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研究生:陳勇汀
研究生(外文):Chen, YungTing
論文名稱:合作式閱讀標註之知識萃取機制研究
論文名稱(外文):A study on developing knowledge extraction mechanisms from cooperative reading annotation
指導教授:陳志銘陳志銘引用關係
指導教授(外文):Chen, Chih Ming
學位類別:碩士
校院名稱:國立政治大學
系所名稱:圖書資訊與檔案學研究所
學門:傳播學門
學類:圖書資訊檔案學類
論文出版年:2011
畢業學年度:99
語文別:中文
論文頁數:188
中文關鍵詞:合作式閱讀標註知識標註學習系統知識萃取機制模糊綜合評判閱讀學習
外文關鍵詞:Cooperative reading annotationKnowledge-based annotation learning system (KALS)Knowledge extraction mechanismFuzzy synthetic decisionReading learning
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本研究在合作式數位閱讀環境中發展了一套「知識標註學習系統」,可以支援多人同時針對一篇數位文本進行閱讀標註與互動討論,以提升讀者閱讀的深度與廣度。此外,本研究更進一步地以專家評估法設計「知識萃取機制」,用於判斷讀者閱讀標註的重要度。
「知識萃取機制」是基於讀者閱讀標註中所蘊含的閱讀理解策略與閱讀技巧,以及合作式閱讀社群中產生的標註共識,考量了「標註範圍長度」、「標註範圍詞性」、「標註範圍位置」、「標註策略類型」、「標註範圍共識」與「標註喜愛共識」等六項因素,以專家評估法制定的標註重要度模糊隸屬函數來評定各因素的重要度並量化為「標註因素分數」指標,最後將六項因素以模糊綜合評判進行推論,再將推論結果解模糊化而成為代表標註重要度的量化指標「標註分數」。基於「知識萃取機制」所計算代表標註重要度的「標註分數」,可作為讀者進行閱讀標註是否不佳的判斷,並據此提供標註技巧建議與優質標註內容推薦的「標註建議」,以幫助讀者提昇閱讀理解能力。
為了驗證「知識萃取機制」計算「標註分數」的有效性,以及探討未來改善「知識萃取機制」和可加入的考量因素與適性化設計的可能方向,本研究以單組後測設計規劃實驗,並以國立政治大學圖書資訊數位碩士在職專班19位學生作為實驗對象,進行一份數位學習論文的合作式閱讀標註學習,並於實驗後評估實驗對象閱讀文章之後的閱讀理解能力,作為評鑑「知識萃取機制」計算方式是否有效的指標。最後再以問卷蒐集實驗對象對於「知識萃取機制」的意見,歸納成為未來研究改善的參考依據。
研究結果發現,本研究所提出「知識萃取機制」中計算標註重要度的「標註分數」與實驗對象的閱讀理解能力呈現低度正相關,一定程度地證實了「知識萃取機制」計算方式的有效性。而「知識萃取機制」六項考量因素中,「標註範圍長度」與「標註喜愛共識」為分辨實驗對象閱讀理解能力的關鍵因素;「標註策略類型」與「標註範圍詞性」的標註重要度模糊隸屬函數有待修正;「標註範圍共識」與「標註範圍位置」為無效因素,但這可能是受到計算方式錯誤與閱讀文章類型的影響,未來仍有待進一步評估。在未來發展方面,系統操作標註行為頻率越高,實驗對象的閱讀理解能力也有較高的跡象,未來可以將其納入「知識萃取機制」作為考量因素之一;而閱讀理解能力較差的實驗對象,呈現出比較不願意回應「標註建議」與較常使用社群互動的現象。本研究歸納可能原因為實驗對象自身的閱讀素養不成熟,以至於無法判斷「標註建議」的正確性,而需要參考他人閱讀標註。
未來研究可針對本研究的實驗對象與閱讀標註資料進行更深入的分析,並且將改良後的「知識萃取機制」擴大至探討其他類型的數位文本閱讀標註與實驗對象。也可以搭配認知策略教學法建構閱讀教學鷹架,或是將「知識標註學習系統」用於支援數位典藏與數位圖書館閱讀學習,以激發更多不同領域的應用研究。
Based on the concept of cooperative reading learning, the study presented a cooperative reading annotation system termed as "Knowledge-based Annotation Learning System (KALS)", which can support cooperative reading annotation while reading a common text-based digital material, to accumulate reading knowledge and to promote readers’ reading comprehension abilities. Through KALS, readers could freely increase annotation for any text words on a text-based digital material with HTML format. Readers can also share and discuss the contributed annotation with other readers via interaction interface in KALS.
Furthermore, this study also developed an intelligent Knowledge Extraction Mechanism (KEM), which can mine the quality annotation knowledge and annotation skills based on a large amount of readers’ annotation archived on KALS, to further promote reading comprehension of readers via on-line recommending high quality annotation knowledge and good annotation skills to readers. KEM employed fuzzy synthetic decision approach to quantify each reader’s annotation as a numeric index termed as "Annotation Score" under simultaneously considering two annotation consensuses including anchor consensus and favorite consensus, and four annotation features including anchor length, part of speech of anchor word, anchor location and annotation strategy. In a manner, "Annotation Score" can represent the importance of reader's annotation. Thus, KEN uses "Annotation Score" to determine which annotation needs the suggestion of annotation skill tips, and which high-quality annotation can be recommended to readers. At the same time, readers are encouraged to reflect their annotation behavior based on the suggestion of annotation skill tips and high-quality annotation recommended by KEN, and are asked to respond the feedback from KEM.
To evaluate the effectiveness of the proposed KALS with KEM, the study designed an experiment to collect readers' annotation behavior after readers read an assigned text-based digital material, and then assessed readers’ reading comprehension ability. Reading comprehension ability was used to verify the effectiveness of "Annotation Score" inferred by KEM and to explore the potential factors that can improve KEM. In the designed experiment, participants were 19 graduate students of E-learning Master Program of Library and Information Studies of National Chengchi University who took the course of Integrating Information Technology into Teaching. All participants were asked to read an academic paper related E-Learning issue based on the support of KALS with KEM during two weeks. Moreover, they had to finish a reading report and accept a test of reading comprehension after finishing reading learning activity. The report and test were served as the measurement of participants' reading comprehension.
The experimental results show that there is a low positive correlation between "Annotation Score" and participants' reading comprehension score, thus confirming the effectiveness of the proposed KEM. Furthermore, KEM could be improved by adjusting the annotation importance calculation approach of part of speech anchor word and annotation strategy. This study also confirmed that the considered factors of KEM should eliminate two factors including anchor consensus and anchor location. Additionally, future study should consider adopting frequency of annotation behavior as considered factors of KEM. Moreover, the experimental results also show that participants with low level of reading comprehension ability have higher need of community interaction than participants with high level of reading comprehension ability while using KALS for reading learning, and they are difficult to confirm whether the recommending tips of annotation from KEM is correct or not. Obviously, exploring the difference of participants’ annotation behavior between different levels of reading comprehension abilities provides benefits to develop adaptive functionalities of KEM in the future.
摘要 i
Abstract iii
目次 v
表次 ix
圖次 xiii
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 4
第三節 研究問題 5
第四節 研究範圍與限制 7
第五節 研究貢獻 8
第六節 名詞定義 9
第二章 文獻探討 13
第一節 閱讀理解教學 13
第二節 標註學習 19
第三節 標註共識 24
第四節 閱讀標註系統 30
第三章 研究方法 37
第一節 研究架構 37
第二節 實驗場域 41
第三節 實驗對象 43
第四節 研究工具 44
第五節 實驗流程 50
第四章 知識標註學習系統 55
第一節 系統介紹 55
第二節 系統開發環境 56
第三節 系統架構 60
第四節 系統運作流程 65
第五節 系統操作畫面 66
第五章 知識萃取機制設計 73
第一節 模糊綜合評判 73
第二節 知識萃取機制考量的因素集 77
第三節 知識萃取機制考量之各因素的標註重要度模糊隸屬函數制定 80
第四節 因素權重集制定 86
第五節 標註建議設計 86
第六節 標註指引設計 90
第六章 實驗結果與分析 91
第一節 實驗對象分析 91
第二節 「標註分數」與閱讀理解分數相關度分析 100
第三節 知識萃取機制考量因素分析 102
第四節 標註因素分數與閱讀理解分數相關度分析 114
第五節 系統操作標註行為高低分組與閱讀理解分數差異分析 118
第六節 閱讀理解分數高低分組之閱讀標註行為差異分析 121
第七章 結論 127
第一節 研究結論 127
第二節 討論 130
第三節 未來發展 134
參考文獻 143
附錄 A 知識萃取專家評估問卷 155
附錄 B 閱讀理解測驗 167
附錄 C 使用意見調查問卷 173
附錄 D 標註重要度模糊隸屬函數 177
附錄 E 標註建議意見調查結果 179
附錄 F 標註範圍詞性類型LSD法多重比較結果 183
作者簡介 187
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