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# Introduction

Learning analytics as a nasent field of scholarship is evolving rapidly and garnering broad interest in both educational research and practice (Johnson 2011). Since the 2011 Learning Analytics and Knowledge (LAK) conference in Banff, Canada, exciting changes have happened during the past four years, including the launches of the Society of Learning Analytics Research (SoLAR) in 2012, Learning Analytics Summer Institute (LASI) in 2013, and the Journal of Learning Analytics in 2014. These events together indicate the establishment of learning analytics as an independent field of research and practice.

Since learning analytics as a field is still in its early stage, efforts have been made to understand the evolution of the field and its linkages with others. For instance, researchers have attempted to understand the similarities and distinctions between Learning Analytics and Educational Data Mining, which as two research communities share similar interests but grow seperately in their early years (Siemens 2012). Efforts have also been made to study the roots of learning analytics and its relations with fields including learning sciences, machine learning, and data-driven analytics (Ferguson 2012, Balacheff 2013). More recently, colleagues have made use of an open dataset—which contains structured metadata from research publications in the field of learning analytics and educational data mining (Taibi 2013, Proceedings of the LA...)1—and developed topic models (Sharkey 2014), ontology (Zouaq 2013), visualizations (Scheffel 2014), and knowledge systems (Lopes 2014, Hu 2014) to make sense of the field. These efforts uncover key themes of the field and identify major challenges faced by the community (Siemens 2012a).

In this article, we start with a brief introduction of Twitter and Twitter analytics in the context of academic conferences. Then we introduce the LAK Twitter dataset and analytic approaches applied in the study. After that, we present results from our analysis and discuss their implications for the field of learning analytics.

Twitter is also widely used at conferences, from its impressive “debut” at the 2007 South by Southwest Interactive (SXSWi) conference to almost every academic conference the authors have attended. At conferences, Twitter has been used to establish a backchannel to enable richer communication among attendees and extend conversations beyond the conference venue (Atkinson 2009). In a conventional academic conference setting, the space is divided into a “front” stage for the speaker and a large “back” area for the audience (Ross 2010). In this context, attention is solely focused to the front and interactions are usually limited. Because of the constrains posed by time and space, opportunities provided for the audience to interact with each other and to collectively construct understanding of a given speech are usually rare. As a result, the traditional conference model has a number of problems, including feedback lag, stress for asking questions, and participation decrease caused by the “single speaker paradigm” (Anderson 2008, Ebner 2009). Twitter could help close the gap of social interactions at conferences, thanks to its simplcity and the ubiquity of Internect connection. Conversationality and collaboration afforded by Twitter, especially through #LAK11 (Honey 2009), could help mitigate the disconnect among conference participants. It thus becomes not surprising that Twitter has become widely used at academic conferences.