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系統識別號 U0002-1709201113193200
中文論文名稱 學習元件評比暨動態週期性個人化推薦機制
英文論文名稱 An Evaluation and Dynamic Period Personalized Recommendation Mechanism for Learning Objects
校院名稱 淡江大學
系所名稱(中) 資訊工程學系碩士班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 99
學期 2
出版年 100
研究生中文姓名 黃俊維
研究生英文姓名 Chun-Wei Huang
學號 698410650
學位類別 碩士
語文別 中文
第二語文別 英文
口試日期 2011-07-27
論文頁數 84頁
口試委員 指導教授-趙榮耀
委員-趙榮耀
委員-王英宏
委員-王俊嘉
中文關鍵字 數位學習  資源檢索  社群理論  個人化推薦 
英文關鍵字 e-Learning  information receive  social theory  Personalized Recommendation 
學科別分類 學科別應用科學資訊工程
中文摘要 隨著網際網路的快速發展,數位學習的資源在網路中也持續遽增,使用者雖有更豐富的資源可以運用,且能利用檢索引擎便利得尋找資源,但卻常需要過濾龐大的檢索結果,才能找到其所真正需求的資源。因此本篇論文主要目的在於開發一個檢索服務,讓使用者能快速找到其所需求的資源,解決使用者在檢索數位學習資源時的困難。本篇論文首先設計一套檢索規則與介面,用來記錄資源的使用情況;接著利用社群理論,將較熱門、較受歡迎、較受好評的資源,視為較有價值的資源,檢索結果的排序將依照此資源價值的高低排序;最後,利用測量個人的使用情境與週期,將較貼近使用者使用情境的資源重新給予評分,使得最終檢索結果重新排序,達到個人化推薦的機制,使得使用者能更快速的找到其所需求的資源。
英文摘要 With the quick development of Internet, Digital learning resources in the network have continued dramatic increase. Although nowadays users can use increasingly abundant resources and powerful search engine to find resources conveniently, but they often need to filter out the huge searched results before finding the real need. Therefore, the main purpose of this paper is to develop a search service that enables users to quickly find the resources they need and to improve difficulties to retrieve e-learning resource.
In this paper, we first design retrieve rules and develop an interface to record the Service condition. Then, based on the social theory, we consider the more popular and more critically acclaimed resources are more valuable ones and sort the results in accordance with the level of the value. Finally, by measuring the individual situations and cycle of the user, we re-evaluate and re-sequence the resources so that the final re-ordered result will optimally meet the requirement of personalized recommendation and make users more quickly find the resources they need.
論文目次 第一章 緒論 1
1.1. 研究背景與動機 1
1.2. 研究目的 3
1.3. 論文架構 4
第二章 相關研究工作 5
2.1. 資訊檢索 5
2.2. 數位學習儲存庫 11
2.2.1. CORDRA 12
2.2.2. MINE Registry 13
2.3. 時間序列探勘 14
2.3.1. Landmark Model 15
2.3.2. Sliding Window Model 16
2.3.3. Time-Fading Model 17
2.3.4. Tilted-Time Window Model 17
2.4. 推薦與排名系統 18
2.4.1. Google Page Rank 18
2.4.2. Personal Preference Search Service (PPSS) 19
第三章 研究方法與系統架構分析 24
3.1. 開發概念 24
3.2. 研究方法 26
3.2.1. 設計學習元件檢索規則與檢索服務介面 26
3.2.2. 檢索流程、學習元件評估原則設計 28
3.2.2.1. 檢索流程設計 28
3.2.2.2. 學習元件評估原則設計 29
3.2.3. 學習元件個人化推薦暨排序重調方法 34
3.2.4. 連續檢索路徑建立機制 40
3.3. 系統架構與分析 42
3.3.1. 學習元件評估模組 46
3.3.2. 學習元件個人化推薦暨排序重調模組 48
3.4. 系統分析 49
第四章 系統實作 56
4.1. 開發工具與建置環境 56
4.2. 系統介面與操作介紹 57
第五章 結論與未來展望 65
5.1. 結論 65
5.2. 未來展望 66
參考文獻 69
附錄 英文論文 74

圖目錄
圖 1、系統與學習資源之關係圖 12
圖 2、LANDMARK MODEL 示意圖 16
圖 3、SLIDING WINDOW MODEL 示意圖 16
圖 4、TIME-FADING MODEL 示意圖 17
圖 5、TILTED-TIME WINDOW MODEL 示意圖 18
圖 6、檢索介面 28
圖 7、檢索流程圖 29
圖 8、YOUTUBE評分系統 31
圖 9、使用者情境示意圖 36
圖 10、SEARCH PATH 42
圖 11、系統架構圖 43
圖 12、JSON格式範例 47
圖 13、學習元件評估模組架構圖 48
圖 14、個人化推薦暨排序重調模組 49
圖 15、PRECISION RECALL CURVE 52
圖 16、系統評估PR值結果 52
圖 17、NIAP評估結果 55
圖 18、檢索系統首頁 59
圖 19、推薦字詞 59
圖 20、建議字詞 59
圖 21、檢索結果列表 61
圖 22、介面功能說明 61
圖 23、使用者登入的歡迎訊息 62
圖 24、使用者使用情況網頁 63
圖 25、個人化檢索結果與連續檢索路徑 64
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[3] Neil Y. Yen, Timothy K. Shih, and Louis R. Chap1 (2009) Repository and Search Based on Distance Learning Standards.
[4] ADL Technical Team, Authoring Tools Application Guidelines-Practical Examples and SCORM Implementation Suggestions Version 1.0,August 18,2003
[5] ADL Technical Team, Sharable Content Object Reference Model (SCORM) Version 1.3 Working Draft 1, Advanced Distributed Learning (ADL),October 22, 2003
[6] N. Vassiliadis, F. Kokoras, I. Vlahavas and D. Sampson, “An Intelligent Educational Metadata Repository”, in C. Leondes (Editor) Intelligent Systems: Technology and Applications, Vol. 4, Databases and Learning Systems, CRC Press, 2003
[7] Pasini, N., & Rehak, D. , “A Process Model for Applying Standards in Content Development”, Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2003 (pp. 541-544). Chesapeake, VA: AACE.
[8] Shinobu Hasegawa, Akihiro Kashihara and Jun ’ichi Toyoca, “ An e-Learning Library on the Web”, Proceeding of the ICCE02, 2002, Vol.2, pp.1281-1282
[9] William H. Blackmon, Daniel R. Rehak, “Customized Learning: A Web Services Approach”, Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2003 (pp. 6-9). Chesapeake, VA: AACE.
[10] ADL Technical Team, DOD Learning Repositories Use Cases Version 1.0, Oct. 23, 2003
[11] ADL Technical Team, Content Object Repository Discovery and Registration/Resolution Architecture, ADL 1’st International Plugfest, June 07, 2004
[12] Colin Holden and Academic ADL Co-Lab Staff, “What We Mean When We Say "Repositories" ”, The Academic ADL Co-Lab with support from The William and Flora Hewlett Foundation, July, 29, 2004
[13] Timothy K. Shih, Chin-Chen Chang, and H. W. Lin, "Reusability on Learning Object Repository," in Proceedings of the 5th International Conference on Web-based Learning (ICWL 2006), Penang, Malaysia, July 19-21, 2006, pp.203-214
[14] H. W. Lin, Mon-Tin Tzou, Timothy K. Shih, Chun-Chia Wang, and Li-Chieh Lin, "Metadata Wizard Design for Searchable and Reusable Repository," in Proceedings of the 2006 International Conference on SCORM (ICSCORM'2006), Taipei, Taiwan, January 17-19, 2006, pp.132-136
[15] S. Sun, S. Reilly, L. Lannom, Handle System Namespace and Service Definition (RFC 3651), http://www.ietf.org/rfc/rfc3651.txt
[16] B Sarwar, G Karypis, J Konstan and J Riedl (2001) Item-Based Collaborative Filtering Recommendation Algorithms. Proceedings of the 10th international conference on World Wide Web.
[17] Ochoa, X. and Duval, E., "Use of Contextualized Attention Metadata for Ranking and Recommending Learning Objects", in CAMA’06, Virginia, USA, November 11, 2006
[18] J.H Chang and W.S. Lee, "Finding Recent Frequent Itemsets Adaptively over Online Data Streams", in Proceedings of ACM SIGKDD, 2003, pp.487- 492.
[19] Y. Chen, G Dong, J. Han, B.W. Wah and J. Wang, "Multidimensional regression analysis of time-series data streams" in Proc. 2002 Int. Conf. Very Large Data Bases(VLDB’02), pp.323-324.
[20] R. David Lankes, “Trusting the Internet: New Approaches to Credibility Tools” in DigitalMedia, Youth, and Credibility, 2008, pp.101-122.
[21] BJ Fogg, Jonathan Marshall, Othman Laraki, Alex Osipovich, Chris Varma, Nicholas Fang, Jyoti Paul, Akshay Rangnekar, John Shon, Preeti Swani, Marissa Treinen, “What Makes Web Sites Credible? A Report on a Large Quantitative Study” in CHI '01 Proceedings of the SIGCHI conference on Human factors in computing systems, 2001.
[22] K Jarvelin, J Kekalainen (2000) IR evaluation methods for retrieving highly relevant documents. Proceedings of the 23rd annual international ACM SIGIR.
[23] http://www.google.com/support/webmasters/bin/answer.py?answer=1140194, Googel網站管理員工具說明.
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[25] Philipp Ka‥ rger, Daniel Olmedilla, Fabian Abel, Eelco Herder, and Wolf Siberski, “What Do You Prefer? Using Preferences to Enhance Learning Technology”, IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES, Vol. 1, No. 1, JANUARY-MARCH 2008
[26] Chen, Y., Dong, G., Han, J., Wah, B.W., Wang, J.: Multidimensional regression analysis of time-series data streams. In: Proc. 2002 Int. Conf. Very Large Data Bases(VLDB 2002)(2002)
[27] Golab, L., Ozsu, M.T.: Issues in Data Stream Management. In: Special Interest Group on Management Of Data (SIGMOD 2003), vol. 32(2). ACM, New York (2003)
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