標題: 基於本體論及規則建構學習順序
Learning Sequences Construction Using Ontology and Rules
作者: 陳瑞言
Ruei-Yan Chen
曾憲雄
Shian-Shyong Tseng
資訊科學與工程研究所
關鍵字: 學習順序;本體論;規則;網域名稱伺服系統;分享內容元件參考模型;learning sequence;ontology;rule;DNS;SCORM
公開日期: 2004
摘要: 網域名稱伺服系統(Domain Name System,以下簡稱DNS)是現今網際網路基礎設施的重要環節之一;然而,根據Men & Mice (2005) 最新一份網路健康狀況調查的報告指出,目前仍有將近70%的 .COM網域存在設定上的錯誤。因此,如果能建構出一個可提供完整DNS相關知識的教學輔助系統,就可以幫助許多上述的系統管理者完成DNS系統的建構,減少錯誤設定的發生。為了簡化學習順序建構的複雜度,在這篇論文中,我們提出一個使用本體論及規則建構學習順序的模型,其中包含有三個功能不同的模組:基於本體論的學習順序建構模組是用以將本體論轉換成一個基本的課程架構;後設知識擷取模組則是用於從規則中擷取出後設知識;最後,我們在範例及測驗附加模組中整合這兩類知識,以呈現給學習者更完整的課程架構。另一方面,分享內容元件參考模型(Shareable Content Object Reference Model,以下簡稱SCORM)是目前最為廣泛使用的數位學習標準,其藉由學習順序與瀏覽模組來定義使用者在數位學習上的學習行為與順序;所以我們設計並實做了一個符合SCORM標準的DNS輔助教學雛型。實際上,只要做點些微的調整,就可將我們所提出的模型套用在其他領域上,幫助學習順序的建構。
The Domain Name System (DNS) is an essential part of the Internet software infrastructure. However, according to the domain health survey for commercial sites (Men & Mice, 2005), almost 70% of .COM zones have at least one mis-configuration. In practice, during design and deployment phases, DNS tutoring system could provide us with related information to help reduce the percentage of DNS mis-configuration. In this thesis, we propose a learning sequences construction model using ontology and rules to simplify the complexity of learning sequence construction. There are three modules in this model. First, the Ontology-based Learning Sequences Construction Module is designed to transform an ontology into a basic course scheme. Second, the Meta-Knowledge Extraction Module is used to extract meta-knowledge form rules. And, finally, these two kinds of knowledge would be integrated in the Example & Quiz Annotation Module. On the other hand, Shareable Content Object Reference Model (SCORM), which is the most popular e-learning standard, defines the learning sequence behavior and the learner navigation by using sequence and navigation model. For illustrating the ideas, we design and implement a SCORM-based DNS tutoring prototype system. In fact, with a few modifications, it is supposed that the model could easily be applied to other domains for learning sequences construction.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT009223553
http://hdl.handle.net/11536/76604
Appears in Collections:Thesis


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