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研究生:李春雄
研究生(外文):Chun-Hsiung Lee
論文名稱:數位學習概念圖之應用與其接受度因果模式之建構
論文名稱(外文):The Application of e-Learning Concept Map and its User Acceptance Causal Model
指導教授:李國光李國光引用關係呂永和
指導教授(外文):Gwo-Guang LeeYungho Leu
學位類別:博士
校院名稱:國立臺灣科技大學
系所名稱:資訊管理系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:中文
論文頁數:114
中文關鍵詞:數位學習概念圖智慧型概念診斷系統結構方程模式
外文關鍵詞:E-learningConcept mapIntelligent Concept Diagnostic SystemStructural equation modeling
相關次數:
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中文摘要
由於數位學習環境並非透過老師面對面的授課與引導,因而除了學習者必須要主動的學習外。更需要有良好的教材引導,避免產生學習者迷失、完善的知識結構整合等。因此,如何藉由數位學習平台提供有用的知識概念圖,以引導迷失的學習者快速找到合乎個人需求之知識。雖然概念圖在教育上可用以表達知識結構及診斷學生迷失,但多數學習概念圖的建構需透過領域專家或教育學者之建議以建構學習概念,而且知識擷取過程相當冗長費時。
因此,如何快速有效率的建構學習者的概念圖為推動數位學習之重要研究議題。本研究設計Association Rules for Concept Map演算法,並實際開發一套智慧型概念診斷系統(Intelligent Concept Diagnostic System, ICDS),提供教師快速建構學習者之概念圖,以即時診斷學習者的學習障礙與迷思,藉由「適性化補救學習路徑 (Adaptive Remedial-Instruction Path ; ARIP)」演算法,可依照不同受測者的迷失概念,由系統自動建立適性化補救學習路徑提供學習者參考。
本研究發現「中分群」與「低分群」學習者透過適性化補救學習路徑引導後有顯著的進步。因此,本研究根據概念圖對學習成效所作的研究結果作為理論基礎,進一步探討影響數位學習的學習成效和接受度之相關文獻,以建構數位學習環境的接受度因果模式,此模式包含教材內容、概念圖、自發互動性、學習成效和接受度五個潛在變項及20 個觀察指標。最後,利用結構方程模式(SEM)統計分析方法,分析254位學習者的問卷,以驗證學習者對於數位學習之接受度模式。
Abstract
In a non-synchronous e-learning environment, owing to the lack of instruction and guidance by a real teacher, learners must take the initiative to learn. However, if the teaching materials are poor, learners face the three problems of:control of learners, disorientation, and cognitive overload in the e-learning environment. Therefore, providing a useful concept map through an e-learning system to guide the confused learners to find the knowledge they need is an important issue of promoting e-learning.
The concept map proposed by J.D. Novak is a good tool to portray knowledge structure and to diagnose students’ misconception in education. However, most of the learning concept maps have to be constructed through the suggestions of experts or scholars in related realm. It is really a complicated and time-consuming knowledge acquisition process.
The study proposed to apply the algorithm of Association Rules for Concept Map to develop an Intelligent Concept Diagnostic System (ICDS). It provides teachers with constructed concept maps of learners rapidly, and enables teachers to diagnose the learning barriers and misconception of learners instantly. The best Adaptive Remedial-Instruction Path (ARIP) can be reached through the algorithm of RIP suggested in this study.
First, according to the finding of this study, “medium-score cluster” and “low-score cluster” had significant improvement after using the guides of adaptive remedial-instruction path. Therefore, the finding of the effect of the concept map is used as the basis of theory in this study. The literature review in relation to the variables of e-learning effect and the acceptance causal model of the concept map is further explored. The construct of the e-learning acceptance causal model consists of five potential variables and twenty observing indices. The five potential variables are the content of teaching materials, the use, the interaction, the leaning effect, and the acceptance of the concept map. Finally, the structural equation modeling is applied to collect the data of 254 senior high students who studied program languages. The data is applied to construct the learner’s acceptance causal model of e-learning.
目錄
中文摘要--------------------------------------------------------------------------- Ⅰ
Abstract----------------------------------------------------------------------------- Ⅱ
誌謝--------------------------------------------------------------------------------- IV
目錄--------------------------------------------------------------------------------- V
圖目次------------------------------------------------------------------------------ Ⅷ
表目次------------------------------------------------------------------------------- X
第一章 緒論----------------------------------------------------------------- 1
1-1 研究背景與動機------------------------------------------------ 1
1-2 研究架構與目的------------------------------------------------ 3
1-2.1 研究兩階段架構-------------------------------------- 3
1-2.2 研究目的----------------------------------------------- 4
1-3 論文架構--------------------------------------------------------- 5
第二章 文獻探討---------------------------------------------------------- 7
2-1 線上學習(e-Learning) ----------------------------------------- 7
2-2 電腦化適性測驗------------------------------------------------ 13
2-2.1 以「試題反應理論」為基礎的適性測驗-------- 13
2-2.2 以「知識或試題結構」為基礎的適性測驗----- 14
2-3 概念圖(Concept Map) ------------------------------------------ 14
2-3.1補救教學結構 ------------------------------------------ 17
2-3.2 補救學習路徑(Remedial-Instruction Path) -------- 17
2-3.3 適性化補救學習路徑---------------------------------- 18
2-4 資料探勘(Data Mining) ----------------------------------------- 20
2-4.1知識發現的過程----------------------------------------- 22
2-4.2關聯規則(Association rule) --------------------------- 25
2-5 科技接受模式(TAM) ----------------------------------------- 27
2-6 網路教學接受度----------------------------------------------- 29
2-7 結構方程模式(Structural Equation Modeling)--------- 29
2-8 數位學習之互動性--------------------------------------------- 37
2-9 數位學習之教材內容------------------------------------------ 39
2-10 結語--------------------------------------------------------------- 39
第三章 Association Rules for Concept Map演算法------- 41
3-1 設定試題之概念權重------------------------------------------- 46
3-2 記錄受測者測驗歷程------------------------------------------- 47
3-3 利用Association Rules演算法找出所有高頻項目集----- 48
3-4 試題關連法則---------------------------------------------------- 49
3-5 將「試題關連法則」轉換成「概念與概念」的影響程度 50
3-6 建構初步之學習概念圖---------------------------------------- 51
3-7 無概念循環之學習概念圖------------------------------------- 52
3-8 先後順序調整之學習概念圖---------------------------------- 52
3-9 結語---------------------------------------------------------------- 54
第四章 自動化建構學習概念圖之智慧型系統-------------- 55
4-1 適性化測驗-------------------------------------------------------- 55
4-2 鑑定學習障礙----------------------------------------------------- 56
4-3 建立適性補救學習路徑演算法(ARIP) ---------------------- 59
4-4 智慧型概念診斷系統(ICDS)----------------------------------- 60
4-5 結語----------------------------------------------------------------- 61
第五章 實驗設計與資料分析--------------------------------------- 62
5-1 前測(Pre-test) ----------------------------------------------------- 62
5-2 分群(Cluster) ----------------------------------------------------- 63
5-3 匯入測驗歷程(Import) ------------------------------------------ 63
5-4 資料探勘(Data Mining) ----------------------------------------- 64
5-5 分組(Sub Cluster) ------------------------------------------------ 64
5-6 後測(Post-test) --------------------------------------------------- 64
5-7 檢定(Test) --------------------------------------------------------- 65
5-8 分析(Analyze) ---------------------------------------------------- 66
5-9 結語----------------------------------------------------------------- 66
第六章 結構方程模式之理論模式與假設--------------------- 67
6-1 問卷設計--------------------------------------------------------- 68
6-2 研究變數定義--------------------------------------------------- 71
6-3 資料收集與研究對象------------------------------------------ 73
6-4 建立理論架構與研究假說------------------------------------ 74
6-5 檢驗模式配適度指標------------------------------------------ 76
6-6 測量模式的驗證性因素分析--------------------------------- 80
6-7 結構模式評估--------------------------------------------------- 83
6-8 結語----------------------------------------------------------------- 87
第七章 結論與建議------------------------------------------------------ 88
7-1 結論----------------------------------------------------------------- 88
7-2 建議----------------------------------------------------------------- 93
參考文獻------------------------------------------------------------------------- 96
附錄-------------------------------------------------------------------------------- 109
作者簡介------------------------------------------------------------------------- 110
Publication List著作清單----------------------------------------------- 112
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