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研究生: 陳錦瑜
Chin-Yu Chen
論文名稱: 結合概念圖之人工智能聊天機器人方法對EFL學生英語口語表達能力的影響
Effects of an integrated concept mapping and AI chatbot-based learning approach on EFL students English oral presentation performance
指導教授: 黃國禎
Gwo-Jen Hwang
口試委員: 楊接期
Jie Chi Yang
許庭嘉
Ting-Chia Hsu
翁楊絲茜
Cathy Weng
王淑玲
Shu-ling Wang
學位類別: 博士
Doctor
系所名稱: 人文社會學院 - 數位學習與教育研究所
Graduate Institute of Digital Learning and Education
論文出版年: 2021
畢業學年度: 109
語文別: 英文
論文頁數: 53
中文關鍵詞: 人工智慧聊天機器人概念構圖社會文化語境
外文關鍵詞: artificial intelligence, chatbots, concept mapping, sociocultural situated contexts
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  • 隨著全球化的發展,全世界對有效的英語溝通的需求一直在增加。在英語教學中,缺乏有意義的演練情境來提高學生的口語表達能力,始终是最大的挑戰之一。社會文化語境是學生熟悉的情境,學生根據熟悉且認同的社會文化情境,對外籍人士進行說明與溝通,能夠產生強烈的文化自尊與熟悉感,進而增進對學習的接受度。傳統的語言教室,比較無法提供配合使用者個人興趣或需求的有意義的學習情境,人工智慧聊天機器人能夠提供多模式的訊息,包括影片,圖片,語音及文字等等,使用者也可以透過語音辨識系統如ARS與關鍵字系統KWS等,直覺地獲得有興趣且符合個別需求的訊息。然而,如果没有適當的學習鷹架,學生面對大量的資訊,往往無法將概念和訊息组織成完整的表達形式,順利進行溝通。概念圖是表示概念知識结構的一種有效的可視化認知工具,能在任務前的规劃過程中,預先畫出重點想法和連接思想的圖表,以支持學生理解概念的意義,並透過系统的组織和歸納,將複雜的知識轉化為可視化的概念圖,讓學生進行有意義的學習。本文旨在研究將概念圖與人工智慧聊天機器人整合,提供EFL學生在英語口語表達前的練習與組織,並探究其對EFL學生在學習成绩、學習動機和自我效能等方面的影響。實驗结果顯示,使用概念圖整合人工智慧聊天機器人方法(CM-AI)的學生,在學習成绩、口語溝通焦慮等方面都比使用傳統人工智慧方法(C-AI)的學生有更佳的表現。


    This study examines four elements of EFL students’ oral presentation performance, motivation, self-efficacy, and oral communication apprehension, in an EFL speaking course that uses the concept mapping-integrated AI chatbot approach. The study found that a preparatory concept mapping task before the AI chatbot sessions improved students’ perceived motivation, self-efficacy, and decreased oral communication apprehension. Taking into account insights from EFL research, the three-week task-based activities are organized to help high school students to enhance English oral presentation performance. A quasi-experimental design was conducted in a Northern Taiwanese high school, thirty-six high school students were randomly assigned into experimental group and control group, the experimental group students used concept mapping-integrated AI chatbot approach (CM-AI) while the control group students used conventional AI chatbot approach (C-AI) in the online sociocultural contexts with international students. Results reveals that concept mapping-integrated AI chatbot approach (CM-AI) can improve students’ oral presentation performance, motivation and self-efficacy better than those using the conventional AI chatbot approach (C-AI). EFL instructors should use concept mapping in a pre-task planning to support learners engaged in AI chatbot-based speaking activities with native or non-native speakers. When the students used concept mapping to prepare for the AI chatbot-based speaking activities, they exhibited higher motivation and self-efficacy in their oral presentation performance in the online sociocultural contexts.

    TABLE OF CONTENTS Chapter 1 Introduction 1 1.1 BACKGROUND AND MOTIVATION 1 1.2 SIGNIFICANCE OF THE STUDY 2 1.3 DEFINITION 4 1.3.1. AI chatbot 4 1.3.2. MindMeister 4 1.3.3. Google Meet 4 1.3.4. Google Classroom 5 1.4 SCOPE AND LIMITATIONS 5 1.5 RESEARCH QUESTIONS 6 Chapter 2. Literature Review 7 2.1 SOCIOCULTURAL SITUATED CONTEXTS IN ENGLISH ORAL PRESENTATION LEARNING 7 2.2 ARTIFICIAL INTELLIGENCE AND CHATBOTS IN FOREIGN LANGUAGE EDUCATION 7 2.3 CONCEPT MAPPING IN FOREIGN LANGUAGE LEARNING 11 2.4 ONLINE SYNCHRONOUS CMC 13 Chapter 3. Integrated concept mapping and AI chatbot-based learning environment 14 Chapter 4. Experimental design 18 4.1. PARTICIPANTS 18 4.2. EXPERIMENTAL INSTRUMENTS 18 4.2.1. AI chatbot 18 4.2.2. The concept mapping software 19 4.2.3. The online synchronous CMC application 19 4.2.4 The scoring rubrics for pre-and-post tests 19 4.2.5. Pre-and-post questionnaires 21 4.2.6 The open-ended questionnaire of interview 22 4.3 EXPERIMENTAL PROCEDURE 22 Chapter 5. Experimental results 25 5.1 ANALYSIS OF ORAL PRESENTATION PERFORMANCE 25 5.2 ANALYSIS RESULTS OF SELF-EFFICACY 26 5.3 ANALYSIS RESULTS OF LEARNING MOTIVATION 26 5.4. ANALYSIS RESULTS OF ORAL COMMUNICATION APPREHENSION 27 Chapter 6. Qualitative results 29 Chapter 7. Discussion 34 Chapter 8. Conclusions 37 Chapter 9. Limitations and future research 38 References 40

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