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研究生: 胡智翔
Hu, Chih-Hsiang
論文名稱: 擴增實境(AR)所引導的環境感知暨無所不在學習(CAUL)之現象學研究:基於專用的學習理論與學習效果來源的AR引導CAUL設計模型
Phenomenological Research on AR-Guided Context-Aware Ubiquitous Learning (CAUL): An AR-Guided CAUL Design Model Based on Focused Learning Theories and Sources of CAUL Effectiveness
指導教授: 劉繼仁
Liu, Gi-Zen
學位類別: 碩士
Master
系所名稱: 文學院 - 外國語文學系
Department of Foreign Languages and Literature
論文出版年: 2019
畢業學年度: 107
語文別: 英文
論文頁數: 184
中文關鍵詞: 環境感知與無所不在學習無所不在學習現象學研究法擴增實境學習設計模型
外文關鍵詞: context-aware ubiquitous learning (CAUL), ubiquitous learning (u-learning), phenomenological research method, augmented reality (AR), learning design model
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  • 環境感知暨無所不在學習(以下簡稱環境感知學習)是利用行動科技、無線通訊科技及感應科技促進環境與人際互動進而達成學習效果的最新數位學習典範。近年,因為擴增實境(以下簡稱AR)能使虛擬資訊交疊於現實環境中,所以AR被廣泛利用於環境感知學習設計用以引導人與環境及學習者之間的互動。因此,AR的應用將會推動環境感知學習設計的革新,也將更新環境感知學習的知識基礎。然而,現已發現學習理論罕用於設計以AR所引導的環境感知學習中,因此本研究採用現象學研究法以回應此研究需求並更新此領域知識基礎。首先,研究者有系統性地分析從2016年至2019年中38篇AR用於教育的實證研究,研究論文來源於7本高影響力教育科技期刊,並使用基於應用在環境感知學習的熱門學習理論之分類法進行系統性的分析。分析38篇文獻後,研究者訪談七位領域知識專家以確認研究成果並蒐集專家意見。在研究討論章節中,研究者歸納出以AR所引導的環境感知學習的現況發展及近期研究潮流,並進而統整出三階段的環境感知學習設計。再者,研究者從重要的變數規律中發現特定AR功能將可引導未來環境感知學習設計學習理論的使用方向。最後,對於以AR所引導的環境感知學習,基於所蒐集的文獻資料及專家意見,研究者提出宏觀與微觀的學習設計模型。對未來研究者而言,本研究提供眾多可能的研究方向,如適合的潛在學習理論與未完全研究的學習設計因素。對教學設計者來說,以AR所引導的環境感知學習現況發展、變數的規律性以及所提出的學習設計模型可用以回顧並調整現行課程。對教師而言,本研究成果及所提出之模型不僅可凸顯出有效果的教學因素,還可以提供有利的學習規律進而有效地引導學生使用AR進行學習活動。

    Context-aware ubiquitous learning (CAUL) is the latest e-learning paradigm that utilizes a synergy of mobile, wireless communication, and sensor technologies to facilitate the learning with contextual and interpersonal information. In recent years, augmented reality (AR) has been widely-incorporated in CAUL designs to enrich and guide learners’ contextual and interpersonal interactions with its powerful capacity to overlay virtual information on physical environments. Therefore, the adoption of the new technology, AR, is bound to propel an evolution of CAUL designs and renovate the knowledge base of CAUL. However, such AR-guided CAUL designs have been identified with a lack of theoretical alignments with learning theories. Therefore, in this research, phenomenological research method was adopted to address the research needs and update the knowledge base of AR-guided CAUL designs. The researcher first systematically reviewing 38 empirical studies of AR in education published from 2016 to 2019 in 7 high-impact journals of educational technology by using a taxonomy that classified popular learning theories for CAUL. Second, seven domain knowledge experts were interviewed to check and provide insights about the identified current developments, patterns of variables, and the proposed models. In Discussion Section, the current developments were highlighted with the recent trends of such learning designs, and three stages of CAUL developments were identified. Moreover, the critical patterns were meaningfully organized with the unique AR functions to guide the future adoption of learning theories in designing AR-guided CAUL. At the end, a macro-design model and three micro-design models of AR-guided CAUL were solidly proposed. For researchers, this study offered a range of perspective research directions from (a) the potential learning theories to be incorporated in such learning design to (b) the components that were rarely investigated. For instructional designers, the current states of AR-guided CAUL, the patterns of the variables, and the proposed models provided insights that would be useful for reviewing and even revising current curricula. For teachers, the findings and the models could not only serve to highlight the effective components in learning, but also offer prospective patterns of variables that could be used to successfully guide the learners with AR.

    Abstract in Chinese ii Abstract in English iii Table of Contents v List of Tables ix List of Figures xi List of Abbreviations xiii CHAPTER ONE INTRODUCTION 1 1.1 Background of the Study 1 1.2 Statement of Problem and the Need for the Study 2 1.3 Purpose of the Study 3 1.4 Significance of the Study 3 1.5 Definitions of Key Terminologies 4 1.5.1 Context-aware Ubiquitous Learning (CAUL) 4 1.5.2 Interactivity 4 1.5.3 Augmented Reality (AR) 5 1.5.4 Learning Guidance 5 1.5.5 Learning Backgrounds 5 1.5.6 Sources of CAUL Effectiveness 5 1.5.6.1 Mechanisms of Learning Guided by the System 6 1.5.7 Learning Outcomes 7 CHAPTER TWO LITERATURE REVIEW 8 2.1 Context-aware Ubiquitous learning (CAUL) as a Learning Design 8 2.1.1 Defining Three Roles in CAUL 12 2.1.1.1 Learner 13 2.1.1.2 Context 13 2.1.1.3 Peer 14 2.2 Interactivity of CAUL 15 2.3 CAUL System as a Learning Technology (LT) to Guide Learning 19 2.4 Relevant Review of Research on CAUL and the Findings 22 2.4.1 Technological Infrastructures of CAUL 22 2.4.2 Educational Opportunities and Instructional Designs of CAUL 24 2.4.3 Focused Learning Theories Found in CAUL review papers 26 2.5 The Learning Theories in CAUL Designs Based on the Three Interaction types 28 2.5.1 The Learning Theories and the Mechanism of Learning Guided in the Learner-CAUL System Interaction 29 2.5.2 The Learning Theories and the Mechanism of Learning Guided in the Learner-Context Interaction 31 2.5.3 The Learning Theories and the Mechanism of Learning Guided in the Learner-Peer Interaction 34 2.6 Augmented Reality (AR) as a New LT for CAUL Design 37 2.6.1 Defining AR 38 2.6.2 Relevant Review Research Papers of AR and the Findings 39 2.6.3 AR Educational Fields 40 2.6.4 Learning Theories Used in AR-based Instructional Designs 41 2.6.5 Focuses of Review Papers on AR types, Functionalities, Instructional Techniques and Strategies, and Learning Outcomes 42 2.7 The Need for this Study 46 2.7.1 The Need for a CAUL Design Model 46 2.7.2 The Need for an AR-based Learning Design Model 49 2.8 Summary of This Chapter 50 2.8.1 Research questions 51 CHAPTER THREE METHODOLOGY 52 3.1 Rationale of the Methodology 52 3.2 Bracketing the Phenomenon 53 3.2.1 The Taxonomy for AR-based CAUL Designs 54 3.2.1.1 Learning background 57 3.2.1.2 Sources of CAUL effectiveness 57 3.2.1.3 Learning outcomes 61 3.3 Collecting Empirical and Interview Data 62 3.3.1 Collecting the Empirical data 62 3.3.2 Collecting the Interview data 66 3.4 Identifying Meaningful Statements 68 3.5 Giving Meaning to the Statements 68 3.6 Creating Thick Descriptions 68 CHAPTER FOUR RESULTS 69 4.1 Current Developments of AR-guided CAUL Designs 69 4.1.1 Descriptive Statistics from the Review of 38 AR Empirical Papers 69 4.1.2 Experts’ Insights about the Current Developments of AR-guided CAUL Designs 79 4.1.2.1 Learning Background 79 4.1.2.2 Sources of CAUL effectiveness 86 4.1.2.3 Learning outcomes 91 4.2 The Patterns of the Variables 95 4.2.1 The Patterns of the Variables Observed from the 38 AR Papers 95 4.2.2 Experts’ Insights about the Patterns among the Variables 99 4.3 An AR-guided CAUL Design Model 112 4.3.1 A Learning Design Model for AR-guided CAUL Based on the Patterns Found from the Empirical Data 112 4.3.2 Experts’ Insights about the Learning Design Model 117 4.4 Contradictory findings from empirical and interview data 122 4.5 Summary of the Findings 122 CHAPTER FIVE DISCUSSION 124 5.1 Current developments of AR-guided CAUL designs 124 5.1.1 Learning backgrounds of AR-guided CAUL designs 130 5.1.2 Sources of CAUL Effectiveness of AR-guided CAUL Designs 131 5.1.3 Learning Outcomes of AR-guided CAUL Designs 134 5.1.4 The Stages of CAUL in Terms of Locus of Control – Past, Present, and Future 135 5.2 Patterns that Guided the Adoption of Learning Theories in AR-guided CAUL Designs 140 5.3 The Proposed AR-guided CAUL Design Model 144 5.4 Limitations of the Present Study 150 CHAPTER SIX CONCLUSIONS 152 6.1 Summary of the Research Purposes, Methodology, and Main Findings 152 6.2 Contributions of This Present Study 153 6.3 Implications for Future Research 154 References 157 Appendices 170 Appendix A 170 Appendix B 173 Appendix C 181 Appendix D 182

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