Title page for etd-0722108-155145


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URN etd-0722108-155145
Author Lu-shih Hsieh
Author's Email Address No Public.
Statistics This thesis had been viewed 5341 times. Download 1189 times.
Department Information Management
Year 2007
Semester 2
Degree Ph.D.
Type of Document
Language English
Title Discovering Discussion Activity Flows in an On-line Forum Using
Data Mining Techniques
Date of Defense 2008-06-13
Page Count 136
Keyword
  • Support Vector Machine (SVM)
  • Content Management System (CMS).
  • Text classification
  • Learning Management System (LMS)
  • Decision tree
  • Data mining
  • Text mining
  • Hidden Markov Model (HMM)
  • Abstract 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.
    Advisory Committee
  • San-Yih Hwang - chair
  • Sheng-Tun Li - co-chair
  • Chao-Min Chiu - co-chair
  • Chih-Ping Wei - co-chair
  • Fu-Ren Lin - advisor
  • Files
  • etd-0722108-155145.pdf
  • indicate accessible in a year
    Date of Submission 2008-07-22

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