臺灣博碩士論文加值系統

在無所不在計算(Ubiquitous Computing)的設計概念中，人們可以透過任何裝置在任何時間、任何地點得到環境中自己需要的服務。本論文基於無所不在計算的技術為基礎，利用非接觸式的辨識技術建置智慧型的環境應用於學習、導引以及生活環境上。隨著學習科技的轉變，學習早已從傳統教室面對面方式轉換成使用新穎的資訊科技與創新的學習理論，讓學習多元化。此外高速的有線無線傳輸網路的廣泛建置，也使得學習已進入到能行動學習(mobile learning)，甚至結合最新且熱門的無所不在技術而所建構出學習者能與學習環境高度互動的無所不在學習(ubiquitous learning)。以被動式無電力非接觸式的RFID技術作為無所不在學習中散佈真實環境中學習物件的無線識別、學習資訊定位、以及學習互動之用，讓學習者以自主學習方式主動接近感應學習物件進行學習。所以借助RFID的無線感應識別能力，讓學習者能感覺到學習物件是活化有計算能力的，相信必能讓學習者的學習與週遭環境中的學習物件高度整合互動，而創造出在真實環境中無所不在學習環境。

In Ubiquitous computing environment, people can get the server which they need by any device at anywhere in any time. We purpose a smart environment for learning, guiding and day living by using ubiquitous computing technique and contactless mechanism. By the improvement of learning technology, the style of learning is transformed that using new computer technology and theory instead of the old style. The building of high speed wire and wireless make learning into mobile learning. Learning event combine the ubiquitous technology to build an ubiquitous learning environment that let learner can interactive with environment. We uses passive, powerless and contactless RFID technology to realize the function of learning object that identification, locating learning information and learning interaction in the environment. Learner can do learning process by approaching to learning object in the style of self learning. Using the wireless sensing identification capability let user perceive the activity and counting capability of learning object. We can create the ubiquitous learning environment in real world by the interaction between learner’s learning activities and learning objects in current environment.

第一章 緒論
1.1 研究背景
1.2 研究動機
1.3 研究目的
1.4 論文架構
第二章 相關文獻探討
2.1 智慧型空間概念
2.2 行動計算演進
2.3 非接觸識別定位機制
2.4 智慧型空間應用
2.4.1 行動學習
2.4.2 定位與引導
2.4.3 其他應用
2.5 智慧型空間的未來發展趨勢
第三章 應用於無所不在學習與導引之智慧型空間設計
3.1 以非接觸辨識機制設計智慧空間
3.2 無所不在的學習設計
3.3 無所不在的定位導覽設計
3.4 具學習調適的無所不在學習設計
第四章 系統實作
4.1 進入智慧型學習環境
4.2 學習物件所提供學習資訊的表示
4.3 學習資訊的取得
4.4 學習的導引與調適
4.5 智慧型環境中資訊的交換
第五章 系統應用的情境及評估
5.1 系統應用情境
5.2 實驗數據評估
第六章 結論與未來展望

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