Abstract
Recent years have witnessed a proliferation of large-scale online education platforms. However, the learning materials provided by online courses are still finite. In this paper, to expand the learning materials on MOOC platforms, we construct an expanded knowledge base named DKG. DKG combines priori knowledge from concept map with extended textual fragments collected from web sources. For the sake of DKG’s quality, we also propose a supervised method with four novel features to evaluate the quality of textual fragments. Finally, we conduct experiments on four online courses. The results show that our method can find good textual fragments efficiently and expand learning materials successfully.
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
Vigentini, L., Clayphan, A.: Exploring the function of discussion forums in moocs: comparing data mining and graph-based approaches. In: Proceedings of the Second International Workshop on Graph-Based Educational Data Mining (GEDM 2015). CEUR-WS (2015)
Liu, J., Jiang, L., Wu, Z., Zheng, Q., Qian, Y.: Mining learning-dependency between knowledge units from text. VLDB J. 20(3), 335–345 (2011)
Wellman, B.: Networks in the Global Village. JSTOR (1999)
Novak, J.D., Cañas, A.J.: The theory underlying concept maps and how to construct and use them (2008)
Navigli, R., Velardi, P., Gangemi, A.: Ontology learning and its application to automated terminology translation. IEEE Intell. Syst. 18(1), 22–31 (2003)
Ruiz-Sánchez, J.M., Valencia-Garcıa, R., Fernández-Breis, J.T., Martınez-Béjar, R., Compton, P.: An approach for incremental knowledge acquisition from text. Expert Syst. Appl. 25(1), 77–86 (2003)
Wang, S., Ororbia, A., Wu, Z., Williams, K., Liang, C., Pursel, B., Giles, C.L.: Using prerequisites to extract concept maps from textbooks. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 317–326. ACM (2016)
Tseng, S.-S., Sue, P.-C., Su, J.-M., Weng, J.-F., Tsai, W.-N.: A new approach for constructing the concept map. Comput. Educ. 49(3), 691–707 (2007)
Chen, N.-S., Wei, C.-W., Chen, H.-J., et al.: Mining e-learning domain concept map from academic articles. Comput. Educ. 50(3), 1009–1021 (2008)
Wang, X.-J., Tu, X., Feng, D., Zhang, L.: Ranking community answers by modeling question-answer relationships via analogical reasoning. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 179–186. ACM (2009)
Agichtein, E., Castillo, C., Donato, D., Gionis, A., Mishne, G.: Finding high-quality content in social media. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 183–194. ACM (2008)
Weimer, M., Gurevych, I., Mühlhäuser, M.: Automatically assessing the post quality in online discussions on software. In: Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions, pp. 125–128. Association for Computational Linguistics (2007)
Massoudi, K., Tsagkias, M., Rijke, M., Weerkamp, W.: Incorporating query expansion and quality indicators in searching microblog posts. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 362–367. Springer, Heidelberg (2011). doi:10.1007/978-3-642-20161-5_36
FitzGerald, N., Carenini, G., Murray, G., Joty, S.: Exploiting conversational features to detect high-quality blog comments. In: Butz, C., Lingras, P. (eds.) AI 2011. LNCS (LNAI), vol. 6657, pp. 122–127. Springer, Heidelberg (2011). doi:10.1007/978-3-642-21043-3_15
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)
Acknowledgments
We would like to thank the anonymous reviewers for their great efforts in improving the quality of the paper. The work was supported in part by the National Science Foundation of China under Grant Nos. 61672419, 61532004, 61532015, the National Key Research and Development Program of China under Grant No. 2016YFB1000903, the MOE Research Program for Online Education under Grant No. 2016YB166, the Fundamental Research Funds for the Central Universities.
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
Duan, H., Zheng, Y., Shi, L., Jin, C., Zeng, H., Liu, J. (2017). DKG: An Expanded Knowledge Base for Online Course. In: Bao, Z., Trajcevski, G., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10179. Springer, Cham. https://doi.org/10.1007/978-3-319-55705-2_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-55705-2_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55704-5
Online ISBN: 978-3-319-55705-2
eBook Packages: Computer ScienceComputer Science (R0)