臺灣博碩士論文加值系統

　　在資訊超載日益嚴重的時空下，搜尋機制已不再能夠滿足所有人的需求，而這也是推薦機制誕生的開始。推薦機制早期多用於商業行為，而在相同的資訊超載背景之下，線上學習是逐年擴展，相對數位教材也紛紛流通於網際網路之中，而推薦學習便於此油然而生。隨著技術的成熟，大量不同種類的教材已數位化於網際網路之中，對於學習其他領域的知識，學習者不再需要耗時耗力。而過去的推薦學習研究中，焦點在於如何讓學習者獲得更加準確的推薦，而這樣僅針對單一領域的推薦並無法滿足目前多元化的學習環境。有鑒於此，我們針對學習者可能在新增學習領域或新增學習興趣之情況下，提出智慧型推薦學習服務系統之研究，以達到在學習者新增學習領域或學習興趣時，能給予快速與準確的推薦。
　　本研究結合學習者之顯性評比與隱性評比資料，並改善TOP-N最熱門推薦法則與關聯推薦法則發展出之智慧型推薦學習服務，系統所使用之教材皆符合SCORM標準，且整併SCORM相關功能於系統中。實驗方法以三個階段進行，最後利用問卷調查系統滿意度。研究結果顯示推薦準確度對系統穩定度與介面操作滿意度成正相關，教材內容滿意度對系統穩定度與介面操作滿意度成正相關，系統整體接受度對推薦準確度與教材內容滿意度成正相關，以及教材內容滿意度對推薦準確度成正相關，表示學習者對於智慧型推薦學習服務系統之整體接受度佳。

As information overload becomes more severe, search agent can no longer fulfill the needs of all users, and this is the beginning of the emergence of recommendation mechanism. In the early days, recommendation mechanism was mostly applied to commercial behavior. Under the same background of information overload, e-Learning expands gradually, and e-Learning materials prevail on the Internet. This is a time when recommendation learning comes into exist. As technology matures, a large amount of various teaching materials have been digitalized and uploaded to the Internet in order for learners to convenient gaining knowledge of other fields. Past studies on recommendation learning mainly discussed how learners could access more accurate recommendations. This type of single direction recommendation cannot satisfy the diversified learning environments at present. Therefore, this research proposes an intelligent recommendation learning service system for learners to learn in different fields, which can provide fast and accurate recommendations to learners searching for information in their new fields or interests of learning.
There are three features in our study. First, we implement the online recommendation system used the technique which combine the explicit rating and implicit rating for learner characteristics. The novel mechanism called Intelligent Recommendation Learning Service was proposed and improved the TOP-N recommendation rule and association recommendation rule. Secondly, the system used the standard SCORM and integrated relative valued functions. Third, we did an experiment to prove our hypotheses. There were three phases in this experiment, and then using questionnaire was processed for learners. After analyzing, the results of the study showed that learners accepted Intelligent Recommendation Learning Service System. The factor of recommendation accuracy was effected system stability and operation interface satisfaction, and the relevance of teaching materials was effected system stability and operation interface satisfaction, and system acceptance was effected recommendation accuracy and the relevance of teaching materials, and the factor of recommendation accuracy was effected the relevance of teaching materials.

第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 5
第三節 研究範圍與限制 5
第二章 文獻探討 7
第一節 評比方式 7
第二節 推薦系統 8
第三節 推薦方法 12
第四節 資料探勘及關聯法則演算法 13
第五節 推薦學習 17
第六節 SCORM數位教材標準 22
第七節 小結 23
第三章 研究方法 25
第一節 系統架構 25
第二節 前置資料的蒐集 27
第三節 推薦學習演算法 30
第四節 教材分類與SCORM標準 38
第五節 實驗設計與評量 42
第六節 問卷設計 45
第四章 研究結果與分析 49
第一節 效度與信度分析 49
第二節 因素分析 54
第三節 迴歸分析與路徑分析 59
第五章 結論與建議 62
第一節 研究結果 62
第二節 研究結論 62
第三節 未來工作與建議 63
參考文獻 65
附錄 69

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