Massive Open Online Courses (MOOCs) have a great potential for sustainable education. Millions of learners annually enrol on MOOCs designed to meet the needs of an increasingly diverse and international student population. Participants’ backgrounds vary by factors including age, education, location, and first language. MOOC authors address consequent needs by ensuring courses are well-organised. Learning is structured into discrete steps, prioritising clear communication; video components incorporate subtitles. Variability in participants’ language abilities inevitably create barriers to learning, a problem most extreme for those studying in a language which is not their first. This paper investigates how to identify ESL participants and how best to predict factors associated with their course completion. This study proposes a novel method for automatically categorising (English as Primary and Official Language; English as Official but not Primary Language; and English as a second Language groups) 25,598 participants studying FutureLearn “Understanding Language: Learning and Teaching” MOOC using natural language processing. We compared algorithms’ performance when extracting discernible features in participants’ engagement. Engagement in discussions at the end of the first week is one of the strongest predictive features, while overall, learner behaviours in the first two weeks were identified as the most strongly predictive feature.

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Abstract. In this study, we aim to analyse English as a Second Language (ESL) and English as a First Language (EFL) MOOC participants' engagements in a MOOC. We aim to find out key points which directly effect learners' dropout and performance in MOOCs. We worked on a FutureLearn data which is provided by the University of Southampton. The course is Understanding Language: Learning and Teaching MOOC that was run between 2016-04-04 and 2016-05-02 is chosen for the analysis. According to the results, it is very challenging to identify who is a second language English speaker by using their location information. One of the important findings is that first language English speakers wrote longer comments. In order to identify strategies for ESL MOOC participants, which is one of the ultimate goal of our research, there is a need for much deeper analyses.