Abstract
Analysis of Massive Open Online Course (MOOC) forums data often use natural language processing (NLP) technology to extract keywords from discussions content as features. However, discussions in different course forums vary significantly, so the analyzed results obtained on these specific forums are not easily applied to other irrelevant forums. Besides, a lot of discussion threads are not related to the course in MOOCs forums. To address above problems, we analyze about 100,000 discussion threads from the forums of 60 MOOCs offered by Coursera, and design many features related to user interaction ways in different subforums. This work proposes a transferable framework to classify MOOC discussion threads using these features. The classification framework is not sensitive to the subjects and the forum discussions, so the classification model can be used directly by other course forums without being trained again. Experiments show that the average classification performance with Area Under ROC Curve (AUC) is 0.8. This work also gives the methods to remove noisy discussion threads and visulize the interactive characteristic of MOOC forum threads using dimensionality reduction technology.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jiang, Z., Zhang, Y., Liu, C.: Influence analysis by heterogeneous network in MOOC forums: what can we discover? In: International Educational Data Mining Society (2015)
Ravi, S., Kim, J.: Profiling student interactions in threaded discussions with speech act classifiers. Front. Artif. Intell. Appl. 158, 357 (2007)
Lin, F.R., Hsieh, L.S., Chuang, F.T.: Discovering genres of online discussion threads via text mining. J. Comput. Educ. 52, 481–495 (2009)
Hecking, T., Chounta, I.A., Hoppe, H.U.: Analysis of user roles and the emergence of themes in discussion forums. In: 2th Network Intelligence Conference, pp. 114–121. IEEE Press, New York (2015)
Atapattu, T., Falkner, K., Tarmazdi, H.: Topic-wise classification of MOOC discussions: a visual analytics approach. In: 9th International Conference on Educational Data Mining, pp. 276–281. ERIC Press, United States (2016)
Song, J., Zhang, Y., Duan, K.: TOLA: topic-oriented learning assistant based on cyber-physical system and big data. J. Future Gener. Comput. Syst. 75, 200–205 (2017)
Chaturvedi, S., Goldwasser, D., Daume III, H.: Predicting instructor’s intervention in MOOC forums. In: 52th Annual Meeting of the Association for Computational Linguistics, pp. 1501–1511. ACL Press, Baltimore (2014)
Rossi, L.A., Gnawali, O.: Language independent analysis and classification of discussion threads in Coursera MOOC forums. In: 15th IEEE International Conference on Information Reuse and Integration, pp. 654–661. IEEE Press, New York (2014)
Anonymized Coursera Discussion Threads Dataset. http://github.com/elleros/courseraforumsJuly2014
Liu, S., Feng, L., Qiao, H.: Scatter balance: an angle-based supervised dimensionality reduction. IEEE Trans. Neural Netw. Learn. Syst. 26, 277–289 (2015)
He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21, 1263–1284 (2009)
Acknowledgments
This work was supported by MOE Research Center for Online Education, and Foundation of LiaoNing Educational Committee (Grant no. 2016YB121, 201602151)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Feng, L., Liu, G., Luo, S., Liu, S. (2017). A Transferable Framework: Classification and Visualization of MOOC Discussion Threads. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_39
Download citation
DOI: https://doi.org/10.1007/978-3-319-70093-9_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70092-2
Online ISBN: 978-3-319-70093-9
eBook Packages: Computer ScienceComputer Science (R0)