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題名：以本體論為基礎推論輔助數位學習系統之研究
作者：朱國光
作者(外文)：Kuo-kuang Chu
校院名稱：國立臺南大學
系所名稱：數位學習科技學系博士班
指導教授：李建億
學位類別：博士
出版日期：2013
主題關鍵詞：推論機制；學習遷移；認知負荷；學習成效；學習策略；本體論；ontology；reasoning mechanism；transfer of learning；cognitive load；learning strategies；learning effect
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以概念構圖建構學習者知識結構並作為學習診斷工具已有許多研究，本體論技術具有概念構圖階層與屬性呈現優點，更具有概念查詢、驗證及推理功能。因此，本研究導入本體論開發概念推理數位學習系統，並建置國中自然與生活科技領域地球的水圍(水的分佈)、生物科人體消化系統與人體神經系統單元之課程本體論，並導入語意網規則語言SWRL及Jena API並建立課程單元之推論法則，以期釐清學生常見混淆之概念。此外，本研究欲進一步瞭解學習者使用系統後之學習成效、學習遷移、認知負荷與學習策略的影響，並透過科技接受模式TAM，以瞭解學習者對系統之認知有用性、認知易用性、使用態度、使用意願的影響。
研究結果發現，在學習成效上，本體論學習對高空間能力者會較有利，傳統教學對不同空間能力則無差異。學習中成就組或中推理能力組在概念構圖、本體論及本體推論三種不同學習法間都會有顯著的學習成效差異，而知識本體推論輔助學習法對於學習中成就及中推理能力組學生的學習成就最具有效果。在認知負荷上，本體推論組在認知負荷的心智努力面向明顯低於本體組與概念構圖組，顯示本體推論組在學習過程中所付出認知能量和資源最少，而本體推論學習系統學習神經系統單元，可以有效降低教材內容的困難度，降低中、高成就學生在心智負荷的程度，且應用本體推論學習系統教學模式，降低學生在學習神經系統的認知負荷並增加學習成效。在學習遷移上，中推理能力者，本體推論學習系統的協助可以解決他們的困境，使得在高層次垂直遷移上有顯著差異。本體推論與學生推理能力在垂直學習遷移上有顯著差異推理能力愈高學生，垂直遷移能力愈好；本體推論與學生推理能力在而水平遷移上則無顯著差異，且不同學習法的水平遷移具有顯著差異。在學習策略上，概念構圖組與本體推論組學生之學習策略成績上有顯著差異。在一般認知、特定認知(語言)、後設認知、注意力、學習動機與焦慮等6種學習策略間，語言策略與其它策略間皆有顯著差異。一般認知與後設認知的同質性很高，皆與語言、注意力與焦慮間有顯著差異。系統可以提高學生注意力，降低學生焦慮，本體推論組在一般認知(注意力策略、複述策略、組織策略及意義化策略)及後設認知(計劃策略、監控策略、評估策略及調整策略)上可以降低學習者的認知策略發展的負擔。就TAM而言，呈現電腦自我效能與科技任務配適度與使用者認知易用性有正向影響，科技任務配適度與系統可靠性與使用者認知有用性間有正向影響，而認知有用性對使用態度有正向影響，同時正向影響使用意願。
本研究對於本體論與推論機制導入數位學習做了豐富完整之研究，證明本 究所開發之學習系統對學習者學習成效、學習遷移、認知負荷與學習策略上之功用。此系統將會是一個很好的開始，未來希望也可以將此系統應用推廣於其他課程或者不同有查詢推理知識領域，以服務更多使用者及解決其他領域知識管理的問題。
There are many studies about concept map, and the learner’s knowledge structure can be present in concept maps. Ontology technology not only can present knowledge like concept map, but also it can research and validate concepts, even it can reasoning rule as an expert system. In this study, researcher implemented three species of concept learning system, and created course ontologies and inference rules with two different inference engines. The ontology engineering applied to the distribution of earth’s water, the human digestive system and the human nervous system of science and technology fields of junior high school. To understand the usability and accessibility of the system, researcher introduced the technology acceptance model to improve system design. In addition, researcher tried to figure out the inference of learners’ learning achievement, transfer of learning, cognitive load and learning strategies after user learning. The results show that computer self-efficacy and task-technology fit both are positive affect to perceived ease of use, and the system robust and task-technology fit both are positive affect to perceived usefulness, then the users’ attitude are positive affect to user wellness. In learning achievement, ontology-based learning has advantages for high spatial ability users, and middle achievement or middle reasoning ability students show the significant difference among three learning approaches. In cognitive load, the mental effort of ontology with reasoning group is significantly lower than ontology group and concept map group. In the transfer of learning, the vertical transfer of learning has significant difference with other learning approaches for middle reasoning ability students, and the horizontal transfer of learning has significant difference among three learning approaches. In learning strategies (common cognitive, specific cognitive, metacognition, attention, motivation and anxiety), the learning strategies of the ontology reasoning group show significant difference with concept map group. Finally, the learning system had been a demo plateform, and research hope it would apply to other courses or domain knowledge in the future.
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