本研究發展一能夠預測學習者閱讀英語文章時之「個人化閱讀焦慮預測模型」，此預測模型係以資料探勘技術為基礎，透過資料探勘技術於個人閱讀歷程及標註行為中進行閱讀焦慮預測規則的建立，並將預測結果與判定規則回傳給教師，以提供教師掌握造成學習者閱讀焦慮之關鍵因素，並提供適當閱讀輔助策略，藉此減緩學習者閱讀焦慮程度，進而提升其閱讀學習成效。 為了驗證本研究所發展「個人化閱讀焦慮預測模型」的可用性，以及探討本研究所設計不同學習機制對閱讀焦慮減緩策略的有效性，本研究以準實驗法設計三種不同閱讀學習機制並分別實施於控制組、實驗組A與實驗組B，接著以臺北市立萬芳國中一年級學生作為實驗對象，進行本研究實驗資料的收集，以作為驗證「個人化閱讀焦慮預測模型」可用性的資料來源及三種學習機制間降低閱讀焦慮與提昇閱讀學習成效的有效性驗證資料。 研究結果發現，「個人化閱讀焦慮預測模型」能有效預測學習者閱讀焦慮程度，為一個可靠的閱讀焦慮程度判別工具。此外，本研究發現低焦慮學習者在閱讀標註互動上較高焦慮學習者使用頻率高，顯示高低焦慮程度學習者在閱讀標註互動行為上有較明顯的差異，而本研究在實驗組A所實施的合作式閱讀機制能有一定程度能降低中焦慮組學習者閱讀焦慮現象；在實驗組B所提供的線上教師閱讀輔助策略亦能有助於學習者閱讀焦慮減緩。除此之外，本研究所設計三種不同學習機制皆能有效提昇學習者閱讀學習成效，顯示本研究所發展合作式閱讀標註系統有助於提昇學習者閱讀學習成效。   最後，將研究結果進行整理同時輔以文獻驗證，並歸納研究者在研究過程中觀察發現，提出個人化閱讀焦慮預測模型修正、合作式閱讀標註學習社群與電子書閱讀輔助應用等未來研究議題的初步架構，供後續研究者參考以進行更深入的探討。 / To effectively reduce reading anxiety while reading English articles, this study employs C4.5 decision tree, which is a widely used data mining technique, to develop a Personalized Reading Anxiety Level Prediction Model (PRALPM) for learners based on individual learners’ reading annotation behavior on a digital reading annotation system. The proposed PRALPM can explore the key factors that cause reading anxiety based on the fired prediction rules determined by decision tree. Through understanding these key factors that cause reading anxiety, instructor can support appropriate reading strategies to reduce learner’s reading anxiety level and promote their reading performance. To assess whether the proposed PRALPM can effectively assist instructor to reduce reading anxiety, this study adopted the quasi-experimental method to compare the learning performances of three learning groups, which are respectively supported by a digital reading annotation system with different learning mechanisms for reducing reading learning. Among the three learning groups, the control group, experimental group A and experimental group B conducted the same English reading learning activity, but were respectively distributed a digital reading annotation system with individual annotation, cooperative annotation and cooperative annotation with instructor’s support based on the proposed PRALPM for reducing reading anxiety. The experiment were executed on Taipei Municipal Wan-fang High School 7-grade student, and collected experimental data for verified the model availability and the effectiveness of different learning mechanism in lower learner’s reading anxiety level. The results found that PRALPM can predict learner’s reading anxiety level efficacious, and it’s also a reliable tool for identify reading anxiety. In addition, the study found that low level anxiety learners has more reading interactive than high level learners, it also mean different anxiety level learners have Significant differences in reading interaction activities. And the Collaborative reading mechanism can help middle-anxiety-level learner reduce their anxiety efficacious in experimental group A. The online teacher reading assisted strategy can also help learners to slow their read anxiety in experimental group B. Furthermore, three type of learning mechanism all have the positive Effect to enhance learner’s reading performance, it shows that this collaborative reading annotation system can help learner Have better learning outcomes. At last, the study summarized the researchers observed and bring forward some future research issues such as PRALPM modify, cooperative learning community and the application of e-book reader-assisted subject.

本研究發展可以在任意網頁上進行閱讀標註之合作式閱讀標註系統，並透過探勘集體智慧方式，在合作式閱讀標註系統上發展「優質標註萃取」及「達人標註萃取」機制，來輔助學習者進行數位文本閱讀學習，以達到提昇閱讀理解成效的目的。此外，本研究也進一步探討透過「優質標註萃取」及「達人標註萃取」機制過濾掉一部份品質較差的標註，是否可有效降低閱讀標註文本時產生的認知負荷。 本研究將學習者分成實驗組1（達人標註）、實驗組2（優質標註）與控制組（所有標註）三組，並分別進行約80分鐘的合作式閱讀標註學習活動。其中控制組的成員採用「呈現所有標註之合作式閱讀標系統」支援閱讀學習；而實驗組1的成員則透過「呈現達人標註之合作式閱讀標註系統」來進行閱讀學習；實驗組2則透過「呈現優質標註之合作式閱讀標註系統」來進行閱讀學習。合作式閱讀標註活動要求學習者在指定時間內閱讀本研究指定的文本（化學科普之文章），同時利用「合作式閱讀標註系統」進行閱讀標註撰寫與分享。閱讀標註活動結束後，學習者將進行所閱讀文本之閱讀理解評量以及認知負荷量表填寫，據此瞭解學習者的閱讀理解成效及認知負荷程度。 研究結果顯示，採用具有「優質標註萃取」機制所得標註支援閱讀學習，有助於過濾品質不佳的閱讀標註，並提供更簡潔易找尋之優質標註支援閱讀學習，進而提昇閱讀理解成效，由於閱讀時更容易找到所需的優質資訊，因此亦較有助於提昇學習者不同面向概念的閱讀理解成效；此外，本研究基於每位學習者的有效標註，在考量標註層次及標註數量下，評估每位學習者的“標註能力”，採用優質標註支援閱讀學習的實驗組2（優質標註）學習者中，標註能力越高的學習者，其閱讀理解成效也較佳；而本研究將學習者依照閱讀理解後測成績高低，分成高分組及低分組後顯示，控制組（所有標註）與實驗組2（優質標註）的組別中，均呈現出低分組學習者的認知負荷顯著高於高分組學習者的現象；除此之外，本研究比較三組採用不同標註呈現方式之合作式閱讀標註系統進行閱讀學習之學習者時，結果發現，採用三種不同閱讀標註呈現方式組別學習者之認知負荷無顯著差異。 最後，本研究歸納研究者在研究過程及結果中之發現，提出發展結合合作式閱讀標註的有效閱讀學習策略、探討各類型標註眼動行為對於閱讀理解成效影響與擴展合作式閱讀標註系統支援行動閱讀學習等未來研究議題之初步架構，供後續研究參考以進行更深入之探究。 / A Collaborative Reading Annotation System, which can be randomly proceeded reading annotations on any web pages, is developed in this study. Furthermore, Quality Annotation Extraction and Master Annotation Extraction are developed on the Collaborative Reading Annotation System by mining collective intelligence for assisting learners in proceeding reading digital texts and promoting the reading comprehension performance. The effect of removing some bad-quality annotations through Quality Annotation Extraction and Master Annotation Extraction on reducing the cognitive load when reading annotation texts is further discussed in this study. The learners are divided into Experiment Group 1 (Master Annotation), Experiment Group 2 (Quality Annotation), and Control Group (All Annotation) for 80-minute collaborative reading annotation learning. Control Group uses Collaborative Reading Annotation System with all annotations for promoting reading; Experiment Group 1 proceeds reading through Collaborative Reading Annotation System with master annotations; and, Experiment Group 2 applies Collaborative Reading Annotation System with quality annotations to reading. The learners are requested to read the assigned texts (articles of popular science in chemistry) in the assigned period and write and share the reading annotations with the Collaborative Reading Annotation System. Afterwards, the learners are evaluated the reading comprehension of the texts and fill in the cognitive load scale for understanding the reading comprehension performance and the cognitive load. The research results show that utilizing the annotations acquired by Quality Annotation Extraction for promoting reading could filter out unfavorable reading annotations and provide quality annotations, which are more easily searched for promoting reading, to further enhance the reading comprehension performance. Since the quality information can be more easily searched, it could better assist learners in promoting reading comprehension performance in various aspects. Moreover, based on the valid annotations of each learner, the annotation ability is evaluated the annotation level and quantity. Learners with higher annotation ability in Experiment Group 2 (Quality Annotation) present better reading comprehension performance. Based on the reading comprehension post-test results, the learners are divided into high-score and low-score groups. The cognitive load of low-score learners in both Control Group (All Annotation) and Experiment Group 2 (Quality Annotation) is higher than it of high-score learners. Besides, the cognitive load among the three groups applying the Collaborative Reading Annotation System with different annotations to reading does not appear significant differences. Finally, developing effective reading strategies with Collaborative Reading Annotation, discussing the effects of various annotations on reading comprehension performance, and expanding Collaborative Reading Annotation System for promoting mobile reading are proposed as the preliminary framework for future research, with which in-depth exploration could be preceded in successive research.

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.