Abstract
Correlating massive resources with knowledge points can help to achieve effective aggregation of resources and to improve learners learning efficiency and learning experience. This paper proposes a result integration mechanism based on the crowd wisdom to determine the association of learning resources and knowledge points, and ensure the final annotation result has certain credibility. Accordingly, we propose a user confidence to evaluate the user’s ability to complete the tasks. The experimental results show that the proposed algorithms improve the accuracy and efficiency comparing with the majority voting method, and algorithm to estimate user’s confidence can converge to actual value efficiently.
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Acknowledgments
This paper was supported by the specific funding for education science research by self-determined research funds of CCNU from the colleges’ basic research and operation of MOE (CCNU16JYKX004).
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Du, X., Zhang, F., Zhang, M., Xu, S., Liu, M. (2018). Research on Result Integration Mechanism Based on Crowd Wisdom to Achieve the Correlation of Resources and Knowledge Points. In: Wu, TT., Huang, YM., Shadiev, R., Lin, L., Starčič, A. (eds) Innovative Technologies and Learning. ICITL 2018. Lecture Notes in Computer Science(), vol 11003. Springer, Cham. https://doi.org/10.1007/978-3-319-99737-7_60
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DOI: https://doi.org/10.1007/978-3-319-99737-7_60
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