![]() |
|
以作者查詢圖書館館藏 、以作者查詢臺灣博碩士 、以作者查詢全國書目 、勘誤回報 、線上人數:37 、訪客IP:18.190.239.122
姓名 陳南光(Nan-kuang Chen) 查詢紙本館藏 畢業系所 資訊工程學系 論文名稱 依學生偏好及學習狀態建構之學習輔助者與知識協尋系統
(A Learning Helper and Knowledge Finding System Based on Student Preference and Student Model)相關論文 檔案 [Endnote RIS 格式]
[Bibtex 格式]
[相關文章]
[文章引用]
[完整記錄]
[館藏目錄]
[檢視]
[下載]
- 本電子論文使用權限為同意立即開放。
- 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
- 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。
摘要(中) 透過網際網路進行遠距教學,已是未來不可避免之趨勢。在非同步遠距教學的環境中,學生難以從龐大的全班學生作品資料庫中找到其所需的資訊;而教師面對學生更多的課業問題或請求,本身的負荷增加;現有的討論區依關鍵字搜尋文章系統,使學生找到一大群文章後,仍需要檢視許多篇文章後,才能得到他所需的相關資訊。本論文的目標便是建立一個知識協尋系統,由學生作品集資料庫中找出符合學生需求之文章,並建立一個學習輔助者協尋系統,為學生找到能給予幫助的專家同學;以及為自動回答系統改進搜尋文章結果的排列順序,讓學生可以花更少的時間得到符合其需求的資訊。
在主動推薦文章方面,我們根據學生對文章的偏好,從全班學生作品集資料庫中,採用k-Nearest Neighbor algorithm,找到和學生偏好相近的資訊,主動推薦給學生。
在找尋專家同學方面,我們根據學生對其他同學的偏好,以及學生之學習者模式,採用Radial Basis Function Network,找到與學生偏好相近的專家同學。
在改進搜尋文章排序方面,我們讓每篇文章的作者也成為在排序搜尋文章結果的依據:根據使用RBFN所找到專家同學的結果,給予每個專家同學一個Expert Weight,再加上原有向量空間模式的文章搜尋方式,來作為排序搜尋文章的依據。
本篇論文建立了一個依學生偏好及學生學習狀態之學習輔助者及知識協尋系統,此系統輔助非同步遠距教學環境中之學生在全班學生作品資料庫中篩選出文章品質較佳之文章;在學生提出課業疑難時,提供在此課程概念上能夠給予幫助之專家同學;並改良搜尋相關資訊結果之排序,使學生減少檢視文章次數和時間,使其獲得符合其需求之文章。摘要(英) Distance learning via Internet becomes more and more popular in recent years. In the asynchronous distance learning environment, students are hard to locate useful information from the large student knowledge database. Teachers’ load increases because of the increasing question from students. The result of searching documents by keyword make students need to examine many documents to get needed ones. Our goals are to establish an knowledge filtering system, find out the documents match a student’s need from the student knowledge database; and to establish a helper finding system, find a expert classmate for the student needs help; and to change the ranking of the searching result to make students reduce the number of times of examine documents.
Based on document feedback from the student, we recommend the student the information from the student knowledge database using k-Nearest Neighbor algorithm. Based on classmate feedback from the student, we recommend the student the expert classmates using Radial Basis Function Network. We take the author of a document into consideration when ranking the searching result.
We establish a learning helper and knowledge finding system based on students’ feedback and student model, it assists students in the asynchronous distance learning environment to find the high quality documents from the student knowledge database; when a student issue a question, it finds a suitable expert classmate to help this student; and it changes the ranking of the searching result to make students reduce the number of times of examine documents.關鍵字(中) ★ 資料探勘
★ 學生偏好
★ 學習輔助者
★ 遠距教學關鍵字(英) ★ student preference
★ distance learning
★ data mining
★ expert finding論文目次 摘要III
AbstractIV
誌謝V
目錄VI
圖形目錄IX
圖表目錄X
表格目錄XI
第一章 緒論1
1.1.研究背景與動機1
1.2.研究目標3
1.3.相關研究4
1.4.研究方法6
1.5.論文架構6
第二章 相關技術8
2.1.k-Nearest Neighbor Algorithm8
2.1.1.Distance-Weighted Nearest Neighbor Algorithm9
2.1.2.應用KNN步驟10
2.2.Radial Basis Function11
2.2.1.Linear Models11
2.2.2.Radial Functions12
2.2.3.Radial Basis Function Networks13
2.2.4.Training RBF Networks13
2.2.5.Least Squares14
2.2.6.The Optimal Weight Vector14
第三章 系統架構17
3.1.整體系統概觀17
3.2.學生作品資料庫19
3.3.討論區活動20
3.4.學習者模式22
3.5.本論文之系統角色定位23
第四章 我們的方法24
4.1.主動推薦文章24
4.1.1.選擇訓練資料集合25
4.1.2.決定距離函數26
4.1.3.決定最近鄰居個數29
4.1.4.決定結合函數29
4.2.個人化專家30
4.2.1.學生屬性定義32
4.2.2.Localization Problem34
4.3.改進搜尋相關文章之排序36
4.3.1.系統架構36
4.3.2.專家權重37
4.3.3.文章相符分數37
第五章 實驗設計與結果38
5.1實驗環境38
5.2主動推薦文章之評估38
5.2.1.學生平均文章品質評分之評估39
5.2.2.文章品質hit ratio之評估40
5.2.3.篩選過文章篇數之評估41
5.2.4.獲得推薦文章學生人數之評估42
5.3個人化專家之評估43
5.3.1.平均專家認同評分之評估44
5.3.2.平均專家個數之評估45
5.3.3.被推薦卅被認同學生次數分佈之評估46
5.4改進搜尋相關文章排序之評估47
5.4.1.符合學生需求文章篇數之評估47
5.4.2.Recall Effort之評估48
第六章 結論51
參考文獻53參考文獻 [1]Armstrong, R., D. Freitag, T. Joachims, and T. Mitchell, “WebWatcher: A learning apprentice for the world wide web,” in AAAI Spring Symposium on Information Gathering from Heterogeneous, Distributed Environments, March 1995.
[2]Belkin, N. J. and W. B. Croft, “Retrieval Techniques,” in Williams, M. (Ed.), Annual Review of Information Science and Technology, ed. M. Williams, pp. 109-145. New York: Elsevier Science Publishers, 1987.
[3]Berry, M. J. A. and Gordon Linoff, Data Mining Techniques — For Marketing, Sales, and Customer Support, John Wiley & Sons, New York, 1997.
[4]Billsus, D. and Michael J. Pazzani, “A Hybrid User Model for News Story Classification,” in Proc. of the 7th Int. Conf. on User Modeling, pp. 99-108, Banff, Canada, 20-24 June, 1999.
[5]Bishop, C. M., Neural networks for pattern recognition. Oxford, England: Oxford University Press, 1995.
[6]Broomhead, D. S., D. Lowe, “Multivariate functional interpolation and adaptive networks”. Complex Systems, 2, pp. 321-355, 1988.
[7]Burke et al., “Question Answering from Frequently-Asked Question Files: Experiences with the FAQ Finder System,” AI Magazine, 18, 2, 1997.
[8]Cover, T. and P. Hart, “Nearest neighbor pattern classification,” IEEE Transactions on Information Theory, 6, pp.21-27, 1967.
[9]Duda, R. and Hart, P., Pattern classification and scene analysis. New York: John Wiley & Sons, 1973.
[10]Foner, Leonard N., “Yenta: A Multi-Agent Referral System for Matchmaking,” in Proc. of the 1st Int. Conf. on Autonomous Agents, Marina Del Rey, CA, 1997.
[11]Kautz, H., Bart Selman, and Al Milewski, “Agent Amplified Communication,” in Proc. of the 13th National Conf. on Artificial Intelligence, pp. 3-9, Portland, OR, 1996.
[12]Pazzani, M., J. Muramatsu, and D. Billsus, “Syskill & Webert: Identifying interesting web sites,” in AAAI Conf., Portland, 1996.
[13]Salton, G., The SMART Retrieval System — Experiments in Automatic Document Processing. Englewood Cliffs, N.J.: Prentice Hall, 1971.
[14]Salton, G. and C. Buckley, “Term-Weighting Approaches in Automatic Text Retrieval,” Information Processing and Management, 24, 5, pp. 513-523, 1988.
[15]Salton, G. and M. E. Lesk, “Computer Evaluation of Indexing and Text Processing,” J. Association for Computing Machinery, 15, 1, pp. 8-36, 1968.
[16]Salton, G. and C. S. Yang, “On the Specification of Term Values in Automatic Indexing,” J. Documentation, 29, 4, pp. 351-372, 1973.
[17]Sparck Jones, K., “A Statistical Interpretation of Term Specificity and Its Application in Retrieval,” J. Documentation, 28, 1, pp. 11-20, 1972.
[18]Vivacqua , A. S., “Agents for Expertise Location,” in Proc. 1999 AAAI Spring Symposium on Intelligent Agents in Cyberspace, Technical Report SS-99-03, Stanford, CA, USA, March 1999.指導教授 陳國棟(Gwo-Dong Chen) 審核日期 2000-6-26 推文 plurk
funp
live
udn
HD
myshare
netvibes
friend
youpush
delicious
baidu
網路書籤 Google bookmarks
del.icio.us
hemidemi
myshare