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.
On the other hand, Gillani and colleagues REF used the Bayesian nonnegative matrix factorization to extract communities (or groups) of learners, based on the nature of their contribution to a MOOC discussion forum.
Communication Communities in MOOCs
ArXiv
ArXiv
Computer Science Mathematics