  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 'confusion' 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
'individual' 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 one's post can indicate that many other students
have similar confusion regarding course topics, these models neglect the
personalised context, such as individual's 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.
