challenges the conventional assumption that active social interaction is essential for connectivist learning. Furthermore, the variations in attention dynamics across different learning resources suggest that a one-size-fits-all approach to course design is inadequate, as it fails to accommodate the diverse engagement patterns and needs of learners. Instead, this study advocates for a more nuanced approach to course design, one that integrates both social interactions and interactive content, thereby catering to a broader spectrum of learning preferences and optimizing engagement across the learner population.