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The substantial growth of online learning, in particular, Massively OpenOnline Courses (MOOCs), supports research into the development of better modelsfor effective learning. Learner 'confusion' is among one of the identifiedaspects which impacts the overall learning process, and ultimately, courseattrition. Confusion for a learner is an individual state of bewilderment anduncertainty of how to move forward. The majority of recent works neglect the'individual' factor and measure the influence of community-related aspects(e.g. votes, views) for confusion classification. While this is a usefulmeasure, as the popularity of one's post can indicate that many other studentshave similar confusion regarding course topics, these models neglect thepersonalised context, such as individual's affect or emotions. Certainphysiological aspects (e.g. facial expressions, heart rate) have been utilisedto classify confusion in small to medium classrooms. However, these techniquesare challenging to adopt to MOOCs. To bridge this gap, we propose an approachsolely based on language and discourse aspects of learners, which outperformsthe previous models. We contribute through the development of a novellinguistic feature set that is predictive for confusion classification. Wetrain the confusion classifier using one domain, successfully applying itacross other domains.

An Identification of Learners' Confusion through Language and Discourse Analysis