§ 瀏覽學位論文書目資料
  
系統識別號 U0002-2906201708493100
DOI 10.6846/TKU.2017.01040
論文名稱(中文) 資料探勘應用於英語補習教育之發展
論文名稱(英文) Data Mining for the Teaching Development in Shadow Education
第三語言論文名稱
校院名稱 淡江大學
系所名稱(中文) 管理科學學系企業經營碩士在職專班
系所名稱(英文) Executive Master's Program of Business Administration (EMBA) in Management Sciences
外國學位學校名稱
外國學位學院名稱
外國學位研究所名稱
學年度 105
學期 2
出版年 106
研究生(中文) 黃芷婕
研究生(英文) Zhi-Jie Huang
學號 704620292
學位類別 碩士
語言別 英文
第二語言別
口試日期 2017-06-09
論文頁數 64頁
口試委員 指導教授 - 廖述賢
委員 - 李旭華
委員 - 王瑞源
關鍵字(中) 補習班
客戶關係管理
教學課程
資料探勘
關鍵字(英) custormer relationship management
shadow education
curriculum
data mining
第三語言關鍵字
學科別分類
中文摘要
近年來,隨著台灣經濟不景氣,薪資倒退,國人為了維持基本生活品質加上對未來的不確定感增加導致生育的情況不減反增。在少子化的情況下除了幼托教育首當其衝,而兒童補教機構深受影響,但根據相關單位資訊統計資料結果顯示,台灣補教業不減反增,而補教業者在僧多粥少的情況下,本研究主要利用資料探勘技術來探討顧客滿意度及新課程開發.暑假課程開發之關聯來發展出新的模式新的市場.本研究以現行連鎖美語補習班家長為研究樣本,共發放1020份問卷,回收811份問卷.本研究透過資料探勘將顧客就讀原因,顧客對於該補習班優缺點看法,顧客滿意因素,成人課程主題偏好,兒童暑假課程主題進行分析,研究結果顯示顧客關係管理的改善與新主題課程的研發具有相關.此研究建議該補習班業者可開發親子共讀課程,該管理者也可藉此進一步了解顧客滿意因素如何影響其課程主題策略選用.
英文摘要
The last two decades have seen growing importance placed on research in shadow education. The field of the shadow education in Taiwan has undergone many fluctuation and shifts over the years. The high cost of living and the necessity for both parents to work has given rise to notion that children are an unwelcome border. Shadow education in Taiwan is having trouble to get students and that is not the only problem of the situation. Shadow education is getting more and more in current market. However, research which has empirically documented the link between data mining and shadow education is scant. Therefore, the aim of this article attempts to explore how parents feel about “E” institute of education and their preference of subject course are related. This research involved a survey; the sample focuses on parents whose children study English at “E” Learning Institutes across Taiwan. A total of 1,860 questionnaires were distributed and 811 effective questionnaires. The quantitative analysis of the questionnaires was conducted through clustering analysis and association rules of data mining. In order to indicate the customer relationship and preferences of parents between related. To conclude, this study may be of importance in explaining development of CRM and new curriculum create, as well as in providing, manager of the institute with a better understanding of how parents feel about the institute relate to their strategy use.
第三語言摘要
論文目次
Index
淡江大學研究生論文中文提要 I
Abstract II
List of Tables VI
List of Figures VII
Chapter 1.Introduction 1
Chapter 2.Literature review 4
2.1. Shadow education 4
2.2. Customer relationship management 6
2.2.1 CRM in education 7
2.3. Curriculum 8
2.4. Data Mining 9
2.4.1 Cluster analysis and K-means algorithm 9
2.4.2 Association rules 10
Chapter 3 The case firm 13
3.1. About “E” of the learning institute 13
3.2. Programs of “E” learning institute 13
3.2.1 Immersion 13
3.2.2 Elementary 13
Chapter 4 Methodology 15
4.1. Database 15
4.1.1 Research framework 15
4.1.2 System design 16
4.1.3 Conceptual database 17
4.1.4 Questionnaire design 17
4.2. Cluster analysis 22
4.2.1 Data mining for multi-subject course and customer adumbration 22
4.2.2 Customer similarity and segmentation 23
4.3. Association rules 27
4.3.1 Points for development of CRM analysis 27
4.3.2 New curriculum creation analysis 28
Chapter 5. Data mining and results 29
5.1. Pattern-1 points for development of CRM 29
5.2. Pattern-2 new curriculum creation 32
Chapter 6. Managerial implication 39
6.1. Points for development of CRM 39
6.1.1 The reason for choosing the institute 39
6.1.2 Strengths/weaknesses 39
6.1.3 Satisfaction/point of concern 40
6.2. New curriculum creation 45
6.2.1 Parent-child course subject preference/summer vacation English course subject preference 46
6.2.2 Elective student-course subject preference/elective parent-course subject preference 47
Chapter 7. Conclusion 52
REFERENCE 53
APPENDIX 1 Questionnaire 56



Table 1. Questionnaire statistics 18
Table 2. Questionnaire data 19
Table 3. Questionnaire results 22
Table 4. K-mean cluster and categories 26
Table 5. Cluster-1 points for development of CRM 30
Table 6. Cluster-2 points for development of CRM 31
Table 7. Cluster-3 points for development of CRM 31
Table 8. Cluster-1 new curriculum creation 36
Table 9. Cluster-2 new curriculum creation 36
Table 10. Cluster-3 new curriculum creation 37
Table 11. Points for development of CRM 45
Table 12. New curriculum creation 51



Figure 1. Research framework 15
Figure 2. System architecture 16
Figure 3. Conceptual database design: E-R model	21
Figure 4. K-means cluster analysis 23
Figure 5. K-mean cluster analysis 25
Figure 6. Cluster-1 points for development of CRM 32
Figure 7. Cluster-2 points for development of CRM 33
Figure 8. Cluster-3 points for development of CRM 34
Figure 9. Cluster-1 new curriculum creation 37
Figure 10. Cluster-2 new curriculum creation 38
Figure 11. Cluster-3 new curriculum creation 38
Figure 12 Points for development of CRM map 44
Figure 13. New curriculum creation map 50
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