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.
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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.
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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
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DOI: https://doi.org/10.1007/978-3-319-55705-2_30
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