• 沒有找到結果。

Applying Data Mining Technology for the Relationship between the Preference of Course-Choosing and Academic Achievement 鄧景木、陳振東

N/A
N/A
Protected

Academic year: 2022

Share "Applying Data Mining Technology for the Relationship between the Preference of Course-Choosing and Academic Achievement 鄧景木、陳振東"

Copied!
2
0
0

加載中.... (立即查看全文)

全文

(1)

Applying Data Mining Technology for the Relationship between the Preference of Course-Choosing and Academic Achievement

鄧景木、陳振東

E-mail: 9800777@mail.dyu.edu.tw

ABSTRACT

Data mining is one of important research methods in recent years. It includes the statistics, artificial intelligence and database correlation methods. Among them, the as-sociation rule is the method to find the relationship between different data sets. It not only can identify a causal relationship between projects, but also can be used as a basis for further predictions.

Each university and college has accumulated huge and complete student databases, which may hide lots of undiscovered knowledge, information systems can provide sta-tistical reports, but can not display the hidden information in the database. Therefore, the purpose of this study is to use the association rules method to mining the implicit information in the course and achievements of students in the database. At the same time, a case study is implemented in this study to show the association rules in the course record. The results can not only understand relationship between the courses and achievements, and will offer the suggestion for school courses planning in the future.

Keywords : data mining、association rule、preference of course-choosing Table of Contents

中文摘要 ..................... iii 英文摘要 ..................... iv 誌謝辭  ..................... v 內容目錄 ..................... vi 表目錄  ..................... viii 圖目錄  ..................... x 第一章  ?論................... 1   第一節  研究背景與動機............ 1   第二節  研究目的............... 3   第三節  研究範圍與限制............ 4   第四節  研究流程............... 4 第二章  文獻探討................. 7   第一節  資料探勘定義............. 7   第二節  知識發掘............... 9   第三節  資料探勘技術............. 11   第四節  關聯法則............... 13   第五節  資料探勘應用於教育上之探討...... 16 第三章  研究方法與探勘模型設計.......... 22   第一節  資料探勘方法說明........... 22   第二節  結構化查詢語言............ 27   第三節  探勘資料來源............. 31   第四節  資料前置處理............. 33   第五節  探勘欄位定義............. 34   第六節  資料探勘使用的軟硬體環境....... 35 第四章  資料探勘模型建置與結果分析........ 38   第一節  建立關聯法則模型........... 38   第二節  探勘結果分析............. 45 第五章  結論與建議................ 69   第一節  結論................. 69   第二節  後續研究與建議............ 70

(2)

參考文獻 ..................... 72 REFERENCES

一、中文部份尹相志(2007),SQL Server 2005 Data Mining資料採礦與Office 2007資料採礦增益集,台北:精誠資訊股份有限公司。任書 鳴(2007),應用資料探勘技術於排課系統之研究,靜宜大學資訊管理學系未出版之碩士論文。朱克剛,夏延德(2002),由選課紀錄推斷課 程類別對選課者的重要性,中原學報,30(3),401-410。吳美玲(2004),成人參與高等回流教育決定因素之研究-以中正大學成教所碩士 在職專班為例,國立中正大學成人及繼續教育所未出版之碩士論文。林文義(2003),應用資料探勘技術進行高級職業教育課程規劃與學 生學習成效的分析-以南部某護理學校為例,樹德科技大學資訊管理研究所未出版之碩士論文。林金火(2007),資料探勘技術於中學生 成績之研究,立德管理學院應用資訊研究所未出版之碩士論文。夏天倫(2004),我國碩士在職專班回流教育成效之調查研究-研究生學 習動機、參與障礙及學習滿意度之探討,國立中山大學政治學研究所碩士在職專班未出版之碩士論文。曾守正,周韻寰(2005),資料庫 系統之理論與實務,台北:華泰文化事業股份有限公司。曾憲雄,蔡秀滿,蘇東興,曾秋蓉,王慶堯著(2006),資料探勘,台北:旗標出版 公司。曾龍譯(2003),資料採礦概念與技術,台北:維科圖書有限公司。黃雅蘭(2007),應用資料探勘技術於軍事院校學生成績分析之研究

-以國防大學管理學院為例,國防管理學院國防資訊研究所未出版之碩士論文。楊振銘(2007),資料探勘技術應用於中學生成績之研究

,立德管理學院應用資訊研究所未出版之碩士論文。楊淦淼(2002),設計與建立在多層式服務架構上的資料挖掘系統-以學生課程管理為 例,逢甲大學資訊工程所未出版之碩士論文。楊琇媛(2003),利用資料倉儲與資料探勘技術於招生策略與學生特質分析之研究,中原大 學資訊管理學系未出版之碩士論文。溫侑柯(2006),應用資料探勘之關聯法則探討大學入學成績對在學成績的影響-以資管系為例,南華 大學資訊管理研究所未出版之碩士論文。廖文瑜(2005),應用資料探勘技術分析學生特性與成績暨選課之關係-以一所科技大學資管系為 例,國立雲林科技大學資訊管理系碩士班未出版之碩士論文。劉佳灝,饒瑞佶(2006),學生選課組合與學習成效的資料探勘,建國科大 學報,26(2),1-18。樓玉玲(1998),以資料挖掘技術分析政大通識課程,國立政治大學資訊管理研究所未出版之碩士論文。鄭景邁(2007)

,資料探勘技術於學童補救教學之研究,立德管理學院應用資訊研究所未出版之碩士論文。蕭舜益(2005),運用關連法則探勘於初等教 育資料分析—以體適能為例,朝陽科技大學資訊管理研究所未出版之論文。謝邦昌(2005),資料採礦與商業智慧-SQL Server 2005,台北:

鼎茂圖書出版公司。顏博文(2003),應用資料探勘技術分析學生選課特性與學業表現,中原大學資訊管理學系未出版之碩士論文。羅子 文(2007),Web 2.0概念的圖書館個人化推薦系統,國立交通大學資訊管理研究所未出版之論文。羅才輝(2006),應用資料探勘技術分析 人格特質與選課之關係-以新竹地區大學生為例,中華大學資訊工程學系未出版之碩士論文。二、英文部份Agrawal, R., & Srikant, R.

(1994). Fast algorithm for mining association rule. In J. B. Bocca, M. Jarke, & C. Zaniolo (Eds.), Proceedings of the 20th International Conference on Very Large Databases (pp. 487-499), Chile: Morgan Kaufmann.Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large database. In P. Buneman & S. Jajodia (Eds.), Proceedings of the ACM SIGMOD Conference on Management of Data (pp. 207-216), Washington: ACM Press.Berry, M. J. A., & Linoff, G. (1997). Data Mining Techniques For Marketing, Sales, and Customer Suppor. New York: John Wiley & Sons.Berson, A., Smith, S., Thearling, K., & Building. (2001). Data Mining Application for CRM. New York:

McGraw-Hill Inc.Brin, S., Motwani, R., Ullman, J. D., & Tsur, S. (1997). Dynamic itemset counting and implication rules for market basket analysis. Proceedings of ACM SIGMOD International Conference on Management of Data (pp. 255-264), Tucson, Arizona: ACM Press.Fayyad, U. M. (1996). Data mining and knowledge discovery: making sense out of data. IEEE Expert, 11(5), 20-25.Fayyad, U. M., Shapiro, G. P., Smithy, P., & Uthurusamy, R. (1996). Advances in knowledge Discovery and Data Mining. Cambridge, MA:AAAI/MIT Press.Fayyad, U. M., Shapiro, G.

P., & Smyth, P. (1996). From Data Mining to Knowledge Discovery in Database. AI Magazine, 17(5), 37-54.Frawley, W. J., Piatetsky, S. G., &

Matheus, C. J. (1996). Knowledge Discovery in Databases: An Overview. Communications of the ACM, 39, 1-34.Fu, Y. (1997). Data Mining:

Tasks, Techniques and Applications. IEEE Potentials, 16(4), 18-20.Han, J., & Kamber, M. (2001). Data Mining: Concepts and Techniques. San Francisco: Morgan Kaufmann Publishers.Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. Proceedings of ACM SIGMOD International Conference on Management of Data (pp. 1-12), Dallas, Texas: ACM Press.Kleissner, C. (1998). Data Mining for the Enterprise, IEEE Proc, 7, 295-304. 31st Annual Hawaii International Conference on System Sciences.Megiddo, N., & Srikant, R. (1998).

Discovering predictive association rules. Proceedings of the fourth international conference on knowledge discovery and data mining (pp. 274-278), New York: AAAI Press.Peacock, P. R. (1998). Data Mining in Marketing:Part 1. Marketing Management, 6(4), 8-18.Scott, N. (2006). The basis for bibliomining: Frameworks for bringing together usage-based data mining and bibliometrics through data warehousing in digital library services.

Information Processing & Management, 42, 785-804.

參考文獻

相關文件

Reading Task 6: Genre Structure and Language Features. • Now let’s look at how language features (e.g. sentence patterns) are connected to the structure

 Promote project learning, mathematical modeling, and problem-based learning to strengthen the ability to integrate and apply knowledge and skills, and make. calculated

Wang, Solving pseudomonotone variational inequalities and pseudocon- vex optimization problems using the projection neural network, IEEE Transactions on Neural Networks 17

volume suppressed mass: (TeV) 2 /M P ∼ 10 −4 eV → mm range can be experimentally tested for any number of extra dimensions - Light U(1) gauge bosons: no derivative couplings. =>

Define instead the imaginary.. potential, magnetic field, lattice…) Dirac-BdG Hamiltonian:. with small, and matrix

incapable to extract any quantities from QCD, nor to tackle the most interesting physics, namely, the spontaneously chiral symmetry breaking and the color confinement.. 

• Formation of massive primordial stars as origin of objects in the early universe. • Supernova explosions might be visible to the most

The difference resulted from the co- existence of two kinds of words in Buddhist scriptures a foreign words in which di- syllabic words are dominant, and most of them are the