| 研究生: |
許家誠 Hsu, Chia-Cheng |
|---|---|
| 論文名稱: |
利用蜜蜂群聚演算法於臉書發展多用途數位學習平台 Development of a Multi-purpose Digital Learning Platform on Facebook Using an Artificial Bee Colony Algorithm |
| 指導教授: |
黃悅民
Huang, Yueh-Min |
| 學位類別: |
博士 Doctor |
| 系所名稱: |
工學院 - 工程科學系 Department of Engineering Science |
| 論文出版年: | 2013 |
| 畢業學年度: | 101 |
| 語文別: | 英文 |
| 論文頁數: | 64 |
| 中文關鍵詞: | 社群網路網站 、臉書 、合作學習 、推薦系統 、蜜蜂群聚演算法 |
| 外文關鍵詞: | Social networking site, Facebook, collaborative learning, recommendation system, artificial bee colony bee algorithm |
| 相關次數: | 點閱:100 下載:1 |
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目前,社群網站已經受到很多的關注,社群網站提供一個個人化的資訊傳播平台分享個人訊息給朋友。一些社群網站,例如:推特、Google+、臉書等等,吸引大量的使用者分享資訊與互相討論。臉書是目前世界上最受歡迎的社交網站,臉書提供一個互動式平台讓使用者可以聯繫朋友與其他社群團體,並且可以發表大量的照片、影片與連結網站。進一步,臉書的使用者可以從各種不同的管道獲得大量資訊去建構他們的知識。一些研究探查臉書的使用經驗與強化他們社交互動的有效性,特別是在教育領域。然而,目前僅少量研究開發數位化學習平台去連接社群網站的一些特點與功能,例如,追蹤學習者的學習經歷或是個人化輪廓,以幫助學生提升他們的學習成效。本研究提出一個結合臉書社群網站之多用途的數位化學習平台在一些特定功能的數位學習推薦系統,根據學習者的個人需求在其學習過程中,尋找出適合的學習教材、學習夥伴或群組成員。在這個數位學習系統中,一個蜜蜂群聚演算法被用來優化這些提出的推薦方法,而我們也透過系統模擬與實驗研究進一步評估所提出系統的實施成效。實驗結果指出,此系統的實施成效確實較傳統方法為佳,並且提升了整體的執行效率與準確性。
Social networking sites, which provide a platform for sharing information with friends, have recently received a lot of attention from both users and academics. Facebook is currently the most popular social networking site in the world, and enables users to keep in contact with friends and other social groups, as well as share photos, videos, and links. Although several studies have investigated the use of Facebook for educational purposes, few have developed e-learning platforms that connect some of the features or functions of social networking sites, such as tracking learning experiences or producing personalized profiles, to help students enhance their learning. This study thus proposes a multi-purpose e-learning platform for use with Facebook, which includes some specific functional e-learning recommendation functions that can meet the needs of individual learners with regard to searching for suitable learning materials, learning companions, or group members. An artificial bee colony algorithm is used to optimize these recommendation functions on the e-learning system, and the system is tested using some experiments. The results indicate that the approach proposed in this work is superior to other schemes, and can improve both the effectiveness and accuracy of the e-learning system.
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校內:2018-03-28公開