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研究生:藍中賢
研究生(外文):Chung-Hsien Lan
論文名稱:以代理人協商機制強化學習之互動與成效
論文名稱(外文):Learning through Negotiation
指導教授:賴國華
指導教授(外文):K. Robert Lai
學位類別:博士
校院名稱:元智大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:英文
論文頁數:139
中文關鍵詞:合作學習代理人協商模糊限制同儕互評問題導向學習適性學習
外文關鍵詞:collaborative learningagent negotiationfuzzy constraintspeer assessmentproblem-based learningadaptive learning
相關次數:
  • 被引用被引用:1
  • 點閱點閱:216
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  • 收藏至我的研究室書目清單書目收藏:2
本論文以社會運算(Social Computing)的觀點,提出「經由協商,促進學習」(Learning through Negotiation)的概念,探討在數位學習中,如何透過代理人協商機制來強化學習互動,進而拓展教學策略與提升學習成效。
在數位學習中,「互動」(Interaction)乃是教學策略的一環,也是影響學習成效的重要因素。尤其是在學習與教學的互動過程中,學生、同儕、及教師之間,往往存在不同的觀點或認知的差異。因此,如何透過某種機制,即時處理這些因認知差異或是利益衝突所造成的困惑,達到彼此間之共識,提供學生、同儕、或教師深度思考及自我調整的空間,對於數位學習與教學的成效,將具有關鍵性的影響。
針對此問題,我們引進代理人技術,將學習環境視為一個多重代理人的系統,協助學生、同儕、及教師進行學習的活動。並且透過信息的交換,促進代理人之間的互動溝通,創造豐富的社會行為與社群營造。而互動過程中所衍生的認知差異或是利益衝突,則可透過代理人協商機制,獲得有效的解決。另外,考量知識表達與計算效能,我們以模糊限制(Fuzzy Constraints)為基礎,建構代理人的協商機制,並且落實於三種學習情境,包括:同儕互評(Peer Assessment)、問題導向學習(Problem-based Learning)、及適性學習(Adaptive Learning),來評量如何經由協商,強化學習之互動與成效。
在同儕互評中,利用模糊限制,可彈性呈現同儕的評量,並依其特質給予調整,然後透過協商機制,同儕與學習者之間在評量上達成共識。由實驗證實,學習者比較願意接受經由協商而來的評量結果。經由反覆的協商與互動,也能提供學習者更為豐富的回饋,認真的反思,提升學習動機及成效。而教師也能藉由觀察學生參與評量的過程,瞭解其學習狀況,適當的調整教學策略。
在問題導向學習中,我們採用遊戲式合作學習概念建構一個商業模擬情境,以啤酒遊戲為例,協助學生學習供應鏈的決策管理。教師負責設計問題的情境,學生可以扮演多種角色,模擬真實的商業經營情境,並與同儕或虛擬的代理人互動與協商,共同解決問題。在協商過程中,學生嘗試解決問題的同時,也能取得所需的資訊,如果有違反限制或決策不當,系統會自動提示並指引解決方案。經由反覆的協商互動與找尋解決方案,學生可以不斷的反省自我認知,吸取更多的知識,並且達成學習目標。
在適性學習中,突破傳統固定學習路徑的教學方式,我們考量學生個人的學習目標與特質,提供適性的學習內容,並允許學生與虛擬教師協商彼此的認知差異,達成共識後,再決定下一個課程教材。由實驗得知,學生可以模糊限制有效表達自己的學習認知,並經由代理人協商機制,更有效率的找到適切的個人化學習路徑。
經由上述這些學習情境的實驗,我們發現經由代理人協商模式,確實可以強化學習的互動與成效。
This dissertation presents a theory of learning through agent negotiation. In a computer-supported learning environment, interaction plays a critical role for developing instructional strategies and promoting learning effectiveness. Especially, learners, peers and instructors often bring different ideas and perspectives to the learning process. Thus, how to resolve these differences and without creating unnecessary discontentment could be of paramount important for the effectiveness of learning.
To that end, a learning environment is viewed as a multi-agent system, in which agents can support effective interactions between each other to create a social community with rich social behaviors. Then, conflicts and discontentment can be reconciled through agent negotiation mechanism. Accordingly, a fuzzy constraint-directed agent negotiation is proposed and then implemented in supporting three learning scenarios, including peer assessment, game-based problem-based learning and adaptive learning, to illustrate how to incorporate the notion of agent negotiation into a learning environment and, more importantly, to improve the effectiveness of learning.
In peer assessment, the proposed model facilitates learners in grading peer work with fuzzy constraints and, to reduce the bias, assessments can be adjusted according to personal characteristics. Through iterative negotiation, learners can reach an agreement with peers for the assessment. Experimental results suggest that learners are more willing to accept the assessment and able to acquire richer feedback to reflect upon their work seriously and enhance learning effectiveness. Moreover, instructors can understand learners’ performances to appropriately adjust instructional strategies by observing learners’ participation and assessment result.
In problem-based learning, a game-based collaborative business simulator is developed to provide learners an environment for learning strategic planning and decision making in a beer game. In this agent-based business simulator, instructors are responsible to describe the problems of scenarios, and learners can play multi-roles to mimic more closely to the real-world business scenario and negotiate with either peers or virtual agents to solve the problems. During the process of negotiation, learners can acquire necessary information while attempting to develop the solutions. Moreover, the business simulator can also guide the learners to explore the decision alternatives. Through the process of iterative negotiation, learners can reflect upon self-cognition and also acquire necessary knowledge to achieve the learning goals.
In adaptive learning, breaking from traditional instructional approaches, the proposed framework considers learners’ learning goals and personal characteristics to provide adaptive learning materials and allow learners to negotiate mutual cognitive differences in knowledge levels and learning concepts with virtual tutor to decide next learning materials. Experimental results show that learners can represent self-cognition by using fuzzy constraints and discover better learning sequences through negotiation with virtual tutor.
In summary, all these learning scenarios reveal that the notion of negotiation can be a valid approach for promoting learning effectiveness.
List of Figures xiii
List of Tables xv
Chapter 1 Introduction 1
1.1 Interaction as an Instructional Strategy 1
1.2 Scope of the Work 2
1.3 Dissertation Organization 8
Chapter 2 Conceptual Framework 9
2.1 Social Computing 9
2.1.1 Framework of Social Computing 9
2.1.2 Social Computing in education and e-learning 11
2.2 Agent-based e-Learning 12
2.2.1 Why Agent-based Approach? 12
2.2.2 Multi-agent Learning System 14
2.3 Interaction between Learning Agents 18
2.3.1 Interaction Types and Cycle 18
2.3.2 Interaction in Learning Activities 22
2.4 Negotiation in Supporting Learning 25
2.4.1 Essence of Negotiation 25
2.4.2 Related Work of Negotiation in Learning 28
Chapter 3 Negotiation Model 31
3.1 Agent Negotiation Process 31
3.2 Negotiation Mechanisms 36
3.3 Fuzzy Constraint-directed Agent Negotiation 42
Chapter 4 Negotiation in Peer Assessment 47
4.1 Peer Assessment 47
4.2 Peer Assessment through Agent Negotiation 51
4.3 An Illustration of Negotiation-based Peer Assessment 58
4.4 Experiments and Evaluation 66
4.5 Summary 73
Chapter 5 Negotiation in Problem-based Learning 76
5.1 Collaborative Learning in Supporting Problem Solving 76
5.1.1 Game-Based Problem-based Learning 81
5.1.2 Business Simulation Game 82
5.2 MANAGER:A Business Simulator for Role-play Game 85
5.2.1 System Architecture 86
5.3 Beer Game: An Illustrative Example 91
5.3.1 Beer Game 92
5.3.2 Walk-through Examples 94
5.4 Summary 103
Chapter 6 Negotiation in Adaptive Learning 105
6.1 Adaptive Learning 105
6.2 Locus of Control in Adaptive Learning 109
6.3 Personalized Learning Sequences: An Illustrative Example 113
6.4 Experimental Results 121
6.5 Summary 123
Chapter 7 Conclusions 125
7.1 Contributions 125
7.2 Limitations and Future Directions 127
Bibliography 129
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