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研究生: 林羿婷
Lin, Yi-Ting
論文名稱: 大學生使用經驗學習環於影像辨識平台上學習人工智慧應用程式之學習成效
Learning Effectiveness of Undergraduates Using the Experiential Learning Cycle to Learn Artificial Intelligence Application on Image Recognition Platform
指導教授: 許庭嘉
Hsu, Ting-Chia
口試委員: 區國良
Ou, Kuo-Liang
謝易錚
Hsieh, Yi-Zeng
口試日期: 2021/07/07
學位類別: 碩士
Master
系所名稱: 科技應用與人力資源發展學系
Department of Technology Application and Human Resource Development
論文出版年: 2021
畢業學年度: 109
語文別: 中文
論文頁數: 133
中文關鍵詞: 運算思維教育人工智慧教育機器學習影像辨識
英文關鍵詞: computational thinking, artificial intelligence course, machine learning course, image recognition
研究方法: 準實驗設計法
DOI URL: http://doi.org/10.6345/NTNU202100856
論文種類: 學術論文
相關次數: 點閱:226下載:0
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  • 本研究發展一個人工智慧影像辨識課程,適合無運算思維及人工智慧基礎之大學生,培養學生運算思維能力與認識人工智慧,使用準實驗研究方法,對於大學生使用經驗學習環進行人工智慧影像辨識課程之學習成效進行研究。研究結果顯示採用不同的教學方式皆可以增加學生的運算思維與人工智慧概念,經驗學習環較適合初始具有較低運算思維能力與較低自我效能的學生,因為經驗學習環具有反思、抽象化及主動驗證歷程,能夠讓學生產生討論、合作與直接操作的行為,這些行為的出現能夠提升初始運算思維能力與自我效能較低的學生運算思維能力與自我效能程度。而主題式導向學習較適合初始具有一定運算思維能力及高自我效能的學生,因為其過去的學習經驗已經習慣講述式的示範教學方法,延續習慣的且表現不錯的學習方法較能夠維持良好的運算思維感知程度。

    This research develops an artificial intelligence image recognition course, which is suitable for college students without computational thinking and artificial intelligence (AI) foundation. This course cultivates the computational thinking ability of students and their recognition of AI knowledge. This research uses quasi-experimental research methods to study learning effects. The research results show that different teaching methods can increase students' computational thinking and artificial intelligence concepts. The experiential learning cycle is more suitable for students who initially have lower computational thinking ability and lower self-efficacy. The stage of reflection, abstraction and operation can enable students to generate discussion, cooperation, and direct manipulation behaviors. These behavior patterns can enhance the computational thinking ability and self-efficacy. The subject-based learning is more suitable for students who have certain computational thinking ability and high self-efficacy initially. Their past learning experience has become accustomed to the lecture teaching method. Lecture continues the habit is better for them able to learn.

    第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的與待答問題 7 第三節 重要名詞釋義 9 第二章 文獻探討 11 第一節 運算思維的學習方法 11 第二節 視覺化程式設計 20 第三節 人工智慧 23 第三章 研究設計與實施 31 第一節 課程與教材設計 31 第二節 研究架構與假設 46 第三節 研究步驟與實驗流程 48 第四節 研究對象 52 第五節 研究工具 53 第六節 資料分析 57 第四章 研究結果與分析 61 第一節 學習成效 61 第二節 運算思維感知 71 第三節 監督式機器學習自我效能 81 第四節 人工智慧焦慮 84 第五節 行為分析 87 第五章 結論與建議 95 第一節 研究結果與討論 95 第二節 研究限制與未來研究建議 103 參考文獻 105 一、 中文部分 105 二、 外文部分 106 附 錄 121 附錄一 運算思維與人工智慧概念測驗卷 122 附錄二 電腦程式自我效能量表 131 附錄三 人工智慧焦慮量表 132 附錄四 監督式機器學習自我效能量表 133

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