Communication Communities in MOOCs
This provides insights for educational researchers and practitioners to develop more responsive online learning environments, though it is incremental as it applies an existing method to a new dataset.
The authors tackled the problem of understanding learner communication in MOOCs by analyzing forum posts with Bayesian Non-negative Matrix Factorization, finding that it yields a superior probabilistic generative model and reveals communities correlated with demographics and performance.
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.