  Massive Open Online Courses (MOOCs) bring together thousands of people from
different geographies and demographic backgrounds -- but to date, little is
known about how they learn or communicate. We introduce a new content-analysed
MOOC dataset and use Bayesian Non-negative Matrix Factorization (BNMF) to
extract communities of learners based on the nature of their online forum
posts. We see that BNMF yields a superior probabilistic generative model for
online discussions when compared to other models, and that the communities it
learns are differentiated by their composite students' demographic and course
performance indicators. These findings suggest that computationally efficient
probabilistic generative modelling of MOOCs can reveal important insights for
educational researchers and practitioners and help to develop more intelligent
and responsive online learning environments.
