資源的分享與知識的共建已成為了現今網際網路發展的趨勢，亦逐漸成為人類獲取知識的來源。換句話說，如何有效地將此類使用者內容（User Generated Content）加以利用成為了研究人員所致力的目標。因此，本團隊憑藉著過去系統開發之經驗，由數位學習（e-Learning）的角度切入， 結合了兩項主要研究課題： 網路資訊檢索與探勘（Web Information Retrieval/Mining）及社群運算（Social Computing），提出了此次「以社群知識探究為基礎之教、學路徑規劃暨內容安排之研究」。預計於國立中央大學與淡江大學兩校大學部間，以elgg 引擎建構一開放式的社群學習平台，除了增加學習者彼此間溝通與分享外，也提供了教師透過探究式教學策略之應用來培養學生獨立思考、組織與解決問題的能力之管道。此外，藉由參與者（教師、學生、資源）於其中之互動過程之分析，建構一社群知識網絡來量化社群使用經驗。再者，為了促使社群經驗之發掘與重複利用，本團隊亦於此平台中設計了一系列之檢索輔助服務，除了優化教師查找相關資源之過程外，更協助學習者從中探索他人之學習方式與思考模式。更具體的產出包含了根據教學大綱之分析來自動生成教學內容（教師端）與根據學習者歷程資訊探勘來生成個人化的學習路徑資訊。 Sharing resources and information has become the trend and goal for World Wide Web development. It also changes the way human beings acquire social knowledge. Thus, lots of researchers have devoted themselves into the related issue about streamlining the usage of UGC (User Generated Content). We continue the experiences on related e-learning systems development in the past, and propose the three-year project entitles: “Using Social Knowledge for Inquiry-based Lecture Arrangement and Learning Path Planning” based on three interesting research issues: e-Learning, Web Information Retrieval/Mining, and Social Computing, found in international academic societies. We are trying to increase the opportunities of communication and sharing for students who are in-study within National Central University, Taiwan, and Tamkang University, Taiwan by adopting the ELGG social engine. This also makes learners cultivate his/her abilities of thinking, organizing, and problem solving through the usage of an Inquiry-based learning strategy. In addition, Social Knowledge Network can be constructed in accordance with the analysis of experiences of interactions from participants include instructor, learner, and resources. Furthermore, to facilitate the discovery and reuse of social experiences, a series of search mechanisms are proposed. It aims at not only the optimization of related resources retrieval by instructors but discovery of learning methods and thinking patterns from other learners. More specifically, our contributions include two major parts. They are intelligent lecture content generation through syllabus topology and personalization learning path organization through the mining of user portfolio and social experiences. 研究期間：10008 ~ 10107
