Bringing order to chaos in MOOC discussion forums with content-related thread identification

Abstract

This study addresses the issues of overload and chaos in MOOC discussion forums by developing a model to categorize and identify threads based on whether or not they are substantially related to the course content. Content-related posts were defined as those that give/seek help for the learning of course material and share/comment on relevant resources. A linguistic model was built based on manually-coded starting posts in threads from a statistics MOOC (n=837) and tested on thread starting posts from the second offering of the same course (n=304) and a different statistics course (n=298). The number of views and votes threads received were tested to see if they helped classification. Results showed that content-related posts in the statistics MOOC had distinct linguistic features which appeared to be unrelated to the subject-matter domain; the linguistic model demonstrated good cross-course reliability (all recall and precision > .77) and was useful across all time segments of the courses; number of views and votes were not helpful for classification.