研究生: |
邱迪凱 Chiu, Ti-Kai |
---|---|
論文名稱: |
結合查詢擴展之學習元件個人化推薦系統 Personalized Recommendation with Query Expansion for SCORM Compliant Learning Objects |
指導教授: |
王宗一
Wang, Tzone-I |
學位類別: |
碩士 Master |
系所名稱: |
工學院 - 工程科學系 Department of Engineering Science |
論文出版年: | 2006 |
畢業學年度: | 94 |
語文別: | 中文 |
論文頁數: | 79 |
中文關鍵詞: | 數位學習 、推薦系統 、查詢擴展 |
外文關鍵詞: | e-Learning, recommender system, query expansion |
相關次數: | 點閱:101 下載:1 |
分享至: |
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
隨著網路的蓬勃發展,網路上互動式遠距教學的數位學習也成為未來學習的新趨勢。在數位學習中,數位課程是數位學習的核心之一,而數位課程是由多個學習單元或學習元件所組成,目前建構數位課程的主流規範為SCORM(Sharable Course Object Reference Model)。在不久的將來,大量相容於SCORM標準的學習元件將會存放在學習元件儲藏庫(Learning Object Repository),學習者將可輕易地在學習元件儲藏庫找到學習物件使用。
因此當學習者在面對如此大量的學習元件時將會不易於輸入符合本身意向的查詢語句或選擇適當、符合個人喜好的學習元件使用。所以本論文提出一結合查詢擴展之學習元件個人化推薦系統,提高學習者查詢語句的查準率(Precision)和加強學習者的學習效果,此機制利用學習者偏好及鄰居興趣的觀點推薦合適的學習元件讓學習者學習。
With vigorous development of Internet, especially the web page interaction technology, distant e-learning has become more and more realistic and popular. A Digital course may consist of many learning units or learning objects and, currently, many learning objects are created according to SCORM standard. It can be seen in the near future a vast amount of SCORM-compliant learning objects will be published and distributed cross many learning object repositories. Learners will more easily find learning objects they need in learning object repositories.
Currently, most of the learning objects retrieval systems just provide keyword-based search results with no personalized ranking. In the future facing the huge volume of learning objects, learners could probably be lost in searching, selecting suitable and favorite learning objects.
This thesis proposes an ontology-based query expansion mechanism to expand, according to his inferred intension, a learner’s query before submitting it for fetching learning objects and a personalized ranking mechanism to sort SCORM-compliant learning objects retrieved from a repository. The personalized ranking mechanism uses both a preference-based and a neighbor-interest-based approach in ranking the degree of relevance of learning objects retrieved to a user’s intension. If embedded with the mechanisms, a tutoring system will be able to provide easily and efficiently for active learners more suitable learning objects.
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