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    題名: 利用多目標最佳化演算法實作以混搭為基礎適性化程式設計學習;Mashup-based Adaptive Programming Learning with Multi-Objective Optimization Algorithm
    作者: 黃信博;Hsin-Bo Huang
    貢獻者: 資訊工程學系碩士在職專班
    關鍵詞: 適性系統;基因演算法;多目標最佳化;混搭;適性化學習;Multi-objects optimization;Mashup;Adaptive learning;Genetic Algorithm;Adaptive application
    日期: 2009-06-23
    上傳時間: 2009-09-22 11:35:05 (UTC+8)
    出版者: 國立中央大學圖書館
    摘要: 傳統的程式設計課程教學方式隨著資訊科技的引領邁入數位化教學的新世代,也讓程式設計課程教材更生動活潑且便利。近年來,網路Web 2.0的興起,提供學習者多元化的網路服務,其中一個技術是混搭(Mashup),混搭不僅能夠整合網際網路上多個資源與功能,更能創造新的服務,他們之間也能進行資訊的溝通。因此,混搭開啟了數位學習新的學習方法,學習者可透過混搭服務進行數位學習。本論文提出一個以混搭服務為基礎的適性化程式設計學習,本系統會自動的依據學習者的程式設計概念能力、學習目標、社群推薦度、學習語言、學習活動地點等學習情境,利用多目標最佳化演算法進行適性化學習服務組合,最後產生一組適性化教材。本系統應用在智慧型程式設計教學系統裡,將促進以能力為導向之程設教學培養的實現。 Because the computer technology is involved, the traditional programming language teaching class methods get into another new era, digital class. It also makes the programming language class’ material more convenient and colorful. Recently, Web 2.0 provides learners more diverse internet service, one of them is Mashup. Mashup not only integrate many resources and functionalities of internet, but can create new services. Moreover, data communication is feasible among them. Therefore, Mashup creates a new way for digital learning. Learners can do digital learning through it. This thesis provides an adaptive learning of programming language, which is based on Mashup services. Our system will generate a set of adaptive learning material according to the ability of program learner, the learning objectives, the social recommended degrees, learning language, learning location, and adaptive learning widgets from Multi-Objective Optimization algorithm. Our system is widely used in intelligent programming teaching systems, to make competency-based training and process-oriented teaching come true.
    顯示於類別:[資訊工程學系碩士在職專班 ] 博碩士論文

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