- C. Rosé. 2017. Expediting the cycle of data to intervention. Abstract: An important research problem in learning analytics is to expedite the cycle of data leading to the analysis of student needs and the improvement of student support. Current work in Learning Analytics and Educational Data Mining provides many examples where machine learning applied to learning relevant data produces models that can teach us about learning processes as well as trigger just-in-time interventions. Though the technology is still young, the time is ripe to look to the future where such learned models will enable personalised experiences to learners at a grand scale. In our own research, we have focused on learning through discussion (Howley & Rosé, in press). In this space, the subfield of Discourse Analytics (Buckingham-Shum, 2013; Buckingham-Shum, De Laat, De Liddo, Ferguson, & Whitelock, 2014; Rosé, in press) provides examples where models with high utility can be learned even over messy, relatively unstructured discourse data. The substantive problems that must be overcome to build effective, interpretable, and actionable models over Discourse data are the continuing focus of work in the area of Discourse Analytics. Beyond this, however, is the more focused, but nevertheless necessary issue of creating a data infrastructure and pipeline specifically targeting this rich but challenging data (Jo, Tomar, Ferschke, Rosé, & Gaesevic, 2016). The foundation of computational analytic work is representation of data. Much of our published work in analytics of collaboration in discussion has been focused on either chat data (Howley, Mayfield, & Rosé, 2013) or transcribed face-to-face discussion (Ai, Sionti, Wang, & Rosé, 2010; Clarke et al., 2013). These can both be represented in a simple, uniform, flat sequence of text segments, each contributed by one speaker. However, when expanding to learning in Massive Open Online Courses (MOOCs) or learning in other online contexts such as opensource communities, the form that the discussions may take becomes more diverse as they are embedded in a variety of platforms. They may even occur simultaneously through multiple separate streams. To that end we offer a publically available data infrastructure we call DiscourseDB, which enables translation of data from multiple streams into a common, integrated representation. The interface level representation is translated down into Discourses, with embedded Discourse Parts consisting of Contributions, which may be related to one another through Relations, and which are associated with content that can be associated with Annotations. This common representation enables combining data across communication streams and applying common modelling technologies. As a concrete example, consider connectivist Massive Open Online Courses (cMOOCs) that include environments like the competency-based learning platform ProSolo, featured in a recent edX MOOC called Data, Analytics, and Learning (DALMOOC) (Rosé et al., 2015). In these environments data is rich and heterogeneous. In ProSolo, for example, student behaviours formally within the environment include LEARNING: RESEARCH AND PRACTICE, 2017 VOL. 3, NO. 1, 59–62 http://dx.doi.org/10.1080/23735082.2017.1286140
