Abstract
There is increasing interest in text analysis based on unstructured data such as articles and comments, questions and answers. This is because they can be used to identify, evaluate, predict, and recommend features from unstructured text data, which is the opinion of people. The same holds true for TEL, where the MOOC service has evolved to automate debating, questioning and answering services based on the teaching-learning support system in order to generate question topics and to automatically classify the topics relevant to new questions based on question and answer data accumulated in the system. To that end, the present study proposes an LDA-based topic modeling. The proposed method enables the generation of a dictionary of question topics and the automatic classification of topics relevant to new questions.
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References
Cerulo, L., Distante, D.: Topic-driven semi-automatic reorganization of online discussion forums: a case study in an e-learning context. In: 2013 IEEE Global Engineering Education Conference (EDUCON). IEEE (2013)
Ezen-Can, A., et al.: Unsupervised modeling for understanding MOOC discussion forums: a learning analytics approach. In: Proceedings of the Fifth International Conference on Learning Analytics and Knowledge. ACM (2015)
Wang, G., et al.: Wisdom in the social crowd: an analysis of quora. In: Proceedings of the 22nd International Conference on World Wide Web. ACM (2013)
Hu, Y., et al.: Interactive topic modeling. Mach. Learn. 95(3), 423–469 (2014)
Yeon, L.S., Lee, K.M.: A reply graph-based social mining method with topic modeling. J. Korean Inst. Intell. Syst. 24(6), 640–645 (2014)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Park, J.D.: A study on mapping users’ topic interest for question routing for community-based Q&A service. J. Korean Soc. Inf. Manag. 32(3), 397–412 (2015)
Anoop, V.S., Asharaf, S., Deepak, P.: Learning concept hierarchies through probabilistic topic modeling. arXiv preprint, arXiv:1611.09573 (2016)
Lin, Y.-S., Jiang, J.-Y., Lee, S.-J.: A similarity measure for text classification and clustering. IEEE Trans. Knowl. Data Eng. 26(7), 1575–1590 (2014)
Sidorov, G., et al.: Soft similarity and soft cosine measure: similarity of features in vector space model. Computacióny Sistemas 18(3), 491–504 (2014)
Acknowledgments
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government MSIP) (No. 2016015499).
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Kim, K., Song, H.J., Moon, N. (2017). Topic Modeling for Learner Question and Answer Analytics. In: Park, J., Chen, SC., Raymond Choo, KK. (eds) Advanced Multimedia and Ubiquitous Engineering. FutureTech MUE 2017 2017. Lecture Notes in Electrical Engineering, vol 448. Springer, Singapore. https://doi.org/10.1007/978-981-10-5041-1_104
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DOI: https://doi.org/10.1007/978-981-10-5041-1_104
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