教師資料查詢 | 類別: 會議論文 | 教師: 黃谷臣 Huang Ku-chen (瀏覽個人網頁)

標題:The best Learning Order Inference Based on Blue-Red Trees of Rule-Space Model for Social Network-Case in ITE Course
學年100
學期1
發表日期2011/11/30
作品名稱The best Learning Order Inference Based on Blue-Red Trees of Rule-Space Model for Social Network-Case in ITE Course
作品名稱(其他語言)
著者陳永輝; Chen, Yung-hui; 鄧有光; Deng, Lawrence Y.; 黃谷臣; Huang, Ku-chen
作品所屬單位淡江大學體育事務處
出版者Fukuoka Institute of Technology (FIT)
會議名稱
會議地點
摘要Network Learning is becoming increasingly popular today. It is getting important to develop adaptive learning by social network that can be applied in intelligent e-learning systems, and provide learners with efficient learning paths and learning orders for learning objects. Therefore, we use the Rule-Space Model to infer reasonable learning effects of Blue-Red trees and their definitions through analyzing all learning objects of courses within system. We can also define all part learning of sub-binary trees from a course and derive all learning paths from each part learning of sub-binary tree based on the premise that we had inferred nine learning groups of social network grouping algorithms. Most importantly, we can define the Relation Weight of every learning object associated with the other learning objects, and separately calculate the Confidence Level values of between two adjacent learning objects from all learning paths. And finally, we can find the optimal learning orders among all learning paths from a sub-binary tree in the case of ITE course.
關鍵字Rule-Space Model;Blue-Red tree;Relation weight;Confidence Leve;Learning Path
語言英文(美國)
收錄於EI
會議性質國際
校內研討會地點
研討會時間20111130~20111202
通訊作者
國別
公開徵稿
出版型式
出處the 13-th International Symposium on Multimedia Network Systems and Applications (MNSA-2011) in conjunction with the Third IEEE International Conference on Intelligent Networking and Collaborative Systems (IEEE INCos 2011), Fukuoka, Japan, pp.466-471
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