Skip to main content
SpringerLink
Log in

Thread Structure Prediction for MOOC Discussion Forum

  • Conference paper
  • First Online:
Social Computing (ICYCSEE 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 624))

Abstract

Discussion forums are an indispensable interactive component for Massive Open Online Courses (MOOC). However, the organization of current discussion forums is not well-designed. Trouble-shooting threads are valuable for both learners and instructors, but they are drowned out in the forums with huge amounts of threads. This work first built a labeled data set for trouble-shooting thread structure prediction by crowdsourcing and then proposed methods for trouble-shooting thread detection and thread structure prediction on the data set. The output of this work can be used to spot trouble-shooting threads and show them along with structure tags in MOOC discussion forums.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Lori, B., David, P., Jennifer, D., Glenda, S., Ho, A., Seaton, D.T.: Studying learning in the worldwide classroom: Research into edx’s first mooc. Res. Pract. Assess. 8, 13–25 (2013)

    Google Scholar 

  2. Abel, F., Bittencourt, I.I., Henze, N., Krause, D., Vassileva, J.: A rule-based recommender system for online discussion forums. In: Nejdl, W., Kay, J., Pu, P., Herder, E. (eds.) AH 2008. LNCS, vol. 5149, pp. 12–21. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  3. Anaya, A.R., Boticario, J.G.: A data mining approach to reveal representative collaboration indicators in open collaboration frameworks. In: International Working Group on Educational Data Mining, pp. 210–219 (2009)

    Google Scholar 

  4. Dringus, L.P., Ellis, T.: Using data mining as a strategy for assessing asynchronous discussion forums. Comput. Educ. 45, 141–160 (2005)

    Article  Google Scholar 

  5. Lin, F.-R., Hsieh, L.-S., Chuang, F.-T.: Discovering genres of online discussion threads via text mining. Comput. Educ. 52, 481–495 (2009)

    Article  Google Scholar 

  6. Anderson, A., Huttenlocher, D., Kleinberg, J., Leskovec, J.: Engaging with massive online courses. In: Proceedings of the 23rd International World Wide Web Conference, pp. 687–698 (2014)

    Google Scholar 

  7. Wen, M., Yang, D., Rosé, C.: Linguistic reflections of student engagement in massive open online courses. In: Proceedings of the International Conference on Weblogs and Social Media (2014)

    Google Scholar 

  8. Ramesh, A., Goldwasser, D.: Modeling learner engagement in MOOCs using probabilistic soft logic. In: NIPS Workshop on Data Driven Education, pp. 1–7 (2013)

    Google Scholar 

  9. Stump, G.S., Deboer, J., Whittinghill, J., Breslow, L.: Development of a framework to classify MOOC discussion forum posts : methodology and challenges. In: NIPS Workshop on Data Driven Education, pp. 1–20 (2013)

    Google Scholar 

  10. Wang, H., Wang, C., Zhai, C., Han, J.: Learning online discussion structures by conditional random fields. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 435–444 (2011)

    Google Scholar 

  11. Wang, L., Lui, M., Kim, S.N., Nivre, J., Baldwin, T.: Predicting thread discourse structure over technical web forums. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 13–25 (2011)

    Google Scholar 

  12. Kim, S., Wang, L., Baldwin, T.: Tagging and linking web forum posts. In: Proceedings of the Fourteenth Conference on Computational Natural Language Learning, pp. 192–202 (2010)

    Google Scholar 

  13. Hong, L., Davison, B.D.: A classification-based approach to question answering in discussion boards. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009, pp. 171–178. ACM Press, New York (2009)

    Google Scholar 

  14. Cong, G., Wang, L., Lin, C.-Y., Song, Y.-I., Sun, Y.: Finding question-answer pairs from online forums. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 467–474. ACM Press, New York (2008)

    Google Scholar 

  15. McCallum, A., Mann, G., Druck, G.: Generalized expectation criteria (2007)

    Google Scholar 

Download references

Acknowledgment

This work is sponsored by Quanta Computers, Inc. under the Qmulus Project and National Natural Science Foundation of China (61572151 and 71573065).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

    Chengjie Sun & Lei Lin

  2. MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, 02139, USA

    Shang-wen Li

Authors
  1. Chengjie Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shang-wen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lei Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chengjie Sun .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology , Harbin, China

    Wanxiang Che

  2. Harbin Engineering University , Harbin, China

    Qilong Han

  3. Harbin Institute of Technology , Harbin, China

    Hongzhi Wang

  4. Northeast Forestry University , Harbin, China

    Weipeng Jing

  5. National University of Defense Technology , Changsha, China

    Shaoliang Peng

  6. Harbin Engineering University , Harbin, China

    Junyu Lin

  7. Harbin Univ. of Science and Technology , Harbin, China

    Guanglu Sun

  8. Harbin Univ. of Science and Technology , Harbin, China

    Xianhua Song

  9. Harbin Engineering University , Harbin, China

    Hongtao Song

  10. Harbin Sea of Clouds & Computer Tech. , Harbin, China

    Zeguang Lu

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Science+Business Media Singapore

About this paper

Cite this paper

Sun, C., Li, Sw., Lin, L. (2016). Thread Structure Prediction for MOOC Discussion Forum. In: Che, W., et al. Social Computing. ICYCSEE 2016. Communications in Computer and Information Science, vol 624. Springer, Singapore. https://doi.org/10.1007/978-981-10-2098-8_13

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-2098-8_13

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2097-1

  • Online ISBN: 978-981-10-2098-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation