  Massive Open Online Courses are educational programs that are open and
accessible to a large number of people through the internet. To facilitate
learning, MOOC discussion forums exist where students and instructors
communicate questions, answers, and thoughts related to the course.
  The primary objective of this paper is to investigate tracing discussion
forum posts back to course lecture videos and readings using topic analysis. We
utilize both unsupervised and supervised variants of Latent Dirichlet
Allocation (LDA) to extract topics from course material and classify forum
posts. We validate our approach on posts bootstrapped from five Coursera
courses and determine that topic models can be used to map student discussion
posts back to the underlying course lecture or reading. Labeled LDA outperforms
unsupervised Hierarchical Dirichlet Process LDA and base LDA for our
traceability task. This research is useful as it provides an automated approach
for clustering student discussions by course material, enabling instructors to
quickly evaluate student misunderstanding of content and clarify materials
accordingly.
