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

A Retrospective Clinical Study to Evaluate Treatment Outcomes of Vital Pulp Therapy with ProRootRTM Mineral Trioxide Aggregate, EndosequenceRTM Root Repair Material, and Biodentine RTM

The aim of this study was to evaluate success rates of vital pulp therapy cases completed exclusively by endodontic residents at West Virginia University School of Dentistry with 3 different bioactive calcium silicate cements. The materials used were ProRootRTM Mineral Trioxide Aggregate (MTA) white, EndosequenceRTM Root Repair Material (ERRM), and BiodentineRTM. Failures were also examined to observe trends toward failure associated with multiple factors.;All follow-up examinations included a clinical and radiographic evaluation, which included multiple examiners that read each radiograph. Associations between procedure failure rates and the factors of interest were examined through non-parametric tests due to the small number of failures relative to the overall sample size. Fisher\u27s exact tests were used to investigate associations between failure rate and each categorical factor. Wilcoxon rank sum tests were employed to assess associations between procedure failure rates and the continuous factors of patient age and follow-up time.;A total of 130 cases were completed by endodontic residents. Fifty cases were successfully recalled, and 41 cases met the inclusion criteria after a retrospective chart review. All cases were completed between 2010 and 2013. The age of patients ranged from 7-58 years with an average age of 14.3 years. The follow-up time for successful cases ranged from 160 to 1000 days with an average of 730 days. Failure follow-up ranged from 7-38 days with an average of 24 days. The overall success rate of the 41 cases was 87.8%. Those patients receiving ERRM materials had over twice the odds of failure compared to those patients receiving ProRootRTM MTA. (OR: 2.29 (0.32,16.51)). ERRM materials included both ERRM putty (8 patients) and ERRM syringeable (1 patient). Those patients with trauma-related procedures had over three times the odds of failure compared to those patients with caries/decay-related procedures. (OR: 3.22 (0.44, 23.65)). Also, one out of the four patients who received cotton and TriageRTM instead of immediate restoration were reported as failed cases. Nearly every patient with a failed procedure was older than the median age of patients that had a successful case. None of the factors examined were statistically significant.;Vital pulp therapy in this study had a success rate of 87.8% with an average of 730 days follow-up. While each of our conservative statistical tests did not indicate statistical significance, they are potentially clinically relevant. The factors of age, cases completed with ERRM, trauma vs. caries, and immediate restoration vs. temporizing should be examined in future studies

Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate

Terror attacks have been linked in part to online extremist content. Although tens of thousands of Islamist extremism supporters consume such content, they are a small fraction relative to peaceful Muslims. The efforts to contain the ever-evolving extremism on social media platforms have remained inadequate and mostly ineffective. Divergent extremist and mainstream contexts challenge machine interpretation, with a particular threat to the precision of classification algorithms. Our context-aware computational approach to the analysis of extremist content on Twitter breaks down this persuasion process into building blocks that acknowledge inherent ambiguity and sparsity that likely challenge both manual and automated classification. We model this process using a combination of three contextual dimensions -- religion, ideology, and hate -- each elucidating a degree of radicalization and highlighting independent features to render them computationally accessible. We utilize domain-specific knowledge resources for each of these contextual dimensions such as Qur'an for religion, the books of extremist ideologues and preachers for political ideology and a social media hate speech corpus for hate. Our study makes three contributions to reliable analysis: (i) Development of a computational approach rooted in the contextual dimensions of religion, ideology, and hate that reflects strategies employed by online Islamist extremist groups, (ii) An in-depth analysis of relevant tweet datasets with respect to these dimensions to exclude likely mislabeled users, and (iii) A framework for understanding online radicalization as a process to assist counter-programming. Given the potentially significant social impact, we evaluate the performance of our algorithms to minimize mislabeling, where our approach outperforms a competitive baseline by 10.2% in precision.Comment: 22 page

Freelance Language Teachers' Professional Development On ... And With ... And Through Twitter

In recent years more and more freelance teachers have been employed in higher education and in further education, and they often struggle with barriers to professional development. Freelance language teachers are understood to work within various (self-) employment situations, often across educational sectors. For these teachers, access to professional development can be particularly challenging. Previous research has suggested that teachers’ use of the social media platform Twitter could lead to effective professional development (Carpenter & Krutka, 2014) and foster the formation of community among language educators (Wesely, 2013; Lord & Lomicka, 2014). Twitter is an Internet platform which enables registered users to communicate via text messages (tweets). While phenomenological research approaches have provided valuable insight into human experiences and perceptions of Twitter for professional learning, they tend to overlook the relational, human and non-human complexities involved (with)in the enactment of human practices. Drawing on the Deleuzo-Guattarian concepts of rhizome, assemblage and becoming (Deleuze & Guattari, 1987), this doctoral research seeks to provide answers to questions concerning how language teachers’ professional development on…and with…and through Twitter works and what it produces. Research data included online narrative frame questionnaires, tweets and online participant interviews. Data enquiry involved the working(s)-together of situational maps (Clarke, 2005) and social network analysis (Newman, 2010). This research suggests professional development and language teaching can be conceived of as entangled practices within human and non-human assemblages, which have the capability to produce unpredictable becomings, rather than as two distinct elements of a binary relationship. Recommendations from this investigation aim to make language educators, language education providers and education policy aware of the relational workings of social media practices, and to provide concrete suggestions for actions that connect with existing practices and programmes to improve freelance language teachers’ professional development

Core-periphery or decentralized? Topological shifts of specialized information on Twitter

In this paper we investigate shifts in Twitter network topology resulting from the type of information being shared. We identified communities matching areas of agricultural expertise and measured the core-periphery centralization of network formations resulting from users sharing generic versus specialized information. We found that centralization increases when specialized information is shared and that the network adopts decentralized formations as conversations become more generic. The results are consistent with classical diffusion models positing that specialized information comes with greater centralization, but they also show that users favor decentralized formations, which can foster community cohesion, when spreading specialized information is secondary

Can Conversations on Reddit Forecast Future Economic Uncertainty? An Interpretable Machine Learning Approach

In recent years, social media has become an indispensable source of information through which public attitudes, opinions, and concerns can be studied and quantified. This paper proposes an interpretable machine learning framework for predicting the Equity Market-related Economic Uncertainty Index using features generated from a popular discussion forum on Reddit. Our framework consists of a series of custom preprocessing and analytics methods, including BERTopic for latent topic identification and regularized linear models. Using our framework, we evaluate explanatory models with different configurations over a large corpus of Reddit posts belonging to the personal finance category. Our analysis generates valuable insights about discussion topics on Reddit and their efficacy in accurately predicting future economic uncertainty. The study demonstrates the potential of using social media data and interpretable machine learning to inform economic forecasting research