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篇名 |
Student Model and Clustering Research on Personalized E-learning
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並列篇名 | Student Model and Clustering Research on Personalized E-learning |
作者 | Sheng Cao、Songdeng Niu、Guanghao Xiong、Xiaolin Qin、Pengfei Liu |
英文摘要 | As the internet and data mining technologies are developing rapidly, how to provide various students with high-quality education services has become the hotspot in the internet environment. In order to promote the characteristics of online education and enhance the quality of personalized e-learning, in this paper, we propose a novel algorithm named MK-means by exploiting the cluster-wise weighing co-association matrix mechanism and improving the K-means algorithm based on the mean shift theory. The experimental results on the UCI’s Iris and Wine test sets demonstrate its effectiveness and superiority, finding that the total Fmeasure of MK-means achieves better performance than the Hierarchical Clustering, FCM, K-means, SOM, and X-means algorithms. Finally, the new algorithm combined with the student model explains the clustering results in detail from perspectives of cognitive model and knowledge map respectively and can extend to support the personalized e-learning in a wide range. |
起訖頁 | 935-947 |
關鍵詞 | E-learning、Mean shift、K-means、CWWCA、Student model |
刊名 | 網際網路技術學刊 |
期數 | 202107 (22:4期) |
出版單位 | 台灣學術網路管理委員會 |
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| Hash Forest Structure Assisted Bi-auditing Protocol with Multiuser Modification in E-health Systems |