@inproceedings{828b458383874307b6709c569e8e69ac,
title = "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.",
keywords = "Discussion forum, Machine learning, Massive open online courses, Natural language processing, Social interaction",
author = "Wise, {Alyssa Friend} and Yi Cui and Jovita Vytasek",
note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 6th International Conference on Learning Analytics and Knowledge, LAK 2016 ; Conference date: 25-04-2016 Through 29-04-2016",
year = "2016",
month = apr,
day = "25",
doi = "10.1145/2883851.2883916",
language = "English (US)",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "188--197",
booktitle = "LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact",
}