隨著網際網路(Internet)時代來臨，愈來愈多學校課程使用課程管理系統(CMS, course management system)或學習管理系統(LMS, learning management system)來教學或輔助教學。為了幫助學生在網路上有效的學習，教師必須知道學生在線上論壇從事那些討論的活動，並且在必要的時候，提供學生所需協助。現今網路教學系統普遍化的結果，更增加老師們參與線上論壇的工作負擔；為減輕教師工作負荷，設計出可協助教師了解討論活動的自動化工具，成為一項重要的工作。本研究呼應這項需求，提出一個可以在課程管理系統或學習管理系統中，協助教師追蹤線上論壇討論活動流程的自動化工具，我們稱此工具為FAFT (Forum Activity Flow Tracer)。
本研究採用資料探勘(data mining)及本文探勘(text ining)技術來發展FAFT 系統。FAFT 系統依其功能可分為，討論活動分類子系統(AC, activity classification)及活動流程探勘子系統(AFD, activity flow discovery)。一般而言，論壇上的一篇文章可以把它歸類為聲明、提問、澄清、解釋（演繹）、詰問、辯護和其它，這六類活動中的一類。討論活動分類子系統採用資料(本文)探勘技術以自動化方式完成每一篇文章活動的分類工作。本文以高中地球科學課程的論壇資料為例，進行實證研究；研究結果顯示，討論活動分類子系統，能有效完成討論活動分類工作。而活動流程探勘子系統採用隱馬爾可夫模型(hidden Markov model)來發覺討論活動流程。由於隱馬爾可夫模型可以方便地以圖形化的方式呈現，故能幫助教師更容易了解學生討論活動。同時也可應用隱馬爾可夫模型為預測模型的特性，來分辨學生的討論活動流程是屬於認知性(cognitive presence)的活動流程，亦或是社交性(social presence)的活動流程。這樣的預測有益於教師採取相對應的措施，來引導學生學習活動。實證結果顯示活動流程探勘子系統，可以有效完成分辨學生活動流程的工作。
因此，我們認為本研究所提的 FAFT 系統，可以協助教師追蹤線上論壇的討論活動流程。

In the Internet era, more and more courses are taught through a course management system (CMS) or learning management system (LMS). In an asynchronous virtual learning environment, an instructor has the need to beware the progress of discussions in forums, and may intervene if ecessary in order to facilitate students’ learning. This research proposes a discussion forum activity flow tracking system, called FAFT (Forum Activity Flow Tracer), to utomatically monitor the discussion activity flow of threaded forum postings in CMS/LMS. As CMS/LMS is getting popular in facilitating learning activities, the proposedFAFT can be used to facilitate instructors to identify students’ interaction types in discussion forums.
FAFT adopts modern data/text mining techniques to discover the patterns of forum discussion activity flows, which can be used for instructors to facilitate the online learning activities. FAFT consists of two subsystems: activity classification (AC) and activity flow discovery (AFD). A posting can be perceived as a type of announcement, questioning, clarification, interpretation, conflict, or assertion. AC adopts a cascade model to classify various activitytypes of posts in a discussion thread. The empirical evaluation of the classified types from a repository of postings in earth science on-line courses in a senior high school shows that AC can effectively facilitate the coding rocess, and the cascade model can deal with the imbalanced distribution nature of discussion postings.
AFD adopts a hidden Markov model (HMM) to discover the activity flows. A discussion activity flow can be presented as a hidden Markov model (HMM) diagram that an instructor can adopt to predict which iscussion activity flow type of a discussion thread may be followed. The empirical results of the HMM from an online forum in earth science subject in a senior high school show that FAFT can effectively predict the type of a discussion activity flow. Thus, the proposed FAFT can be embedded in a course management system to automatically predict the activity flow type of a discussion thread, and in turn reduce the teachers’ loads on managing online discussion forums.

Abstract ..................................................................................................................... I
Keywords ................................................................................................................. II
中文摘要.................................................................................................................III
關鍵詞.....................................................................................................................IV
Table of Contents ..................................................................................................... V
List of Tables ...........................................................................................................IX
List of Figures .........................................................................................................XI
Chapter 1 Introduction ..............................................................................................1
1.1 Motivation ...................................................................................................1
1.2 The Proposed Approach Meeting the Need.................................................6
1.3 Organization of the Thesis ........................................................................ 11
Chapter 2 Background.............................................................................................13
2.1 Learning Activity Flow .............................................................................14
2.1.1 An activity flow example ...............................................................16
2.1.2 Learning in a computer media communication (CMC) environment
.................................................................................................................18
2.2 Learning Management System (LMS)......................................................21
2.3 Text Mining Process..................................................................................23
2.4 Classification in Text Mining ....................................................................28
2.4.1 Decision tree classifiers..................................................................29
2.4.2 Support vector machines (SVM) classifiers...................................30
2.4.3 Imbalanced data distribution issue .................................................31
2.5 Mining Forum Activity Flows...................................................................34
Chapter 3 The Architecture of Forum Activity Flow Tracer (FAFT)......................42
3.1 FAFT Architecture.....................................................................................43
3.2 Activity Classification (AC) subsystem ....................................................44
3.2.1 AC implementation ........................................................................47
3.3 Activity Flow Discovery (AFD) subsystem ..............................................49
3.3.1 AFD implementation ......................................................................51
Chapter 4 Evaluation Design...................................................................................52
4.1 Data Set and Activity Type Coding...........................................................53
4.2 Evaluation Criteria ....................................................................................59
4.2.1 Evaluation criteria for AC ..............................................................59
4.2.2 Evaluation criteria for AFD............................................................61
Chapter 5 Evaluation Results and Discussion.........................................................64
5.1 Evaluation Results of AC Subsystem........................................................64
5.1.1 Results of decision tree classifiers .................................................65
5.1.2 Results of SVM classifiers .............................................................75
5.1.3 Results of the cascade model classifier ..........................................78
5.1.4 Discussion of AC subsystem ..........................................................87
5.2 Evaluation Results of AFD Subsystem .....................................................92
5.2.1 Results of the discovery and prediction of activity flow types ......92
5.2.2 Discussion of the Results of AFD subsystem.................................97
Chapter 6 Conclusion and Research Limitations ..................................................102
6.1 Conclusion...............................................................................................102
6.2 Research Limitations...............................................................................105
References .............................................................................................................108
Appendix A. Examples of Forum Discussion Posts and Corresponding Activity
Types ..................................................................................................................... 113
Appendix B. Evaluation Results of AC................................................................. 116

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