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

Alyssa Wise, Yi Cui, Jovita Vytasek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationLAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation
PublisherAssociation for Computing Machinery
Pages188-197
Number of pages10
Volume25-29-April-2016
ISBN (Electronic)9781450341905
DOIs
StatePublished - Apr 25 2016
Event6th International Conference on Learning Analytics and Knowledge, LAK 2016 - Edinburgh, United Kingdom
Duration: Apr 25 2016Apr 29 2016

Other

Other6th International Conference on Learning Analytics and Knowledge, LAK 2016
CountryUnited Kingdom
CityEdinburgh
Period4/25/164/29/16

Fingerprint

Linguistics
Chaos theory
Identification (control systems)
Statistics

Keywords

  • Discussion forum
  • Machine learning
  • Massive open online courses
  • Natural language processing
  • Social interaction

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Wise, A., Cui, Y., & Vytasek, J. (2016). Bringing order to chaos in MOOC discussion forums with content-related thread identification. In LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation (Vol. 25-29-April-2016, pp. 188-197). Association for Computing Machinery. https://doi.org/10.1145/2883851.2883916

Bringing order to chaos in MOOC discussion forums with content-related thread identification. / Wise, Alyssa; Cui, Yi; Vytasek, Jovita.

LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation. Vol. 25-29-April-2016 Association for Computing Machinery, 2016. p. 188-197.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wise, A, Cui, Y & Vytasek, J 2016, Bringing order to chaos in MOOC discussion forums with content-related thread identification. in LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation. vol. 25-29-April-2016, Association for Computing Machinery, pp. 188-197, 6th International Conference on Learning Analytics and Knowledge, LAK 2016, Edinburgh, United Kingdom, 4/25/16. https://doi.org/10.1145/2883851.2883916
Wise A, Cui Y, Vytasek J. Bringing order to chaos in MOOC discussion forums with content-related thread identification. In LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation. Vol. 25-29-April-2016. Association for Computing Machinery. 2016. p. 188-197 https://doi.org/10.1145/2883851.2883916
Wise, Alyssa ; Cui, Yi ; Vytasek, Jovita. / Bringing order to chaos in MOOC discussion forums with content-related thread identification. LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation. Vol. 25-29-April-2016 Association for Computing Machinery, 2016. pp. 188-197
@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 = "Alyssa Wise and Yi Cui and Jovita Vytasek",
year = "2016",
month = "4",
day = "25",
doi = "10.1145/2883851.2883916",
language = "English (US)",
volume = "25-29-April-2016",
pages = "188--197",
booktitle = "LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation",
publisher = "Association for Computing Machinery",

}

TY - GEN

T1 - Bringing order to chaos in MOOC discussion forums with content-related thread identification

AU - Wise, Alyssa

AU - Cui, Yi

AU - Vytasek, Jovita

PY - 2016/4/25

Y1 - 2016/4/25

N2 - 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.

AB - 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.

KW - Discussion forum

KW - Machine learning

KW - Massive open online courses

KW - Natural language processing

KW - Social interaction

UR - http://www.scopus.com/inward/record.url?scp=84976483565&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84976483565&partnerID=8YFLogxK

U2 - 10.1145/2883851.2883916

DO - 10.1145/2883851.2883916

M3 - Conference contribution

AN - SCOPUS:84976483565

VL - 25-29-April-2016

SP - 188

EP - 197

BT - LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation

PB - Association for Computing Machinery

ER -