An Identification of Learners' Confusion through Language and Discourse Analysis.

The substantial growth of online learning, in particular, Massively Open Online Courses (MOOCs), supports research into the development of better models for effective learning. Learner u0027confusionu0027 is among one of the identified aspects which impacts the overall learning process, and ultimately, course attrition. Confusion for a learner is an individual state of bewilderment and uncertainty of how to move forward. The majority of recent works neglect the u0027individualu0027 factor and measure the influence of community-related aspects (e.g. votes, views) for confusion classification. While this is a useful measure, as the popularity of oneu0027s post can indicate that many other students have similar confusion regarding course topics, these models neglect the personalised context, such as individualu0027s affect or emotions. Certain physiological aspects (e.g. facial expressions, heart rate) have been utilised to classify confusion in small to medium classrooms. However, these techniques are challenging to adopt to MOOCs. To bridge this gap, we propose an approach solely based on language and discourse aspects of learners, which outperforms the previous models. We contribute through the development of a novel linguistic feature set that is predictive for confusion classification. We train the confusion classifier using one domain, successfully applying it across other domains.