臺灣博碩士論文加值系統

數位學習的發展已成為網路應用的重要領域之一，而數位學習的課程也因e-Learning的進步而有愈來愈多的趨勢，除了課程的增加外，課程的學習方式也因學習者需求而有所改變，不再只有傳統流水式(Flow)的學習方式，目錄式(Choice)學習方式可依學習者個人需求而做跳躍課程的學習。然而，隨著e-learning系統中的課程愈來愈多，學習者卻又不知該從何學起才能更有效率。基於此，本研究在e-Learning系統平台上建構一推薦系統，透過Web Mining的技術做推薦推薦有效的學習路徑給學習者，讓學習者在e-Learning系統可做有效率的學習。

Today the amount of e-Learning information is incredible. To find out the learning information from searching is an annoying and tedious task. Moreover, if only applying chasing hyperlinks to find the relevant information of learning profile, it may be daunting. In order to overcome such a problem, the recommendation learning system is required. The user’s learning route is given and then provides the relevant learners useful messages through dynamically searching for the appropriate learning profile. The recommendation learning system, which is based on e-Learning system, provides an effective learning profile for learners. The learner can study effectively instead of lavishing time on so many curricula. The recommendation system has been used in Electronic Commerce for analyzing the customers’ track record in order to increase the purchasing power of the websites through introducing the commodities that the customer may buy again. E-Learning system is applied on the basis of the method. This paper recommends learners the studying activities or learning profile through the technology of Web Mining with the purpose of helping they adopt a proper learning profile. Therefore, we refer idea and combine with their ideal description to accelerate the learning speed in the recommendation e-learning system.

中文摘要 I
Abstract II
誌 謝 III
表目錄 VII
圖目錄 VIII
第一章 緒論 1
1.1研究背景 1
1.2研究動機 2
1.3研究目的 5
1.4研究範圍 6
1.5論文架構 8
第二章 文獻探討 10
2.1 e-Learning的現況及發展 10
2.2 SCORM 12
2.3 學習歷程(Learning Profile) 14
2.4 資料探勘(Data Mining) 15
2.4.1 網頁探勘(Web Mining) 16
2.4.2 關聯法則(Association Rule) 17
2.4.2.1 Apriori 19
2.4.2.2 Path and Purchase(PPA) 21
第三章 研究方法 24
3.1 資料流程架構 24
3.2 電子商務與線上學習的比較 25
3.3 學習路徑的蒐集依據 29
3.4 適合e-Learning的資料探勘方法 31
3.4.1 e-Learning上的網頁探勘需求 31
3.4.2 PPA與 Apriori 的比較 33
3.5 資料的處理 35
3.5.1 資料的轉換 35
3.5.2 資料的篩選 37
3.6學習概念圖 42
3.7使用者評估學習路徑 46
第四章 建構推薦系統學習平台上 47
4.1 模擬推薦學習平台之系統流程 48
4.2 模擬推薦學習平台之執行結果與畫面展示 51
第五章 總結及未來方向 60
參考文獻 65
簡 歷 68

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