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系統識別號 U0002-1908201419482200
中文論文名稱 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
英文論文名稱 Automated Generation of Lectures and Computation of Influencing Domains Based on Social Learning Environment
校院名稱 淡江大學
系所名稱(中) 資訊工程學系博士班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 102
學期 2
出版年 103
研究生中文姓名 翁孟廷
研究生英文姓名 Meng-Ting Weng
學號 898410146
學位類別 博士
語文別 英文
口試日期 2014-06-23
論文頁數 84頁
口試委員 指導教授-趙榮耀
委員-施國琛
委員-許輝煌
委員-顏淑惠
委員-洪啟舜
委員-趙榮耀
中文關鍵字 社群學習  課程產生機制  影響力領域  自動化機制 
英文關鍵字 Social learning  Lecture generation  Influencing domains  Automatic mechanism 
學科別分類 學科別應用科學資訊工程
中文摘要 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。
隨著社群媒體的流行以及行動科技的發達,越來越多人喜歡在社群媒體上分享或討論很多即時訊息與知識,也因此很多人可也藉由社群媒體得到這些即時的知識與訊息。像這一類來自社群媒體平台上的知識與訊息,我們可以統稱之為「社群知識」,而這一類的社群知識更容易讓使用者去激勵自己在社群平台上自我學習的意識,並且藉由與其他社群媒體使用者的互動,來得到更多學習方面的競爭感和學習來源。而這樣的學習模式,我們也可以稱做是「社群學習」。
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。
英文摘要 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.
論文目次 Table of Content
Chapter 1 Introduction 1
1.1. Research Background 2
1.2. Research Purpose 7
Chapter 2 Related Works 9
2.1. Shift and Impact of Social Learning Phenomenon 10
2.2. Summary and Definition of Social Techniques in Education 12
2.3. Knowledge Sharing, Elicitation and Acquisition from Social Network 13
2.4. Intelligent Technologies for Social Learning 15
2.5. Recommendation System 16
Chapter 3 Modeling the Social Learning Relations 19
3.1. Relations among Users and Learning Objects 20
3.2. A Graph Definition Model 22
3.3. Adding Temporal Information 29
Chapter 4 The Automated Mechanisms 34
4.1. Lecture Generation 35
4.2. Computation of Influencing Domains 42
Chapter 5 The Implementation & Analysis of Experiment on Elgg 51
5.1. The implementation of proposed algorithm on Elgg social platform 52
5.2. Evaluation and Analysis of Implemented System 58
5.3. Performance on Empirical Usage of the Learning System 62
5.4. Case Study and Analysis 73
Chapter 6 Conclusion & Future Work 77
Reference 80

List of Figure
Figure 1. Illustration of Relation Categories in Social Learning 20
Figure 2. Illustration of temporal information 29
Figure 3. Illustration of Path Topology Generation 38
Figure 4. A Conceptual Illustration of the Example 40
Figure 5. Process of influencing domains computation 43
Figure 6. First step of influencing domains computation 44
Figure 7. Second step of influencing domains computation 45
Figure 8. Third step of influencing domains computation 45
Figure 9. Fourth step of influencing domains computation 47
Figure 10. Category of degree of influence 47
Figure 11. Fifth step of influencing domains computation 48
Figure 12. The main dashboard 53
Figure 13. Widget/Plug-in editing page 53
Figure 14. Object accessing 54
Figure 15. Relations among users 55
Figure 16. Lecture generation 55
Figure 17. Influencing domains computation 56
Figure 18. The flowchart of first experiment 63
Figure 19. The comparison of average using frequency per week 64
Figure 20. The comparison of average using time (hours) per week 64
Figure 21. The comparison of the average interaction times by each student 65
Figure 22. The comparison of the average download times of learning content by each student 65
Figure 23. The comparison of final exam by different score interval-1 65
Figure 24. The comparison of final exam by different score interval-2 66
Figure 25. Comparison between the pre-post tests 67
Figure 26. Results of behavior in “Browse System” 69
Figure 27. Results of behavior in “Browse Content” 70
Figure 28. Results of behavior in “Social Interaction” 71

List of Table
Table 1. Related Definition of E-learning and S-learning 3
Table 2. The characteristics on e-learning and social learning 4
Table 3. General criteria for evaluation 58
Table 4. Results of the questionnaire survey 59
Table 5. Questionnaire For The Social Learning System 60
Table 6. Aspects and factors for user behavior analysis 68
Table 7. Results with different inputs 73
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