As the field of learning analytics continues to mature, there is a corresponding evolution and sophistication of the associated analytical methods and techniques. In this regard social network analysis (SNA) has emerged as one of the cornerstones of learning analytics methodologies. However, despite the noted importance of social networks for facilitating the learning process, it remains unclear how and to what extent such network measures are associated with specific learning outcomes. Motivated by Simmel's theory of social interactions and building on the argument that social centrality does not always imply benefits, this study aimed to further contribute to the understanding of the association between students' social centrality and their academic performance. The study reveals that learning analytics research drawing on SNA should incorporate bothdescriptive and statistical methods to provide a more comprehensive and holistic understanding of a students' network position. In so doing researchers can undertake more nuanced and contextually salient inferences about learning in network settings. Specifically, we show how differences in the factors framing students' interactions within two instances of a MOOC affect the association between the three social network centrality measures (i.e., degree, closeness, and betweenness) and the final course outcome.

In a technology-fueled world, coding is an essential skill for young people. MOOCs (Massive Open Online Courses), which are free online courses available to a very large number of people, are an effective and increasingly popular option for teaching scientific topics to a worldwide audience. However, despite the large number of MOOCs available on computer science, there is a scarcity of coding-related MOOCs that are designed for children and teenagers. In this paper, we present a programming MOOC that was recently developed by The University of Edinburgh and Universidad ORT Uruguay for teenager high school students with no prior programming experience. The MOOC was collaboratively developed by the two teams, resulting in a shared instructional design but with a bilingual delivery: "Code Yourself" in English and "A Programar" in Spanish. In this paper, we describe the course design for a young audience and we discuss the international codevelopment of the course materials. Furthermore, we present results from its simultaneous bilingual delivery in spring 2015, where around 85000 students participated. Student surveys show encouraging results: more than 93% found that the course met or exceeded their expectations, and more than 90% stated that they plan to continue programming in the future.

Surgery is a highly critical and costly procedure, and there is an imperative need to improve the efficiency in surgical wards. Analyzing surgical patient flow and predicting cycle times of different peri-operative phases can help improve the scheduling and management of surgeries. In this paper, we propose a novel approach to mining temporal patterns of surgical patient flow with the use of Bayesian belief networks. We present and compare three classes of probabilistic models and we evaluate them with respect to predicting cycle times of individual phases of patient flow. The results of this study support previous work that surgical times are log-normally distributed. We also show that the inclusion of a clustering pre-processing step improves the performance of our models considerably.

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