A recent phenomenon in the MOOC space has been the development of courses tailored to educators serving in K-12 settings. MOOCs, particularly as a form of educator professional development, face a number of challenges. Academics, as well as pundits from traditional and new media, have raised a number of concerns about MOOCs, including the lack of instructional and social supports. It is an assumption of this study that challenges arising form this problem of scale can be addressed by leveraging these massive numbers to develop robust online learning communities. This mixed-methods case study addresses critical gaps in the literature and issues of peer support in MOOCs through an examination of the characteristics, mechanisms, and outcomes of peer networks. Findings from this study demonstrate that even with technology as basic as a discussion forum, MOOCs can be leveraged to foster these networks and facilitate peersupported learning. Although this study was limited to two unique cases along the wide spectrum of MOOCs, the methods applied provide other researchers with an approach for better understanding the dynamic process of peer supported learning in MOOCs.

Massively Open Online Courses (MOOCs) have gained attention recently because of their great potential to reach learners. Substantial empirical study has focused on student persistence and their interactions with the course materials. However, most MOOCs include a rich textual dialogue forum, and these textual interactions are largely unexplored. Automatically understanding the nature of discussion forum posts holds great promise for providing adaptive support to individual students and to collaborative groups. This paper presents a study that applies unsupervised student understanding models originally developed for synchronous tutorial dialogue to MOOC forums. We use a clustering approach to group similar posts, compare the clusters with manual annotations by MOOC researchers, and further investigate clusters qualitatively. This paper constitutes a step toward applying unsupervised models to asynchronous communication, which can enable massive-scale automated discourse analysis and mining to better support students' learning.

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