適應性內容傳輸機制於多樣行動載具之研究

Abstract

隨著行動載具的性能越來越強，在無線網路的環境之下傳輸合適的學習資源給多樣化行動載具的需求也越來越大。但是因載具能力因素以及頻寬不足的情形，學習內容本身無法展現出令人滿意的效果，因此如何將學習資源做內容適應性轉換形成一個重要課題。先前學習者取得學習物件時，當時的網路情況與硬體、使用者偏好設定將會被記錄下來，這提供了我們解決問題的靈感，當新使用者要求學習物件時，可以依據和先前學習者的設定偏好相似程度，先行給予最接近需求的內容，不但可同時兼顧內容傳輸上的效能，也能夠兼顧使用者對於學習內容的滿意程度。在本論文中，我們提出了適應性內容傳輸機制(ACDM)於符合SCORM教學平台的元件資料庫(LOR)中。ACDM可以有效率地管理先前學習者的傳輸記錄，快速判斷目前學習者及以往記錄中設定的相似程度，以送出更符合使用者本身需求的學習內容。ACDM包含了資料格式定義階段，與內容傳輸階段，前者依據硬體、使用者設定與頻寬狀態定義了內容轉換規則(CAR)，而後者則包含了內容轉換管理機制(CAMS)，將CAR實行群集以及決策樹分析(CADT)，也包含了適應內容決策進程(ADP)以根據決策樹判斷何內容版本符合當前使用者需求；最後包括內容合成機制(CS)，負責將單一媒體根據參數轉換成不同的版本。最後的實驗結果展現出ACDM機制在內容傳輸上有明顯的效果改善。

With the mobile devices becoming more and more powerful, the requirements of delivering and displaying the learning contents on the diverse mobile devices over the wireless network are increasing rapidly. However, the insufficient hardware capabilities and the limited bandwidth have leaded to the bad navigation experience and unfavorable presentation of contents. Therefore, how to perform the Content Adaptation becomes an important issue. The historical user’s request records including hardware capabilities, user’s preference, and the current network situation, can provide us an opportunity to solve the above issue. The inspired concept is that if we can provide a new user’s request with the adaptive contents created from previous similar user’s request, not only the performance of content delivery but also the user’s satisfaction will be improved highly. Therefore, in this thesis, based on the aforementioned concept and SCORM complaint learning object repository (LOR), we propose an Adaptive Content Delivery Mechanism, called ACDM, which can efficiently manage a large number of historical user’s requests, and intelligently deliver a proper adaptive content with higher fidelity from LOR to the user directly and then prepare a transcoded content version for next similar request. The ACDM includes Adaptation Data Format Definition Phase and Adaptive Content Delivery Phase. The former defines an adaptation data format, called Content Adaptation Rule (CAR), based upon CC/PP, UAProf, etc. In order to efficiently deliver the suitable content with associated learning resources to users in accordance with their user preferences, hardware capabilities, and variable wireless bandwidth, the latter consists of 1) Content Adaptation Management Scheme (CAMS): apply clustering approach and decision tree approach successively to create a Content Adaptation Decision Tree (CADT), which can be used to decide the appropriate adaptive contents from the LOR, 2) Adaptation Decision Process (ADP): propose an Adaptation Decision Process Algorithm to intelligently determine a suitable version of the existing adaptive content based on the CADT, and 3) Content Synthesizer: transcode the content if necessary. For evaluating our proposed approach, an ACDM prototypical system is developed. Furthermore, the experimental results show that the ACDM is workable and beneficial.