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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/102567

    Title: Automated generation of lectures and computation of influencing domains based on social learning environment
    Other Titles: 基於社群學習之自動化課程產生機制及影響力領域之計算
    Authors: 翁孟廷;Weng, Meng-Ting
    Contributors: 淡江大學資訊工程學系博士班
    Keywords: 社群學習;課程產生機制;影響力領域;自動化機制;Social learning;Lecture generation;Influencing domains;Automatic mechanism
    Date: 2014
    Issue Date: 2015-05-04 09:59:05 (UTC+8)
    Abstract: 近年來,在數位學習領域的相關研究已經如雨後春筍般地被提出以及探討了,但隨著科技時代的進步,仍然有許多數位學習相關的議題被提出且仍就未被完善的解決。舉例來說,在傳統數位學習中,學生只能在數位裝置上,從老師或助教中得到被指派的學習課程,但這些課程內容卻都是根據課程大綱進度或是老師安排的進度去安排的,而且大部分的內容幾乎都是在固定的課程大綱內的內容。但這樣形式的課程,只對於課內的教材或是比較被動的學生來說是有些許幫助的,而且這樣的幫助也是有限的,另外對於其他課外的課程或是比較主動學習的學生來說,可能喪失更多額外的學習機會。
    在本論文中,將會探討兩個在社群學習中的議題 (1)社群學習中的知識關係 (2)時間因素對於社群知識之影響。另外本研究也設計出兩個自動化機制來優化社群學習效率 (1)自動化課程產生機制 (2)影響力領域之計算。本研究也將這兩個自動化機制實做在ELGG社群平台上,並用此來做相關實驗。而研究結果也指出使用者的高滿意度及進步的學習成效。也因此,我們深信在未來的數位學習領域中,社群學習將扮演著很重要的角色。
    In recent years, the research in E-learning scope has been concerned for many years, but there are still have unsolved issues with the technology-center century in E-learning. For example, traditional E-learning only allows student to retrieve learning content from instructor or teach assistant. Otherwise, most of the learning content are pre-defined curriculum and a fixed knowledge domain. This type of learning content only have solid impact in class and for the students who are passive. With the population of social media (e.g., Facebook, Twitter, Google+ etc.) and mobile technology (e.g., smart phone, tablet) in recent years. Some sort of instant knowledge can be obtained by daily users with smart phone or PC, this kind of knowledge from social media can be called as social knowledge which can lead self-paced learning from social networks. This type of learning way can be called as Social Learning (or s-Learning). This thesis also points out two issues and in social learning: (1) knowledge for social learning, (2) time factors for social knowledge. Two significant automation mechanisms proposed: (1) automated generation of lectures, (2) computation of influencing domains. Those two mechanisms are proposed to facilitate the efficiency of social learning. Also, this research implemented proposed mechanisms based on a social networking engine named Elgg, with the support from a learning object repository that has stored and shared for more than twenty thousand records. In experiment parts, the questionnaire indicates the positive feedback of proposed algorithm on Elgg. With the results of experiment, we conclude that daily users may learn from instant social knowledge in the next era of e-learning.
    Appears in Collections:[資訊工程學系暨研究所] 學位論文

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