閱讀理解是資訊解讀的過程，除了讀者如何識辨資訊間的關聯，讀者的前備經驗、知識與價值觀亦會影響閱讀結果。因此，為能在有限的課堂提供學生相等的機會，以瞭解他們對文本的多元想法，本研究發展對話機器人引導學生進行課文問答，搜集學生對文本的想法。
本研究以高中經典文學為範疇，並根據建構-整合模型之文本模式與情境模式設計6道開放式問題，並邀請品學堂之專家編輯進行核心命題與語句標注。本研究實驗邀請71名北部某知名大學學生進行實驗，並被分為三組：紙本組、對話機器人I(II)組。實驗過程中，受試者皆需回答高層次的問題(Q1)，並根據答題狀況提供引導問題，接著再重新審視Q1決定是否要修正。紙本實驗是由研究者監督該流程，當實驗結束時會訪談受試者對題幹與資訊處理的想法。對話機器人組別則是由機器人監督流程，其中因為對話機器人II是改良過後的版本，會以SUS問卷評估並比較兩對話機器人的易用性狀況。最後，為瞭解紙本實驗與對話機器人II的閱讀行為差異，量話與質化分析並比較兩者的修改次數、字數、及回答內容。
本研究紙本實驗結果顯示，文本資訊的解讀具有複雜性與多元性，前者係指資訊間關聯的程度有多個層次，後者係指單一資訊有多元的解讀方式。針對「未提及的核心概念」進行問答，並無法有效協助受試者修改答案，訪談指出可能原因為檢查策略、論述困難、個人情意因素所致。在前述紙本與對話機器人I實驗基礎上發展之對話機器人II，使用性從高可接受邊際的60分提升至可接受的70分，且從回答資料發現紙本實驗中未出現的閱讀行為，受試者願意與機器人分享個人生活經驗、啟示或進行批判思考。
本研究貢獻在於發展對話機器人，能有效率地搜集讀者對文本的多元理解與想法。過去以標準答案來分析學生的閱讀理解雖有效率，但卻忽略閱讀理解的個體詮釋差異，及應用於閱讀輔導或課程規劃上的潛力。本研究目前僅以大學生為受試者，並以敘述短文作測試，期許未來能以國、高中生為受試者，並嘗試不同的文體與文長，以探索其應用於課堂的可能性。

Reading comprehension is the process of interpreting information which is not only affected by the way readers deal with information but also by the prior related experiences, knowledge, and personal values. In order to collect students' perspectives about the texts within the time limits in a reading class, we develop a Line chatbot system guiding students to express their perspectives and collecting them automatically and efficiently.

This research focuses on the classic literature and uses it to design six open questions based on the textual and situational mode in the construction and integration model (CI model). To ensure the quality of the experimental materials, the experts of the Wisdomhall, a well-known publisher which develops reading materials in Taiwan, are invited to annotate the core propositions and sentences. 71 college students are invited to take the experiments and divided into three groups: the technology-free group, and the groups that used the chatbot (I) and chatbot (II) systems. In the experiments, first, subjects have to answer the main question (Q1) that requires the highest reading comprehension. Afterward, they need to answer the guiding question. After answering, they have to review the Q1 answer and decide whether to revise it or not. In the technology-free group, this process is supervised by the researcher. When the experiment is completed, all the subjects are interviewed to understand their thoughts about the question and the reasons for dealing with information in the text. In the groups that used the chatbot systems, the system can supervise the process automatically. Since the chatbot (II) is the improved version based on the experiment results of the other groups, their usability is evaluated by the SUS questionnaire and compared with each other. To understand the difference in answering behaviors of the technology-free group and the chatbot (II) group, they are compared with the number of modifications, typing words, and answering contents in the qualitative and quantitative ways.

The result of the technology-free group reveals that the subjects' interpretation of the information is complicated and diverse. Complication means there are many levels of relationships among information, and diversity means one piece of information can be interpreted in different ways. Providing the guiding questions based on the information that subjects neglect cannot efficiently assist them to modify the previous answer, and it might come from the review strategies and personal affective factors. In the usability of the chatbot systems, the chatbot (II) is improved from 60 to an acceptable 70 grade, and from the replying contents, subjects are willing to express their personal life experiences, life revelations, and critical perspectives, which are never shown in the technology-free group.

The contribution of this research is using the chatbot to collect students' complicated and diverse perspectives on reading. In the past, though using standard answers to assess students' reading comprehension is efficient, it ignores the differences in personal interpretation and their potential in designing student-based instructions. Since this research only discusses the situations of college students and of the short narrative text, in the future, the situations of K-12 students and other text types should be further discussed.

第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 3
第三節 名詞解釋 4
第二章 文獻探討 6
第一節 閱讀理解理論 6
第二節 閱讀科技發展與閱讀分析方法 11
第三節 文本的解構與重建 17
第四節 對話機器人與SUS易用性評估 24
第三章 研究方法 35
第一節 研究架構與流程 35
第二節 研究材料與工具 39
第三節 研究對象 44
第四節 多路徑問答引導設計 45
第五節 分析方式 50
第四章 研究分析 52
第一節 各題核心概念詮釋差異 52
第二節 回答形成歷程 78
第三節回答形式與常見錯誤理解 83
第四節 紙本實驗與對話機器人II閱讀行為比較 90
第五節 對話機器人I與(II)的易用性分析 97
第五章 研究結論與展望 111
第一節 讀者個人詮釋差異 111
第二節 探討影響讀者調整答題內容之成因 113
第三節 對話機器人使用性建議 116
第四節 未來展望與結語 118
第六章 參考文獻 121
第一節 中文文獻 121
第二節 英文文獻 121
第三節 網路文獻 131
附件 133
附件一：燭之武退秦師(白話版本) 133
附件二：對話機器人整體易用性測試 (Usability test)問卷題項 134
附件三：對話機器人「問答流程」易用性測試 (Usability test)問卷題項 135
附件四：對話機器人「語句選擇」易用性測試 (Usability test)問卷題項 136

第一節 中文文獻
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