Understanding the Roles of Personalization and Social Learning in a Language MOOC Through Learning Analytics

In the last decade, there has been a great deal of interest in language MOOCs (LMOOCs) and their potential to offer learning opportunities for large audiences, including those in disadvantaged communities. However, experiences and research have shown MOOCs to suffer from several challenges. Chief among these have been low participation and completion rates, which are often attributed to limitations in how opportunities for personalisation and social interaction are implemented. For the current study, a dedicated LMOOC was designed and implemented, called the “Social and Personal Online Language Course (SPOLC).” This language learning environment incorporates a recommendation system and emphasizes personalisation and social interaction. The study identified the types of learning behaviour that were related to course completion and observed how 270 learners in the LMOOC used the various course features. The data were collected using learning analytical methods and analysed using binary logistic regression and feature extraction prediction model. The results demonstrated that working in groups and creating a learning plan were important factors associated with course completion, while interacting with other learners online was not. We conclude with several suggestions and implications for future LMOOC design, implementation, and research.