papers AI Learner
The Github is limit! Click to go to the new site.

Tracing Forum Posts to MOOC Content using Topic Analysis

2019-04-15
Alexander William Wong, Ken Wong, Abram Hindle

Abstract

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.

Abstract (translated by Google)
URL

http://arxiv.org/abs/1904.07307

PDF

http://arxiv.org/pdf/1904.07307


Comments

Content