Community tracking in a cMOOC and nomadic learner behavior identification on a connectivist rhizomatic learning network

This article contributes to the literature on connectivism, connectivist MOOCs (cMOOCs) and rhizomatic learning by examining participant interactions, community formation and nomadic learner behavior in a particular cMOOC, #rhizo15, facilitated for 6 weeks by Dave Cormier. It further focuses on what we can learn by observing Twitter interactions particularly. As an explanatory mixed research design, Social Network Analysis and content analysis were employed for the purposes of the research. SNA is used at the macro, meso and micro levels, and content analysis of one week of the MOOC was conducted using the Community of Inquiry framework. The macro level analysis demonstrates that communities in a rhizomatic connectivist networks have chaotic relationships with other communities in different dimensions (clarified by use of hashtags of concurrent, past and future events). A key finding at the meso level was that as #rhizo15 progressed and number of active participants decreased, interaction increased in overall network. The micro level analysis further reveals that, though completely online, the nature of open online ecosystems are very convenient to facilitate the formation of community. The content analysis of week 3 tweets demonstrated that cognitive presence was the most frequently observed, while teaching presence (teaching behaviors of both facilitator and participants) was the lowest. This research recognizes the limitations of looking only at Twitter when #rhizo15 conversations occurred over multiple platforms frequented by overlapping but not identical groups of people. However, it provides a valuable partial perspective at the macro meso and micro levels that contribute to our understanding of community-building in cMOOCs

Wandering words: Tracing changes in words used by teacher tweeters over time

Public school teachers in the United States are often constrained in terms of their ability to express their moral views on issues that affect their schools, classrooms, students, and teaching practices, but are able to express their ideas, concerns, and frustrations as private citizens using social media. Previously we developed the Tweet Capture and Clustering System (TCCS) in order to explore how teachers use Twitter, looking at word usage among a group of teacher tweeters, and attempting to find clusters of teachers who have similar patterns of word usage in their tweets. In the work reported here, we look at teacher tweeters across the 12 months of 2016, seeking to understand how the clusters and the words used in these clusters vary from month to month. In this initial look at the dynamics of the system, we see some evidence of word usage changing across the 12-month period. This initial work suggests that extending TCCS to have temporal topic tracing as a core capability will be a meaningful addition to of the system. Copyright held by the author(s)

Educational Technology as Seen Through the Eyes of the Readers

In this paper, I present the evaluation of a novel knowledge domain visualization of educational technology. The interactive visualization is based on readership patterns in the online reference management system Mendeley. It comprises of 13 topic areas, spanning psychological, pedagogical, and methodological foundations, learning methods and technologies, and social and technological developments. The visualization was evaluated with (1) a qualitative comparison to knowledge domain visualizations based on citations, and (2) expert interviews. The results show that the co-readership visualization is a recent representation of pedagogical and psychological research in educational technology. Furthermore, the co-readership analysis covers more areas than comparable visualizations based on co-citation patterns. Areas related to computer science, however, are missing from the co-readership visualization and more research is needed to explore the interpretations of size and placement of research areas on the map.Comment: Forthcoming article in the International Journal of Technology Enhanced Learnin

An Investigation of Domain-based Social Influence on ChatGPT-Related Twitter Data

Recently, the word ChatGPT has made its way into the vocabulary of various fields that are important to society, including healthcare, education, governance, and robotics. This study examines the various reasons for social media influences using the Social Influence Theory. The study used tweets connected to ChatGPT collected from January 1st, 2023 to March 31st, 2023, totaling 402, 965 tweets. Initially, the key domains of ChatGPT discussions were identified along with the variations in the sentiments over the period. Subsequently drawing the literature related to Social Influence Theory, this study explored the relationship between subjective norm, social identity, user commitment, and features of the tweet content with social influence on the readership of a ChatGPT-related tweet. Subjective norm, identity, sentiment and domain are significant factors for social influence. The user commitment suggests a negative relationship with the social influence. Finally, the theoretical and practical implications are explained

Participant association and emergent curriculum in a MOOC: can the community be the curriculum?

We investigated how participants associated with each other and developed community in a Massive Open Online Course (MOOC) about Rhizomatic Learning (Rhizo14). We compared learner experiences in two social networking sites (SNSs), Facebook and Twitter. Our combination of thematic analysis of qualitative survey data with analysis of participant observation, activity data, archives and visualisation of SNS data enabled us to reach a deeper understanding of participant perspectives and explore SNS use. Community was present in the course title and understood differently by participants. In the absence of explanation or discussion about community early in the MOOC, a controversy between participants about course expectations emerged that created oppositional discourse. Fall off in activity in MOOCs is common and was evident in Rhizo14. As the course progressed, fewer participants were active in Facebook and some participants reported feelings of exclusion. Despite this, activity in Facebook increased overall. The top 10 most active participants were responsible for 47% of total activity. In the Rhizo14 MOOC, both community and curriculum were expected to emerge within the course. We suggest that there are tensions and even contradictions between ‘Community Is the Curriculum’ and Deleuze and Guattari's principles of the rhizome, mainly focussed on an absence of heterogeneity. These tensions may be exacerbated by SNSs that use algorithmic streams. We propose the use of networking approaches that enable negotiation and exchange to encourage heterogeneity rather than emergent definition of community

Learning Analytics Dashboard for Teaching with Twitter

As social media takes root in our society, more University instructors are incorporating platforms like Twitter into their classroom. However, few of the current Learning Analytics (LA) systems process social media data for instructional interventions and evaluation. As a result, instructors who are using social media cannot easily assess their students’ learning progress or use the data to adjust their lessons in real time. We surveyed 54 university instructors to better understand how they use social media in the classroom; we then used these results to design and evaluate our own Twitter-centric LA dashboard. The overarching goals for this project were to 1) assist instructors in determining whether their particular use of Twitter met their teaching objectives, and 2) help system designers navigate the nuance of designing LA dashboards for social media platforms