研究生: |
吳玟秀 Wu, Wen-Hsiu |
---|---|
論文名稱: |
對話機器人融入文本之多路徑閱讀理解研究 A Study of Using the Chatbot to Implement Multipath Reading Comprehension in Text |
指導教授: |
廖冠智
Liao, Guan-Ze |
口試委員: |
陳明蕾
蔡宗翰 黃國珍 Chen, Ming-Lei Tsai, Tzong-Han |
學位類別: |
碩士 Master |
系所名稱: |
竹師教育學院 - 學習科學與科技研究所 Institute of Learning Sciences and Technologies |
論文出版年: | 2023 |
畢業學年度: | 111 |
語文別: | 中文 |
論文頁數: | 136 |
中文關鍵詞: | 閱讀行為 、閱讀理解 、對話機器人 、建構-整合模型 |
外文關鍵詞: | Reading Behavior, Reading Comprehension, Chatbot, Construction and Integration Model |
相關次數: | 點閱:2 下載:0 |
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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.
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