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研究生:陳冠廷
研究生(外文):Kuan-Ting Chen
論文名稱:利用言語行為理論設計問題本位學習中討論輔助精靈
論文名稱(外文):Designing a Problem-Based Learning Conversation Agent based on Speech Act Theory
指導教授:賀嘉生賀嘉生引用關係
指導教授(外文):Jia-Sheng Heh
學位類別:碩士
校院名稱:中原大學
系所名稱:資訊工程研究所
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:2006
畢業學年度:94
語文別:中文
論文頁數:71
中文關鍵詞:問題本位學習討論輔助精靈言語行為理論知識地圖
外文關鍵詞:Problem-Based LearningKnowledge MapConversation AgentSpeech Act Theory
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討論是學生互相交換知識、分享心得的重要學習過程。本論文最終目的在設計一個輔助討論的智慧型代理人(conversation agent),試著將此輔助精靈機制應用於問題本位學習(Problem-Based Learning, PBL)教學過程的討論步驟中,並且期望在討論過程中達到引導學生討論並監控討論過程的目的。
本研究首先將討論資料分成兩大部分:發言話語相關與發言話語無關部分。發言話語相關主要運用的是語用學(Pragmatic)與語意學(Semantic)兩種理論做基礎來分析討論話語內容;發言話語無關的部份則是分析發言者與發言時間這兩種類型的討論狀態。
本研究根據文獻與研究,以言語行為理論(Speech Act Theory)中的不同語用行為四種分類:詢問型(request)、回覆型(reply)、評價型(evaluation)、招呼型(chat),相對應地建立語用關鍵字彙(pragmatic keywords)來切割語用段落類型,另外配合使用知識地圖(Knowledge Map)之概念階層方式來儲存語意關鍵字彙(semantic keywords),進一步設計演算法來分析討論,產生語用語意分析報告(pragmatic-semantic analysis report),提供給教師作為教學參考依據。
經由以上的語用語意分析為基礎,建構討論精靈則包含有三個主要流程步驟:感知討論狀態、規則庫條件萃取、產生輔助語句。首先感知討論的不同種狀況,接下來將討論的狀況當作條件,輸入規則庫(Rulebase),經由規則的萃取,輸出輔助精靈的話語對策,最後根據這樣的對策從話語的資料庫中取出相對應的輔助精靈話語,輸出至聊天室中,輔助學生的討論。
透過討論輔助精靈的參與,在小組討論時,教師本身可以負責較為需要照護的組別,而將某些組別交由討論輔助精靈監控整個討論過程。精靈一方面偵測發言話語相關的狀態:計算討論的概念數量、概念類型、語用數量三種不同方向;另一方面,也可以偵測與發言話語無關的狀態:分析討論參與人數、討論經過時間以及討論頻率等,最後再根據這些討論狀態發出相對應語句輔助小組的討論。
Discussion is a good way for students exchanging their idea and finding out the solution of a problem. In the chat-room of an e-learning platform, a teacher could not participate in all discussion of different groups at the same time. An intelligent agent which has ability to analyse student’s conversation could help teachers solve this problem. This research focuses on how to generate a conversation agent with the abilities of participating in students’ discussion and navigating the direction of the discussion.
The method of analyzing conversation data is divided into two types: sentence content-dependent analysis and sentence content-independent analysis. The sentence content-dependent analysis of a conversation is related to the theories of Pragmatic and Semantic. The sentence content-independent part is related to conversation participants and speaking timing.
Based on these analyses, this research constructs a conversation agent for navigation in students’ discussion. The process of conversation agent for navigating conversation includes the following steps: sensing conversation, extracting rules, and generating guidance sentence.
In the end, the conversation agent could not only participate in a chat-room on web, but also generate a Pragmatic-Semantic Analysis Report for teachers. When the agent involves in the discussion of chat-room, it would speak sentences with different sentence strength based on the sentence content-dependent and sentence content-independent analyses. After the conversation, the agent would generate the Pragmatic-Semantic Analysis Report according to the speech act of the conversation and the keywords discussion in this conversation. This report would help teachers know the discussion style and the most discussion concepts of each discussion group.
摘要…………………………………………………………………………………..Ⅰ
Abstract……................................................................................................................Ⅱ
誌謝…………………………………………………………………………………..Ⅲ
目次…………………………………………………………………………………..Ⅳ
圖次…………………………………………………………………………………..Ⅵ
表次………………………………………………………………………………….Ⅶ

第一章 緒論 1
1.1動機與目的 1
1.2論文貢獻 2
1.3內容章節概述 3
第二章 文獻探討 4
2.1數位學習與問題本位學習 4
2.1.1數位學習 4
2.1.2問題本位學習與教師角色 5
2.1.3數位學習中之問題本位學習 6
2.2智慧型代理人與知識架構 7
2.2.1智慧型代理人 7
2.2.2知識地圖 8
2.2.3概念範疇與認知模式 9
2.3話語分析與會話分析 11
2.3.1話輪與話輪交接規則 12
2.3.2語用學與言語行為理論 13
2.3.3對答結構 15
2.4問題表述 17
2.4.1分析討論資料元素並產生對話 17
2.4.2期望結果 17
第三章 討論內容資料分析 18
3.1討論資料分析 18
3.2發言話語相關-討論語用分析 19
3.2.1參與者語句語用行為(Participants’ Pragmatic Behavior) 19
3.2.2會話語用段落類型(Conversation Pragmatic Segment Type) 21
3.3發言話語相關-討論語意分析 24
3.3.1語意概念階層(Semantic Concept Hierarchy) 24
3.4發言話語無關討論分析 26
3.5討論狀態(Discussion Situation)分析 27
3.5.1狀態對象(Target) 27
3.5.2狀態類型(Situation Type) 27
3.5.4狀態強度(Intensity) 28
3.6討論之教師話語 29
3.6.1問題本位學習教師話語語用行為 29
3.6.2 教師話語強度(Sentence Strength) 30
第四章 討論輔助精靈與語用語意分析演算法 31
4.1討論資料蒐集 31
4.2即時聊天室輔助學習精靈模型 32
4.2.1即時聊天室討論狀態感知 34
4.2.2即時聊天室討論規則對應與對策產生 38
4.2.3即時聊天室輔助話語輸出 40
4.3語用語意分析演算法 41
4.3.1 語用分析演算法 41
4.3.2語意分析演算法 44
第五章 系統實作與資料探勘 46
5.1實驗系統資料來源 46
5.2即時聊天室討論輔助精靈系統 46
5.3討論資料之探索 48
5.3.1討論資料語用語意分析報告 48
5.3.2資料統計分析 53
第六章 結論 57
6.1總結 57
6.2未來研究方向 57
參考文獻 58
附錄A 桃園縣某私立高職討論資料(部分) 61
附錄B 中原大學資料庫系統課程討論資料(部分) 65

圖次
圖2 1 智慧型代理人架構 7
圖2 2 概念階層架構 8
圖4 1 討論輔助精靈流程 32
圖4 2 討論輔助精靈架構 33
圖4 3 概念數量強度標準 36
圖4 4 概念類型強度標準 36
圖5 1 討論輔助精靈介面與結果 47
圖5 2 聊天資料表 49
圖5 3 概念階層表 50
圖5 4 語用關鍵字表 51
圖5 5 語用語意分析報告一(中原大學資料庫系統課程) 52
圖5 6 語用語意分析報告二(私立高職課程) 53
圖5 7 語用段落類型數量總計 (私立高職課程) 54
圖5 8 學生詢問教師討論數量分析 (私立高職課程) 54
圖5 9 資訊詢問者數量分析 (私立高職課程) 55

表次
表2-1 概念基模狀態表................................................................................................9
表3-1 語用行為分類表..............................................................................................20
表3-2 語用段落類型..................................................................................................23
表3-3 語意概念階層..................................................................................................24
表4-1 語用分析演算法使用變數資料......................................................................41
表4-2 語用分析演算法..............................................................................................42
表4-3 語用分析演算法例子......................................................................................44
表4-4 語意分析演算法使用變數資料......................................................................44
表4-5 語意分析演算法..............................................................................................45
表4-6 語意分析演算法例子......................................................................................45
表5-1 語用語意統計數量 (資料庫系統課程) ....................................................55
表5-2 概念類型數量統計 (資料庫系統課程) ....................................................56
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