Title page for etd-0205113-140901


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URN etd-0205113-140901
Author Chih-Ming Wang
Author's Email Address kukowang@gmail.com
Statistics This thesis had been viewed 5566 times. Download 824 times.
Department Computer Science and Engineering
Year 2012
Semester 1
Degree Ph.D.
Type of Document
Language English
Title Collaborative Content Sharing and Matching Service on Cloud-based e-Learning Platform
Date of Defense 2013-01-10
Page Count 114
Keyword
  • sharing
  • Collaborative content
  • local search
  • cloud computing
  • matching
  • Abstract With the development of Web 2.0, the Internet users can be led to creating, collaborating, and sharing all kinds of information and content. Rich and diverse contents on the Internet promote the development of learning to a new direction. An innovative learning platform should be designed as a cross-platform towards information, learning, and services. In the past era of Web 1.0, the learning model was content-oriented, and the teaching materials were often created and uploaded to the learning system by teachers, while students passively received the knowledge by studying such materials. When suffering from problems in learning, students often sought the Internet resources for answers, such as search engines, forums, Wiki, YouTube and etc. Such searching behaviors were kinds of knowledge discovery and expansion. When the discovered contents could be shared in the learning system, students would no longer simply be provided knowledge by teachers, but cooperatively created it with peers.
    In this thesis, a collaborative content sharing and matching service (CCSMS) on cloud-based e-Learning platform is proposed. The service is user-oriented and developed with the technology of Web 2.0, which is expected to extend the contents by increasing the behaviors of content creation, sharing, and cooperation of students, enhancing the social network interaction, and integrating the cloud resources. More and more relevant contents are created and the usefulness is supportive of students’ learning. In addition, the platform supports computing resources for processing the large number of computing analyses generated by the proposed service for an efficient and stable environment.
    Advisory Committee
  • Sy-Yen Kuo - chair
  • Li-Ming Tseng - co-chair
  • Chung-Nan Lee - co-chair
  • Tzung-Pei Hong - co-chair
  • Wen-Shyong Hsieh - co-chair
  • Wei-Kuang Lai - co-chair
  • Tei-Wei Kuo - co-chair
  • Chu-Sing Yang - advisor
  • Ming-Chao Chiang - advisor
  • Files
  • etd-0205113-140901.pdf
  • Indicate in-campus at 0 year and off-campus access at 1 year.
    Date of Submission 2013-02-05

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