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Reference to teachers as agents of change has become commonplace in the education literature, including change toward more inclusive practice in response to the changing demographic of schooling. Yet, little is known about how teacher agency relates to (1) their understanding of, and commitment to any given change agenda and (2) the institutional and social structures through which they are able to access knowledge and resources within and beyond their schools. This study combined social and epistemic network analysis to examine teachers’ understanding of change and their sense of agency as they use their social networks to mobilise support for furthering change that matters to them. Our study is the first to apply this learning analytic approach in a real setting context. We used theories of teacher agency and inclusive pedagogy to interpret teachers’ social interactions in light of the extent to which they seek to make a difference toward greater inclusion. We collected data with an online log completed by teachers and other staff in two schools in Sweden over 6 months. The findings suggest that teachers understanding of change is embedded in their day-to-day activities such as student support, lesson planning, improvement of programs, and working conditions. Teachers tend to exercise agency toward inclusion when they seek to support student learning and well-being. When teachers act as agents of change, their social networks are bigger, more diverse and more collaborative than in situations in which they act as role implementers. We discuss substantive and methodological implications of these findings.

In this article, we empirically validate Tinto's Student Integration model, in particular, the predictions the model makes regarding both students' academic outcomes and their dropout decisions. In doing so, we analyze three decades' worth of student enrollments at an Australian university and present a novel methodological approach using graph embedding techniques to capture both structural and neighborhood-based features of the co-enrollment network. In keeping with Tinto's model, we find that not only do these embedded representations of students' social network predict their final grade point average (GPA), but also are able to successfully classify students who dropout. Our results show that these embedded representations of a student's social network can achieve F1-scores of up to 0.79 when classifying dropout and explain up to 10% of the variance in student's final GPA. When controlling for a small set of covariates and variables common to the literature, this performance increases to 0.83 and 24%, respectively. Furthermore, the performance of these methods is robust to both changes in their parameterization and to corruption of the underlying social networks. Importantly, this implies that hyperparameters may be selected to reduce the computational demands of this method without loss of predictive power. The novelty of this method, and its ability to identify student dropout, merits further investigation to preemptively identify at-risk students.

Models and processes developed in learning analytics research are increasing in sophistication and predictive power. However, the ability to translate analytic findings to practice remains problematic. This study aims to address this issue by establishing a model of learner behaviour that is both predictive of student course performance, and easily interpreted by instructors. To achieve this aim, we analysed fine grained trace data (from 3 offerings of an undergraduate online course, N=1068) to establish a comprehensive set of behaviour indicators aligned with the course design. The identified behaviour patterns, which we refer to as observed study strategies, proved to be associated with the student course performance. By examining the observed strategies of high and low performers throughout the course, we identified prototypical pathways associated with course success and failure. The proposed model and approach offers valuable insights for the provision of process-oriented feedback early in the course, and thus can aid learners in developing their capacity to succeed online.

Learning in computer-mediated setting represents a complex, multidimensional process. This complexity calls for a comprehensive analytical approach that would allow for understanding of various dimensions of learner generated discourse and the structure of the underlying social interactions. Current research, however, primarily focuses on manual or, more recently, supervised methods for discourse analysis. Moreover, discourse and social structures are typically analyzed separately without the use of computational methods that can offer a holistic perspective. This paper proposes an approach that addresses these two challenges, first, by using an unsupervised machine learning approach to extract speech acts as representations of knowledge construction processes and finds transition probabilities between speech acts across different messages, and second, by integrating the use of discovered speech acts to explain the formation of social ties and predicting course outcomes. We extracted six categories of speech acts from messages exchanged in discussion forums of two MOOCs and each category corresponded to knowledge construction processes from well-established theoretical models. We further showed how measures derived from discourse analysis explained the ways how social ties were created that framed emerging social networks. Multiple regression models showed that the combined use of measures derived from discourse analysis and social ties predicted learning outcomes.

The capacity to foster interpersonal interactions in massive open online courses (MOOCs) has frequently been contested, particularly when learner interactions are limited to MOOC forums. The establishment of social presence-a perceived sense of somebody being present and "real"-is among the strategies to tackle the challenges of online learning and could be applied in MOOCs. Thus far, social presence in MOOCs has been under-researched. Studies that previously examined social presence in MOOCs did not account for the peculiar nature of open online learning. In contrast to the existing work, this study seeks to understand how learners perceive social presence, and the different nuances of social presence in diverse MOOC populations. In particular, we compare perceptions of social presence across the groups of learners with different patterns of forum participation in three edX MOOCs. The findings reveal substantial differences in how learners with varying forum activity perceive social presence. Perceptions of social presence also differed in courses with the varying volume of forum interaction and duration. Finally, learners with sustained forum activity generally reported higher social presence scores that included low affectivity and strong group cohesion perceptions. With this in mind, this study is significant because of the insights into brings to the current body of knowledge around social presence in MOOCs. The study's findings also raise questions about the effectiveness of transferring existing socio-constructivist constructs into the MOOC contexts., System Engineering