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系統識別號 U0026-0812200915122043
論文名稱(中文) 應用群聚智慧技術於部落格學習模式中之數位教材重組與推薦
論文名稱(英文) Applying Swarm Intelligence Technology to e-Learning Material Reorganization and Recommendation in a Blog-based Learning Model
校院名稱 成功大學
系所名稱(中) 工程科學系碩博士班
系所名稱(英) Department of Engineering Science
學年度 97
學期 2
出版年 98
研究生(中文) 黃天麒
研究生(英文) Tien-Chi Huang
電子信箱 n9894106@mail.ncku.edu.tw
學號 N9894106
學位類別 博士
語文別 英文
論文頁數 95頁
口試委員 口試委員-朱正忠
指導教授-黃悅民
召集委員-張智凱
口試委員-黃武元
口試委員-朱治平
中文關鍵字 部落格  粒子群聚最佳化演算法  群聚智慧  數位學習  資訊擷取  拼圖式合作學習  社會互動  認知負載 
英文關鍵字 Cognitive load  Information Retrieval  Social Interaction  e-Learning  Artificial Intelligence  Particle Swarm Optimization  Blog  Jigsaw Cooperative Learning 
學科別分類
中文摘要 近年來,部落格輔助學習被廣泛的應用在高等教育環境中,不同於文獻中所記載之研究方向,本論文利用粒子群聚最佳化演算法(Particle Swarm Optimization)發展出一個自動化之部落格文章組成演算法(SBACPSO, Serial Blog Article Composition using Particle Swarm Optimization),用以提昇部落格輔助學習之效率。將曾經在學習平台上發表過的部落格文章視為一個知識庫,並根據這些文章共有的特徵值,包括:文章被評論的數量、文章被引用的次數、文章的難易度、文章與某個主題的相關程度,加以將知識庫中的部落格文章挑選出並重新組合成適合學生使用之輔助教材。此外,我們發現若只將輔助教材所組成的範圍侷限在過去所發表的文章中將可能會造成窄化學生的學習視野。因此,我們利用前述所發展之部落格文章組成演算法在網際網路上收集部落格空間內之文章,並進行文章篩選、組成並推薦給學習者做為輔助教材。首先,我們分析學習者目前所學習的文章型態是屬於何種主題範圍,在此我們利用資訊擷取技術(Information Retrieval)達成分析的目的。接著我們將所分析出的索引字根據其重要性訂定權重值,權重值最高的三個索引字將被挑選為關鍵字並使用Google的部落格文章搜尋引擎搜尋符合的文章,再利用不同的文章特徵值組成符合個人學習者使用的部落格文章並加以進行推薦。為防止當學習者的學習主題改變時推薦機制會持續推薦較舊的學習主題文章,我們使用遺忘曲線來模擬新舊主題轉換時的情況,並讓此推薦機制有能力得知使用者目前的學習主題。研究結果顯示,SBACPSO演算法在效率分析上可達到預期的收斂結果,並且在可接受的運算效能下找到適合此演算法之文章數量。再者,研究結果顯示使用者對於推薦機制的介面設計與推薦內容的正確性都給予正面的回應。
此外,本論文第五章探究在高等教育中如何實踐一個部落格學習模式,目的在於檢驗「在高等教育情境中使用部落格技術輔助合作學習」以及探討用SBACPSO方法所產生出的輔助教材之效果。本研究探討利用SBACPSO方法所產生的輔助教材對學生的協助,並聚焦於部落格本身特有的各種功能(例如RSS訂閱機制、關鍵字搜尋與編輯工具),同時探討這些功能對於學習歷程中的社會互動、訊息延遲與認知負載之影響;以準實驗研究設計加以檢驗。參與研究之受試者為115位大學二年級學生,隨機分派至實驗組與對照組。其中,對照組的學生所接受的教學是在教室中進行拼圖式合作學習活動。實驗組的學生則在拼圖式合作學習之中兩個合作學習步驟,採用線上部落格系統進行學習。本研究之重要發現如下:(1)部落格系統與拼圖式合作學習活動之結合,促使實驗組學生產生比對照組學生更理想的社會互動情況。(2) RSS訂閱機制與關鍵字搜尋功能對於合作學習效果顯著,其成效之良好更甚過去研究所顯示。且於本研究中更以實證研究支持此二種部落格功能分別有助於減輕認知負載與訊息傳送延遲的問題。(3) 重組過後的部落格文章集合對於學生的學習過程中的確在多個面向上都有讓學生感知到輔助效果。綜上而論,本研究肯認部落格技術於高等教育環境之教學與學習中,扮演了重要的角色。
英文摘要 In recent years blog-assisted learning has been used widely in higher education for improving writing and collaboratively sharing work online. Different from the studies in the literature, the present study utilized a PSO-based algorithm, serial blog article composition particle swarm optimization (SBACPSO) algorithm, to automatically and intelligently generate supplementary materials for improving the efficiency of blog-assisted learning. Contrary to the study which mentioned content-fixed supplementary materials, the proposed supplementary materials, which consist of blogs posted by learners and instructors, provide more interactive and cooperative characteristics for the learning process. With a few blog attributes such as comments, trackbacks, difficulty levels, and association degree related to a specific topic, the best combination of blog articles is produced as a supplementary material. In addition, this dissertation proposes an individualized blog article recommendation mechanism to provide quality blog articles that accord with users’ learning topics. First, an IR-based technique was applied to extract and score index terms. The top three index terms were then entered into Google’s blog search engine to find the raw recommended blog articles. To avoid the situation where frequent topic-changing leads to a deficiency of article data on a specific learning topic, a forgetting rate was employed to simulate the phenomenon of changing learning topics. Subsequently, a modified SBACPSO algorithm was employed to generate recommended materials to users. The experimental results indicate that SBACPSO algorithm can achieve the expected convergence. Furthermore, the system’s performance was evaluated to find the appropriate article population size. Finally, user satisfaction regarding both the system and recommended content were gauged to find the system’s limitations and possible improvements. This study is of importance in that it provides users with dynamic blog article recommendation, improved online information discovery skills and opportunities to socialize with other bloggers.
In Chapter 5, an implementation of blog-based learning model is presented. The goal is to examine the effects of using blog technologies to support cooperative learning and the effectiveness of blog-based supplementary materials in higher education. Specifically, the study focused on the effects (as perceived by students) that distinct features embedded in blogs (such as RSS feeds, keyword searches, and an editor tool) can have on social interactions, time lags in information flows, and cognitive loads. A quasi-experimental control-group research design was adopted. The participants were 115 undergraduates who were randomly assigned to one of two groups. Students in the comparison group engaged in Jigsaw learning activities in the classroom, while students in the experimental group used a blogging system during parts of the cooperative processes. Two important findings were obtained. (1) Blogs, combined with Jigsaw cooperative learning activities, promoted better social interactions than those found among participants in the comparison group. (2) RSS feeds and a keyword search function made important contributions to cooperative learning, more so than has been previously identified in the literature. In particular, these two components were found to alleviate cognitive overload and the consequences of time lags in the delivery of information. (3) Besides the regular course materials, reorganized supplementary materials indeed provide support in the learning process perceived by students. Consequently, this study provides new insights into the role that blog technology can play in higher education.
論文目次 摘要……………………………………………………………………………………… IV
Abstract VI
誌 謝 VIII
Chapter 1 Introduction 1
1.1 Particle swarm optimization for blog-based e-Learning supplementary material generation 1
1.2 A blog article recommendation generating mechanism using an SBACPSO algorithm 3
1.3 Implementation of blog-assisted learning model 4
Chapter 2 Literature Review 9
2.1 The rise of blog 9
2.2 Blog-based learning 11
2.3 Particle Swarm Optimization Algorithm 13
Chapter 3 Serial blog article composition and recommendation mechanism 16
3.1 Serial blog article composition 16
3.1.1 An overview of the proposed architecture 16
3.1.2 Model design for serial blog article composition 17
3.1.3 SBACPSO (Serial Blog Article Composition with PSO) Algorithm 20
3.2 Blog article recommendation mechanism 22
3.2.1 Keywords extraction and scoring 24
3.2.2 Rating extracted keywords by forgetting rate 26
3.2.3 Blog search query 29
3.2.4 Model design for blog article recommendation with SBACPSO algorithm 29
Chapter 4 Empirical Evaluations 36
4.1 Analytical experiments for the performance of SBACPSO algorithm 36
4.2 Analytical experiments for blog article recommendation 39
4.3 User satisfaction measuring for blog article recommendation 40
4.3.1 Factor analysis 40
4.3.2 Results and discussion 43
Chapter 5 An implementation of blog-assisted cooperative learning model 46
5.1 Methodology 46
5.1.1 Setting 46
5.1.2 Research design 46
5.1.3 Jigsaw learning activities 47
5.1.4 Asynchronous Jigsaw group meetings on the blogging system 50
5.1.5 Keyword search and tag functions to assist learning 50
5.1.6 Cooperative blog-based supplementary materials using the RSS mechanism 51
5.2 Measurements 52
5.3 Results 56
5.3.1 Attitudes towards Jigsaw learning activities 56
5.3.2 Perceptions of social interaction in the weblogging cooperative learning environment 57
5.3.3 Responses to the blog-based supplementary materials with RSS mechanism 59
5.3.4 Perceptions of the keyword search and tag functions 64
5.4 Discussion 65
Chapter 6 Conclusions 72
References 76
自述……………………………………………………………………………………… 82
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