使用蟻群最佳化演算法建立適性學習資源搜尋平台

Using Ant Colony System for Adaptive Learning Object Search Platform

楊曜榮(Yao-Jung Yang)

本體論 ； 蟻群最佳化 ； 適性學習 ； 電子學習 ； 啟發性演算法 ； Ontology ； fuzzy ontology ； adaptive learning ； E-learning ； E-Education Portal

興國學報

14期（2013 / 01 / 01）

181 - 191

繁體中文

本研究利用義大利學者Dorigo[22]提出的蟻群最佳化演算法（Ant Colony Optimization Algorithm，以下簡稱ACO）及ontology的技術來建構一個E-Education Portal，協助網路學習者（learner）如何在網路上依個人的學習風格(learning style)[Kolb]、學習經驗（learning experience）及知識背景（Knowledge background）來找尋最佳且適性（adaptive）的學習資源，讓網路上的學習資源（Learning Resource）能有效的重複使用（re-usability）並將適當的學習資源供給學習者學習以達到個人化（personalize）且適性化（Adaptive）的學習需求。本研究亦提出加強學習資源與其他學習資源間關係屬性（如關係強度、前置關係或是後置關係）的解決方案，以建議學習者選取學習資源的順序達到課程推薦的目的，並以Oracle OCA課程為例，利用ontology的技術來描述學習資源的屬性及與其他學習資源間的關係，由提供者針對學習資源的難易度（learning resource level）及與其他學習資源的關係強度予以適當的屬性值，結合蟻群最佳化演算法所建置的搜尋代理程式（ontology search agent）依學習者的屬性及學習資源的屬性找出最適當的學習資源以供學習。

Teachers usually have a personal understanding of what ”good teaching” means from their experience and domain knowledge background in education, lots of them create learning resources (LR) and put on the web for learners or students to study. In fact, most of student can not find the suitable LR from webs, In this paper, we based on ontology and fuzzy technology to propose an architecture for adaptive and re-usability in personalized web-based E-Education Portal, help learners fetch the LR from ontology LR repositories according by the learner characteristics/attribute (e.g. domain knowledge background and Kolb's [11] Learning Style), and we also interested in exploiting the semantic relationships that characterize theses LRs, so we redefine two ”relation” attributes from Dublin Core [3] metadata schema call ”Pre_Relation” and ”Succ_Relation” for enhance LR relations in order to recommend the relative LRs for learner further reference.

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